diff --git a/gee_catalog.json b/gee_catalog.json index c67aadd..21b8911 100644 --- a/gee_catalog.json +++ b/gee_catalog.json @@ -726,7 +726,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S1_GRD')", "provider": "European Union/ESA/Copernicus", "state_date": "2014-10-03", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel", @@ -744,7 +744,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2')", "provider": "European Union/ESA/Copernicus", "state_date": "2015-06-27", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -56, 180, 83", "deprecated": true, "keywords": "copernicus, esa, eu, msi, radiance, sentinel", @@ -762,7 +762,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')", "provider": "European Union/ESA/Copernicus/SentinelHub", "state_date": "2015-06-27", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub", @@ -780,7 +780,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_HARMONIZED')", "provider": "European Union/ESA/Copernicus", "state_date": "2015-06-27", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "copernicus, esa, eu, msi, radiance, sentinel", @@ -798,7 +798,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_SR')", "provider": "European Union/ESA/Copernicus", "state_date": "2017-03-28", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -56, 180, 83", "deprecated": true, "keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr", @@ -816,7 +816,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')", "provider": "European Union/ESA/Copernicus", "state_date": "2017-03-28", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr", @@ -834,7 +834,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S3/OLCI')", "provider": "European Union/ESA/Copernicus", "state_date": "2016-10-18", - "end_date": "2024-12-10", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "copernicus, esa, eu, olci, radiance, sentinel, toa", @@ -852,7 +852,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -870,7 +870,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -888,7 +888,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-05", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi", @@ -906,7 +906,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-11-22", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi", @@ -924,7 +924,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-10-02", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi", @@ -942,7 +942,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi", @@ -960,7 +960,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -978,7 +978,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi", @@ -996,7 +996,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -1014,7 +1014,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -1032,7 +1032,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4')", "provider": "European Union/ESA/Copernicus", "state_date": "2019-02-08", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi", @@ -1050,7 +1050,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi", @@ -1068,7 +1068,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-06-28", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi", @@ -1086,7 +1086,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-12-05", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi", @@ -1104,7 +1104,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-06-28", - "end_date": "2024-12-02", + "end_date": "2024-12-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi", @@ -1122,7 +1122,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-09-08", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -1140,7 +1140,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-04-30", - "end_date": "2024-11-25", + "end_date": "2024-11-27", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -1158,7 +1158,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-12-05", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi", @@ -1626,7 +1626,7 @@ "snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR')", "provider": "Daily Aggregates: Google and Copernicus Climate Data Store", "state_date": "1950-01-02", - "end_date": "2024-12-04", + "end_date": "2024-12-05", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind", @@ -1644,7 +1644,7 @@ "snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY')", "provider": "Copernicus Climate Data Store", "state_date": "1950-01-01", - "end_date": "2024-12-05", + "end_date": "2024-12-06", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind", @@ -2400,7 +2400,7 @@ "snippet": "ee.ImageCollection('FIRMS')", "provider": "NASA / LANCE / EOSDIS", "state_date": "2000-11-01", - "end_date": "2024-12-10", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal", @@ -2688,7 +2688,7 @@ "snippet": "ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')", "provider": "Google Earth Engine", "state_date": "2015-06-27", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "google, cloud, sentinel2_derived", @@ -2706,7 +2706,7 @@ "snippet": "ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1')", "provider": "World Resources Institute", "state_date": "2015-06-27", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "global, google, landcover, landuse, nrt, sentinel2_derived", @@ -2994,7 +2994,7 @@ "snippet": "ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')", "provider": "University of California Merced", "state_date": "1979-01-01", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-124.9, 24.9, -66.8, 49.6", "deprecated": false, "keywords": "climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind", @@ -3750,7 +3750,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index", @@ -3804,7 +3804,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst", @@ -3858,7 +3858,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color", @@ -3912,7 +3912,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2018-01-22", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst", @@ -3930,7 +3930,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "2014-03-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -3966,7 +3966,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "2014-03-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -3984,7 +3984,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "1998-01-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -5514,7 +5514,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT')", "provider": "USGS", "state_date": "2013-03-18", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs", @@ -5532,7 +5532,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')", "provider": "USGS/Google", "state_date": "2013-03-18", - "end_date": "2024-12-10", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l8, landsat, lc8, toa, usgs", @@ -5622,7 +5622,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs", @@ -5640,7 +5640,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs", @@ -5658,7 +5658,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA')", "provider": "USGS/Google", "state_date": "2021-10-31", - "end_date": "2024-12-10", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, landsat, toa, usgs", @@ -5676,7 +5676,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2')", "provider": "USGS", "state_date": "2021-11-02", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs", @@ -5694,7 +5694,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_L2')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs", @@ -5712,7 +5712,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA')", "provider": "USGS/Google", "state_date": "2021-11-02", - "end_date": "2024-12-10", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, toa, usgs", @@ -7674,7 +7674,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD19A1_GRANULES')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-12-21", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs", @@ -7692,7 +7692,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD19A2_GRANULES')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs", @@ -7710,7 +7710,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD43A1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-11-28", + "end_date": "2024-12-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs", @@ -7728,7 +7728,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD43A2')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-11-28", + "end_date": "2024-12-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs", @@ -7746,7 +7746,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD43A3')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-11-28", + "end_date": "2024-12-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, black_sky, daily, global, modis, nasa, usgs, white_sky", @@ -7764,7 +7764,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD43A4')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-11-28", + "end_date": "2024-12-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, brdf, daily, global, modis, nasa, reflectance, usgs", @@ -7782,7 +7782,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD43C3')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-01", + "end_date": "2024-12-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, black_sky, brdf, daily, global, modis, nasa, usgs, white_sky", @@ -7854,7 +7854,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD09CMG')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "brightness_temperature, ozone, surface_reflectance, terra", @@ -7872,7 +7872,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD09GA')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs", @@ -7890,7 +7890,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD09GQ')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs", @@ -7926,7 +7926,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD10A1')", "provider": "NASA NSIDC DAAC at CIRES", "state_date": "2000-02-24", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra", @@ -7944,7 +7944,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD11A1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-05", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs", @@ -8124,7 +8124,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD16A2')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2001-01-01", - "end_date": "2024-11-16", + "end_date": "2024-11-24", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "8_day, evapotranspiration, global, mod16a2, modis, nasa", @@ -8214,7 +8214,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD21A1D')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs", @@ -8232,7 +8232,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD21A1N')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-08", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs", @@ -8250,7 +8250,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD21C1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs", @@ -8340,7 +8340,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD09CMG')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2002-07-04", - "end_date": "2024-12-08", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "brightness_temperature, ozone, surface_reflectance, aqua", @@ -8358,7 +8358,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD09GA')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2002-07-04", - "end_date": "2024-12-07", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs", @@ -8376,7 +8376,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD09GQ')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2002-07-04", - "end_date": "2024-12-07", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs", @@ -8412,7 +8412,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD10A1')", "provider": "NASA NSIDC DAAC at CIRES", "state_date": "2002-07-04", - "end_date": "2024-12-08", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow", @@ -8430,7 +8430,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD11A1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2002-07-04", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs", @@ -8646,7 +8646,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD21A1D')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs", @@ -8664,7 +8664,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD21A1N')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-07", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs", @@ -8682,7 +8682,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD21C1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs", @@ -9708,7 +9708,7 @@ "snippet": "ee.ImageCollection('NASA/EMIT/L1B/RAD')", "provider": "NASA Jet Propulsion Laboratory", "state_date": "2022-08-09", - "end_date": "2024-12-01", + "end_date": "2024-12-04", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emit, nasa, radiance", @@ -9726,7 +9726,7 @@ "snippet": "ee.ImageCollection('NASA/EMIT/L2A/RFL')", "provider": "NASA Jet Propulsion Laboratory", "state_date": "2022-08-09", - "end_date": "2024-12-01", + "end_date": "2024-12-04", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emit, nasa, reflectance", @@ -9816,7 +9816,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf')", "provider": "NASA / GMAO", "state_date": "2022-10-01", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9834,7 +9834,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr')", "provider": "NASA / GMAO", "state_date": "2022-10-01", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9852,7 +9852,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf')", "provider": "NASA / GMAO", "state_date": "2018-01-01", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9870,7 +9870,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr')", "provider": "NASA / GMAO", "state_date": "2018-01-01", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -10014,7 +10014,7 @@ "snippet": "ee.ImageCollection('NASA/GPM_L3/IMERG_V07')", "provider": "NASA GES DISC at NASA Goddard Space Flight Center", "state_date": "2000-06-01", - "end_date": "2024-12-10", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, geophysical, gpm, half_hourly, imerg, jaxa, nasa, precipitation, weather", @@ -10302,7 +10302,7 @@ "snippet": "ee.ImageCollection('NASA/HLS/HLSL30/v002')", "provider": "NASA LP DAAC", "state_date": "2013-04-11", - "end_date": "2024-12-05", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "landsat, nasa, sentinel, usgs", @@ -10338,7 +10338,7 @@ "snippet": "ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2')", "provider": "NASA / LANCE / NOAA20_VIIRS", "state_date": "2023-10-08", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs", @@ -10356,7 +10356,7 @@ "snippet": "ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2')", "provider": "NASA / LANCE / SNPP_VIIRS", "state_date": "2023-09-03", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs", @@ -10464,7 +10464,7 @@ "snippet": "ee.ImageCollection('NASA/NLDAS/FORA0125_H002')", "provider": "NASA GES DISC at NASA Goddard Space Flight Center", "state_date": "1979-01-01", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-125.15, 24.85, -66.85, 53.28", "deprecated": false, "keywords": "climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind", @@ -10626,7 +10626,7 @@ "snippet": "ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006')", "provider": "Google and NSIDC", "state_date": "2023-12-04", - "end_date": "2024-12-08", + "end_date": "2024-12-10", "bbox": "-180, -84, 180, 84", "deprecated": false, "keywords": "drought, nasa, smap, soil_moisture, surface, weather", @@ -10644,7 +10644,7 @@ "snippet": "ee.ImageCollection('NASA/SMAP/SPL4SMGP/007')", "provider": "Google and NSIDC", "state_date": "2015-03-31", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-180, -84, 180, 84", "deprecated": false, "keywords": "drought, nasa, smap, soil_moisture, surface, weather", @@ -10662,7 +10662,7 @@ "snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP09GA')", "provider": "NASA Land SIPS", "state_date": "2012-01-19", - "end_date": "2024-12-05", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga", @@ -10698,7 +10698,7 @@ "snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP13A1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2012-01-17", - "end_date": "2024-11-16", + "end_date": "2024-11-24", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "16_day, evi, nasa, ndvi, noaa, npp, vegetation, viirs, vnp13a1", @@ -10716,7 +10716,7 @@ "snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP14A1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2012-01-19", - "end_date": "2024-12-08", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "fire, land, nasa, noaa, surface, viirs", @@ -10752,7 +10752,7 @@ "snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP21A1D')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2012-01-19", - "end_date": "2024-12-05", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, day, land, nasa, noaa, surface, temperature, viirs", @@ -10770,7 +10770,7 @@ "snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP21A1N')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2012-01-19", - "end_date": "2024-12-05", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, land, nasa, night, noaa, surface, temperature, viirs", @@ -10842,7 +10842,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/sea_level_pressure')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis", @@ -10860,7 +10860,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/surface_temp')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature", @@ -10878,7 +10878,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/surface_wv')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor", @@ -11112,7 +11112,7 @@ "snippet": "ee.ImageCollection('NOAA/CDR/OISST/V2_1')", "provider": "NOAA", "state_date": "1981-09-01", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature", @@ -11184,7 +11184,7 @@ "snippet": "ee.ImageCollection('NOAA/CFSR')", "provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)", "state_date": "2018-12-13", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather", @@ -11202,7 +11202,7 @@ "snippet": "ee.ImageCollection('NOAA/CFSV2/FOR6H')", "provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)", "state_date": "1979-01-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather", @@ -11220,7 +11220,7 @@ "snippet": "ee.ImageCollection('NOAA/CPC/Precipitation')", "provider": "NOAA Physical Sciences Laboratory", "state_date": "2006-01-01", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, noaa, precipitation, weather", @@ -11238,7 +11238,7 @@ "snippet": "ee.ImageCollection('NOAA/CPC/Temperature')", "provider": "NOAA Physical Sciences Laboratory", "state_date": "1979-01-01", - "end_date": "2024-12-10", + "end_date": "2024-12-11", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, noaa, precipitation, weather", @@ -11292,7 +11292,7 @@ "snippet": "ee.ImageCollection('NOAA/GFS0P25')", "provider": "NOAA/NCEP/EMC", "state_date": "2015-07-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind", @@ -11310,7 +11310,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/FDCC')", "provider": "NOAA", "state_date": "2017-05-24", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-152.11, 14, -49.18, 56.77", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire", @@ -11328,7 +11328,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/FDCF')", "provider": "NOAA", "state_date": "2017-05-24", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire", @@ -11346,7 +11346,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPC')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-152.11, 14, -49.18, 56.77", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -11364,7 +11364,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPF')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -11382,7 +11382,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPM')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -11490,7 +11490,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/FDCC')", "provider": "NOAA", "state_date": "2022-10-13", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, 14.57, 180, 53.51", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire", @@ -11508,7 +11508,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/FDCF')", "provider": "NOAA", "state_date": "2022-10-13", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire", @@ -11526,7 +11526,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPC')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, 14.57, 180, 53.51", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11544,7 +11544,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPF')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11562,7 +11562,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPM')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11670,7 +11670,7 @@ "snippet": "ee.ImageCollection('NOAA/NWS/RTMA')", "provider": "NOAA/NWS", "state_date": "2011-01-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-130.17, 20.15, -60.81, 52.91", "deprecated": false, "keywords": "climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind", @@ -11904,7 +11904,7 @@ "snippet": "ee.ImageCollection('NOAA/VIIRS/001/VNP46A2')", "provider": "NASA LAADS DAAC", "state_date": "2012-01-19", - "end_date": "2024-11-28", + "end_date": "2024-12-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "brdf, daily, nasa, noaa, viirs", @@ -12048,7 +12048,7 @@ "snippet": "ee.ImageCollection('OREGONSTATE/PRISM/AN81d')", "provider": "PRISM / OREGONSTATE", "state_date": "1981-01-01", - "end_date": "2024-12-08", + "end_date": "2024-12-09", "bbox": "-125, 24, -66, 50", "deprecated": false, "keywords": "climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather", @@ -13182,7 +13182,7 @@ "snippet": "ee.ImageCollection('TOMS/MERGED')", "provider": "NASA / GES DISC", "state_date": "1978-11-01", - "end_date": "2024-12-09", + "end_date": "2024-12-10", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms", @@ -14784,7 +14784,7 @@ "snippet": "ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1')", "provider": "Institute of Industrial Science, The University of Tokyo, Japan", "state_date": "2007-01-01", - "end_date": "2024-12-09", + "end_date": "2024-12-11", "bbox": "60, -60, 180, 60", "deprecated": false, "keywords": "drought, kbdi, lst_derived, rainfall, utokyo, wtlab", @@ -16116,7 +16116,7 @@ "snippet": "ee.ImageCollection('projects/gcp-public-data-weathernext/assets/59572747_4_0')", "provider": "Google", "state_date": "2020-01-01", - "end_date": "2024-12-11", + "end_date": "2024-12-12", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "weather, weathernext, forecast, temperature, precipitation, wind", diff --git a/gee_catalog.tsv b/gee_catalog.tsv index 7b24a7a..c843c6a 100644 --- a/gee_catalog.tsv +++ b/gee_catalog.tsv @@ -39,31 +39,31 @@ COPERNICUS/CORINE/V20/100m Copernicus CORINE Land Cover image_collection ee.Imag COPERNICUS/DEM/GLO30 Copernicus DEM GLO-30: Global 30m Digital Elevation Model image_collection ee.ImageCollection('COPERNICUS/DEM/GLO30') Copernicus 2010-12-01 2015-01-31 -180, -90, 180, 90 False copernicus, dem, elevation, geophysical https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_DEM_GLO30.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_DEM_GLO30 proprietary COPERNICUS/Landcover/100m/Proba-V-C3/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 3 image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V-C3/Global') Copernicus 2015-01-01 2019-12-31 -180, -90, 180, 90 False copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V-C3_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global proprietary COPERNICUS/Landcover/100m/Proba-V/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 2 [deprecated] image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V/Global') Copernicus 2015-01-01 2015-01-01 -180, -90, 180, 90 True copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V_Global proprietary -COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2024-12-11 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary -COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2024-12-11 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary -COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2024-12-11 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary -COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2024-12-11 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary -COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2024-12-11 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary -COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2024-12-11 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary -COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2024-12-10 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary -COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2024-12-11 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary -COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2024-12-11 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary -COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2024-12-11 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary -COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2024-12-11 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary -COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2024-12-11 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary -COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2024-12-11 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary -COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2024-12-11 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary -COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2024-12-11 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary -COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2024-12-09 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary -COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2024-12-09 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary -COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2024-12-09 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary -COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2024-12-09 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary -COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2024-12-09 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary -COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2024-12-09 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary -COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-12-02 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary -COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2024-12-09 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary -COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-11-25 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary -COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2024-12-09 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary +COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2024-12-12 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary +COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2024-12-12 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary +COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2024-12-12 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary +COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2024-12-12 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary +COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2024-12-12 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary +COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2024-12-12 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary +COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2024-12-11 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary +COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2024-12-12 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary +COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2024-12-12 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary +COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2024-12-12 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary +COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2024-12-12 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary +COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2024-12-12 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary +COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2024-12-12 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary +COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2024-12-12 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary +COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2024-12-12 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary +COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2024-12-10 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary +COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2024-12-10 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary +COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2024-12-10 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary +COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2024-12-10 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary +COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2024-12-10 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary +COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2024-12-10 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary +COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-12-03 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary +COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2024-12-10 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary +COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-11-27 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary +COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2024-12-10 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary CPOM/CryoSat2/ANTARCTICA_DEM CryoSat-2 Antarctica 1km DEM image ee.Image('CPOM/CryoSat2/ANTARCTICA_DEM') CPOM 2010-07-01 2016-07-01 -180, -88, 180, -60 False antarctica, cpom, cryosat_2, dem, elevation, polar https://storage.googleapis.com/earthengine-stac/catalog/CPOM/CPOM_CryoSat2_ANTARCTICA_DEM.json https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM proprietary CSIC/SPEI/2_8 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.8 [deprecated] image_collection ee.ImageCollection('CSIC/SPEI/2_8') Spanish National Research Council (CSIC) 1901-01-01 2021-01-01 -180, -90, 180, 90 True climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_8.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_8 CC-BY-4.0 CSIC/SPEI/2_9 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.9 image_collection ee.ImageCollection('CSIC/SPEI/2_9') Spanish National Research Council (CSIC) 1901-01-01 2023-01-01 -180, -90, 180, 90 False climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_9.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_9 CC-BY-4.0 @@ -89,8 +89,8 @@ DOE/ORNL/LandScan_HD/Ukraine_202201 LandScan High Definition Data for Ukraine, J ECMWF/CAMS/NRT Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time image_collection ee.ImageCollection('ECMWF/CAMS/NRT') European Centre for Medium-Range Weather Forecasts (ECMWF) 2016-06-22 2024-12-03 -180, -90, 180, 90 False aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_CAMS_NRT.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT proprietary ECMWF/ERA5/DAILY ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/DAILY') ECMWF / Copernicus Climate Change Service 1979-01-02 2020-07-09 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_DAILY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY proprietary ECMWF/ERA5/MONTHLY ERA5 Monthly Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/MONTHLY') ECMWF / Copernicus Climate Change Service 1979-01-01 2020-06-01 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY proprietary -ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2024-12-04 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary -ECMWF/ERA5_LAND/HOURLY ERA5-Land Hourly - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY') Copernicus Climate Data Store 1950-01-01 2024-12-05 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_HOURLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY proprietary +ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2024-12-05 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary +ECMWF/ERA5_LAND/HOURLY ERA5-Land Hourly - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY') Copernicus Climate Data Store 1950-01-01 2024-12-06 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_HOURLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY proprietary ECMWF/ERA5_LAND/MONTHLY ERA5-Land Monthly Averaged - ECMWF Climate Reanalysis [deprecated] image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY') Copernicus Climate Data Store 1950-02-01 2023-04-01 -180, -90, 180, 90 True cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY proprietary ECMWF/ERA5_LAND/MONTHLY_AGGR ERA5-Land Monthly Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR') Monthly Aggregates: Google and Copernicus Climate Data Store 1950-02-01 2024-11-01 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR proprietary ECMWF/ERA5_LAND/MONTHLY_BY_HOUR ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_BY_HOUR') Climate Data Store 1950-01-01 2024-10-01 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR proprietary @@ -132,7 +132,7 @@ FAO/WAPOR/2/L1_NPP_D WAPOR Dekadal Net Primary Production 2.0 image_collection e FAO/WAPOR/2/L1_RET_D WAPOR Dekadal Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_D') FAO UN 2009-01-01 2023-03-11 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_D proprietary FAO/WAPOR/2/L1_RET_E WAPOR Daily Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_E') FAO UN 2009-01-01 2023-03-20 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_E.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_E proprietary FAO/WAPOR/2/L1_T_D WAPOR Dekadal Transpiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_T_D') FAO UN 2009-01-01 2023-03-01 -30.0044643, -40.0044644, 65.0044644, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_T_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_T_D proprietary -FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2024-12-10 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary +FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2024-12-11 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary FORMA/FORMA_500m FORMA Global Forest Watch Deforestation Alerts, 500m [deprecated] image ee.Image('FORMA/FORMA_500m') Global Forest Watch, World Resources Institute 2006-01-01 2015-06-10 -180, -90, 180, 90 True alerts, deforestation, forest, forma, geophysical, gfw, modis, nasa, wri https://storage.googleapis.com/earthengine-stac/catalog/FORMA/FORMA_FORMA_500m.json https://developers.google.com/earth-engine/datasets/catalog/FORMA_FORMA_500m proprietary Finland/MAVI/VV/50cm Finland NRG NLS orthophotos 50 cm by Mavi image_collection ee.ImageCollection('Finland/MAVI/VV/50cm') NLS orthophotos 2015-01-01 2018-01-01 18, 59, 29.2, 69.4 False falsecolor, finland, mavi, nrg, orthophoto https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_MAVI_VV_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_MAVI_VV_50cm CC-BY-4.0 Finland/SMK/V/50cm Finland RGB NLS orthophotos 50 cm by SMK image_collection ee.ImageCollection('Finland/SMK/V/50cm') NLS orthophotos 2015-01-01 2023-01-01 18, 59, 29.2, 69.4 False finland, orthophoto, rgb, smk https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_SMK_V_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_SMK_V_50cm proprietary @@ -148,8 +148,8 @@ GLIMS/20230607 GLIMS 2023: Global Land Ice Measurements From Space table ee.Feat GLIMS/current GLIMS Current: Global Land Ice Measurements From Space table ee.FeatureCollection('GLIMS/current') National Snow and Ice Data Center (NSDIC) 1750-01-01 2023-06-07 -180, -90, 180, 90 False glacier, glims, ice, landcover, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/GLIMS/GLIMS_current.json https://developers.google.com/earth-engine/datasets/catalog/GLIMS_current proprietary GLOBAL_FLOOD_DB/MODIS_EVENTS/V1 Global Flood Database v1 (2000-2018) image_collection ee.ImageCollection('GLOBAL_FLOOD_DB/MODIS_EVENTS/V1') Cloud to Street (C2S) / Dartmouth Flood Observatory (DFO) 2000-02-17 2018-12-10 -180, -90, 180, 90 False c2s, cloudtostreet, dartmouth, dfo, flood, gfd, inundation, surface, water https://storage.googleapis.com/earthengine-stac/catalog/GLOBAL_FLOOD_DB/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1.json https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1 CC-BY-NC-4.0 GOOGLE/AirView/California_Unified_2015_2019 Google Street View Air Quality: High Resolution Air Pollution Mapping in California table ee.FeatureCollection('GOOGLE/AirView/California_Unified_2015_2019') Google / Aclima 2015-05-28 2019-06-07 -180, -90, 180, 90 False air_quality, nitrogen_dioxide, pollution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_AirView_California_Unified_2015_2019.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_AirView_California_Unified_2015_2019 CC-BY-NC-4.0 -GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2024-12-11 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0 -GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2024-12-11 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0 +GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2024-12-12 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0 +GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2024-12-12 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0 GOOGLE/GLOBAL_CCDC/V1 Google Global Landsat-based CCDC Segments (1999-2019) image_collection ee.ImageCollection('GOOGLE/GLOBAL_CCDC/V1') Google 1999-01-01 2020-01-01 -180, -60, 180, 72 False change_detection, google, landcover, landsat_derived, landuse https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_GLOBAL_CCDC_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1 CC-BY-4.0 GOOGLE/Research/open-buildings-temporal/v1 Open Buildings Temporal V1 image_collection ee.ImageCollection('GOOGLE/Research/open-buildings-temporal/v1') Google Research - Open Buildings 2016-06-30 2023-06-30 -180, -90, 180, 90 False building_height, height, annual, built_up, open_buildings, africa, asia, south_asia, southeast_asia, high_resolution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings-temporal_v1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings-temporal_v1 CC-BY-4.0 GOOGLE/Research/open-buildings/v1/polygons Open Buildings V1 Polygons [deprecated] table ee.FeatureCollection('GOOGLE/Research/open-buildings/v1/polygons') Google Research - Open Buildings 2021-04-30 2021-04-30 -180, -90, 180, 90 True africa, building, built_up, open_buildings, structure https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings_v1_polygons.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v1_polygons CC-BY-4.0 @@ -165,7 +165,7 @@ HYCOM/GLBu0_08/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Ve HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation proprietary HYCOM/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity image_collection ee.ImageCollection('HYCOM/sea_temp_salinity') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity proprietary HYCOM/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity image_collection ee.ImageCollection('HYCOM/sea_water_velocity') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity proprietary -IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2024-12-07 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary +IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2024-12-10 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary IDAHO_EPSCOR/MACAv2_METDATA MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA') University of California Merced 1900-01-01 2100-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA CC0-1.0 IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY') University of California Merced 1900-01-01 2099-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY CC0-1.0 IDAHO_EPSCOR/PDSI PDSI: University of Idaho Palmer Drought Severity Index [deprecated] image_collection ee.ImageCollection('IDAHO_EPSCOR/PDSI') University of California Merced 1979-03-01 2020-06-20 -124.9, 24.9, -66.8, 49.6 True climate, conus, crop, drought, geophysical, merced, palmer, pdsi https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_PDSI proprietary @@ -207,20 +207,20 @@ JAXA/ALOS/PALSAR/YEARLY/SAR Global PALSAR-2/PALSAR Yearly Mosaic, version 1 imag JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH Global PALSAR-2/PALSAR Yearly Mosaic, version 2 image_collection ee.ImageCollection('JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH') JAXA EORC 2015-01-01 2023-01-01 -180, -90, 180, 90 False alos, alos2, eroc, jaxa, palsar, palsar2, sar https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH proprietary JAXA/GCOM-C/L3/LAND/LAI/V1 GCOM-C/SGLI L3 Leaf Area Index (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V1 proprietary JAXA/GCOM-C/L3/LAND/LAI/V2 GCOM-C/SGLI L3 Leaf Area Index (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V2 proprietary -JAXA/GCOM-C/L3/LAND/LAI/V3 GCOM-C/SGLI L3 Leaf Area Index (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-12-09 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3 proprietary +JAXA/GCOM-C/L3/LAND/LAI/V3 GCOM-C/SGLI L3 Leaf Area Index (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-12-10 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3 proprietary JAXA/GCOM-C/L3/LAND/LST/V1 GCOM-C/SGLI L3 Land Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V1 proprietary JAXA/GCOM-C/L3/LAND/LST/V2 GCOM-C/SGLI L3 Land Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V2 proprietary -JAXA/GCOM-C/L3/LAND/LST/V3 GCOM-C/SGLI L3 Land Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-12-09 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V3 proprietary +JAXA/GCOM-C/L3/LAND/LST/V3 GCOM-C/SGLI L3 Land Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-12-10 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V3 proprietary JAXA/GCOM-C/L3/OCEAN/CHLA/V1 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V1 proprietary JAXA/GCOM-C/L3/OCEAN/CHLA/V2 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V2 proprietary -JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-12-09 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3 proprietary +JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-12-10 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3 proprietary JAXA/GCOM-C/L3/OCEAN/SST/V1 GCOM-C/SGLI L3 Sea Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V1 proprietary JAXA/GCOM-C/L3/OCEAN/SST/V2 GCOM-C/SGLI L3 Sea Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V2 proprietary -JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2018-01-22 2024-12-09 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary -JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2024-12-11 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary +JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2018-01-22 2024-12-10 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary +JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2024-12-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary JAXA/GPM_L3/GSMaP/v6/reanalysis GSMaP Reanalysis: Global Satellite Mapping of Precipitation image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/reanalysis') JAXA Earth Observation Research Center 2000-03-01 2014-03-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_reanalysis.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis proprietary -JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2024-12-11 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary -JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2024-12-11 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary +JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2024-12-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary +JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2024-12-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary JCU/Murray/GIC/global_tidal_wetland_change/2019 Murray Global Tidal Wetland Change v1.0 (1999-2019) image ee.Image('JCU/Murray/GIC/global_tidal_wetland_change/2019') Murray/JCU 1999-01-01 2019-12-31 -180, -90, 180, 90 False coastal, ecosystem, intertidal, landsat_derived, mangrove, murray, saltmarsh, tidal_flat, tidal_marsh https://storage.googleapis.com/earthengine-stac/catalog/JCU/JCU_Murray_GIC_global_tidal_wetland_change_2019.json https://developers.google.com/earth-engine/datasets/catalog/JCU_Murray_GIC_global_tidal_wetland_change_2019 CC-BY-4.0 JRC/CEMS_GLOFAS/FloodHazard/v1 JRC Global River Flood Hazard Maps Version 1 image_collection ee.ImageCollection('JRC/CEMS_GLOFAS/FloodHazard/v1') Joint Research Centre 2024-03-16 2024-03-16 -180, -90, 180, 90 False flood, monitoring, wri https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_CEMS_GLOFAS_FloodHazard_v1 CC-BY-4.0 JRC/D5/EUCROPMAP/V1 EUCROPMAP image_collection ee.ImageCollection('JRC/D5/EUCROPMAP/V1') Joint Research Center (JRC) 2018-01-01 2022-01-01 -16.171875, 34.313433, 36.386719, 72.182526 False crop, eu, jrc, lucas, sentinel1_derived https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_D5_EUCROPMAP_V1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_D5_EUCROPMAP_V1 CC-BY-4.0 @@ -305,18 +305,18 @@ LANDSAT/GLS2005_L5 Landsat Global Land Survey 2005, Landsat 5 scenes image_colle LANDSAT/GLS2005_L7 Landsat Global Land Survey 2005, Landsat 7 scenes image_collection ee.ImageCollection('LANDSAT/GLS2005_L7') USGS 2003-07-29 2008-07-29 -180, -90, 180, 90 False etm, gls, l7, landsat, radiance, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_GLS2005_L7.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7 PDDL-1.0 LANDSAT/LC08/C02/T1 USGS Landsat 8 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1') USGS 2013-03-18 2024-12-08 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1 PDDL-1.0 LANDSAT/LC08/C02/T1_L2 USGS Landsat 8 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') USGS 2013-03-18 2024-12-08 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 proprietary -LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2024-12-11 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0 -LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2024-12-10 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0 +LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2024-12-12 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0 +LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2024-12-12 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0 LANDSAT/LC08/C02/T1_TOA USGS Landsat 8 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA') USGS/Google 2013-03-18 2024-12-08 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA PDDL-1.0 LANDSAT/LC08/C02/T2 USGS Landsat 8 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2') USGS 2021-10-28 2024-12-08 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2 PDDL-1.0 LANDSAT/LC08/C02/T2_L2 USGS Landsat 8 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_L2') USGS 2013-03-18 2024-12-08 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2 proprietary LANDSAT/LC08/C02/T2_TOA USGS Landsat 8 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_TOA') USGS/Google 2021-10-28 2024-12-08 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_TOA PDDL-1.0 -LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-12-11 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0 -LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2024-12-09 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary -LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2024-12-10 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0 -LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2024-12-11 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0 -LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2024-12-09 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary -LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2024-12-10 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0 +LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-12-12 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0 +LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2024-12-10 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary +LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2024-12-12 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0 +LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2024-12-12 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0 +LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2024-12-10 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary +LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2024-12-11 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0 LANDSAT/LE07/C02/T1 USGS Landsat 7 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1 PDDL-1.0 LANDSAT/LE07/C02/T1_L2 USGS Landsat 7 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_L2') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False cfmask, cloud, etm, fmask, global, landsat, lasrc, le07, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2 proprietary LANDSAT/LE07/C02/T1_RT USGS Landsat 7 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_RT') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, nrt, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_RT PDDL-1.0 @@ -425,22 +425,22 @@ MODIS/061/MCD12Q2 MCD12Q2.006 Land Cover Dynamics Yearly Global 500m image_colle MODIS/061/MCD15A3H MCD15A3H.061 MODIS Leaf Area Index/FPAR 4-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MCD15A3H') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-02 -180, -90, 180, 90 False 4_day, fpar, global, lai, mcd15a3h, modis, nasa, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD15A3H proprietary MODIS/061/MCD18A1 MCD18A1.061 Surface Radiation Daily/3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18A1 proprietary MODIS/061/MCD18C2 MCD18C2.061 Photosynthetically Active Radiation Daily 3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18C2') NASA LP DAAC at the USGS EROS Center 2002-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18C2 proprietary -MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2024-12-07 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary -MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary -MODIS/061/MCD43A1 MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-28 -180, -90, 180, 90 False albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1 proprietary -MODIS/061/MCD43A2 MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-28 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2 proprietary -MODIS/061/MCD43A3 MCD43A3.061 MODIS Albedo Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-28 -180, -90, 180, 90 False albedo, black_sky, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 proprietary -MODIS/061/MCD43A4 MCD43A4.061 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A4') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-28 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4 proprietary -MODIS/061/MCD43C3 MCD43C3.061 BRDF/Albedo Daily L3 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MCD43C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-01 -180, -90, 180, 90 False albedo, black_sky, brdf, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43C3 proprietary +MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2024-12-10 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary +MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary +MODIS/061/MCD43A1 MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-02 -180, -90, 180, 90 False albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1 proprietary +MODIS/061/MCD43A2 MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-02 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2 proprietary +MODIS/061/MCD43A3 MCD43A3.061 MODIS Albedo Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-02 -180, -90, 180, 90 False albedo, black_sky, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 proprietary +MODIS/061/MCD43A4 MCD43A4.061 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A4') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-02 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4 proprietary +MODIS/061/MCD43C3 MCD43C3.061 BRDF/Albedo Daily L3 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MCD43C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-03 -180, -90, 180, 90 False albedo, black_sky, brdf, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43C3 proprietary MODIS/061/MCD64A1 MCD64A1.061 MODIS Burned Area Monthly Global 500m image_collection ee.ImageCollection('MODIS/061/MCD64A1') NASA LP DAAC at the USGS EROS Center 2000-11-01 2024-09-01 -180, -90, 180, 90 False burn, change_detection, fire, geophysical, global, mcd64a1, modis, monthly, nasa, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD64A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD64A1 proprietary MODIS/061/MOD08_M3 MOD08_M3.061 Terra Atmosphere Monthly Global Product image_collection ee.ImageCollection('MODIS/061/MOD08_M3') NASA LAADS DAAC at NASA Goddard Space Flight Center 2000-02-01 2024-11-01 -180, -90, 180, 90 False atmosphere, geophysical, global, mod08, mod08_m3, modis, monthly, nasa, temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD08_M3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD08_M3 proprietary MODIS/061/MOD09A1 MOD09A1.061 Terra Surface Reflectance 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD09A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-24 -180, -90, 180, 90 False 8_day, global, mod09a1, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09A1 proprietary -MODIS/061/MOD09CMG MOD09CMG.061 Terra Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD09CMG') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09CMG proprietary -MODIS/061/MOD09GA MOD09GA.061 Terra Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MOD09GA') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GA proprietary -MODIS/061/MOD09GQ MOD09GQ.061 Terra Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09GQ') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ proprietary +MODIS/061/MOD09CMG MOD09CMG.061 Terra Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD09CMG') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09CMG proprietary +MODIS/061/MOD09GA MOD09GA.061 Terra Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MOD09GA') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-09 -180, -90, 180, 90 False daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GA proprietary +MODIS/061/MOD09GQ MOD09GQ.061 Terra Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09GQ') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-09 -180, -90, 180, 90 False daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ proprietary MODIS/061/MOD09Q1 MOD09Q1.061 Terra Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09Q1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-24 -180, -90, 180, 90 False 8_day, global, mod09q1, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1 proprietary -MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2024-12-08 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary -MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-05 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary +MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2024-12-09 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary +MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary MODIS/061/MOD11A2 MOD11A2.061 Terra Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-24 -180, -90, 180, 90 False 8_day, emissivity, global, lst, mod11a2, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2 proprietary MODIS/061/MOD13A1 MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD13A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-16 -180, -90, 180, 90 False 16_day, evi, global, mod13a1, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A1 proprietary MODIS/061/MOD13A2 MOD13A2.061 Terra Vegetation Indices 16-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD13A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-16 -180, -90, 180, 90 False 16_day, evi, global, mod13a2, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A2 proprietary @@ -450,24 +450,24 @@ MODIS/061/MOD13Q1 MOD13Q1.061 Terra Vegetation Indices 16-Day Global 250m image_ MODIS/061/MOD14A1 MOD14A1.061: Terra Thermal Anomalies & Fire Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD14A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-01 -180, -90, 180, 90 False daily, fire, global, mod14a1, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD14A1 proprietary MODIS/061/MOD14A2 MOD14A2.061: Terra Thermal Anomalies & Fire 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD14A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-24 -180, -90, 180, 90 False 8_day, fire, global, mod14a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD14A2 proprietary MODIS/061/MOD15A2H MOD15A2H.061: Terra Leaf Area Index/FPAR 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD15A2H') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-11-24 -180, -90, 180, 90 False 8_day, fpar, global, lai, mod15a2h, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD15A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD15A2H proprietary -MODIS/061/MOD16A2 MOD16A2.061: Terra Net Evapotranspiration 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD16A2') NASA LP DAAC at the USGS EROS Center 2001-01-01 2024-11-16 -180, -90, 180, 90 False 8_day, evapotranspiration, global, mod16a2, modis, nasa https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 proprietary +MODIS/061/MOD16A2 MOD16A2.061: Terra Net Evapotranspiration 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD16A2') NASA LP DAAC at the USGS EROS Center 2001-01-01 2024-11-24 -180, -90, 180, 90 False 8_day, evapotranspiration, global, mod16a2, modis, nasa https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 proprietary MODIS/061/MOD16A2GF MOD16A2GF.061: Terra Net Evapotranspiration Gap-Filled 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD16A2GF') NASA LP DAAC at the USGS EROS Center 2000-01-01 2023-12-27 -180, -90, 180, 90 False 8_day, evapotranspiration, global, modis, nasa https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2GF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2GF proprietary MODIS/061/MOD17A2H MOD17A2H.061: Terra Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A2H') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-11-24 -180, -90, 180, 90 False 8_day, global, gpp, mod17a2, modis, nasa, photosynthesis, productivity, psn, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2H proprietary MODIS/061/MOD17A2HGF MOD17A2HGF.061: Terra Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A2HGF') NASA LP DAAC at the USGS EROS Center 2021-01-01 2023-12-27 -180, -90, 180, 90 False 8_day, global, gpp, modis, nasa, photosynthesis, productivity, psn, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2HGF proprietary MODIS/061/MOD17A3HGF MOD17A3HGF.061: Terra Net Primary Production Gap-Filled Yearly Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A3HGF') NASA LP DAAC at the USGS EROS Center 2001-01-01 2023-01-01 -180, -90, 180, 90 False global, gpp, nasa, npp, photosynthesis, productivity, psn, terra, usgs, yearly https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A3HGF proprietary -MODIS/061/MOD21A1D MOD21A1D.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1D proprietary -MODIS/061/MOD21A1N MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-08 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N proprietary -MODIS/061/MOD21C1 MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-09 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1 proprietary +MODIS/061/MOD21A1D MOD21A1D.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1D proprietary +MODIS/061/MOD21A1N MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N proprietary +MODIS/061/MOD21C1 MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1 proprietary MODIS/061/MOD21C2 MOD21C2.061 Terra Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-24 -180, -90, 180, 90 False emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C2 proprietary MODIS/061/MOD21C3 MOD21C3.061 Terra Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-01 -180, -90, 180, 90 False emissivity, global, lst, monthly, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C3 proprietary MODIS/061/MYD08_M3 MYD08_M3.061 Aqua Atmosphere Monthly Global Product image_collection ee.ImageCollection('MODIS/061/MYD08_M3') NASA LAADS DAAC at NASA Goddard Space Flight Center 2002-07-01 2024-11-01 -180, -90, 180, 90 False aqua, atmosphere, geophysical, global, modis, monthly, myd08, myd08_m3, nasa, temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD08_M3 proprietary MODIS/061/MYD09A1 MYD09A1.061 Aqua Surface Reflectance 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD09A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-11-24 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09a1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09A1 proprietary -MODIS/061/MYD09CMG MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD09CMG') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-08 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, aqua https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09CMG proprietary -MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-07 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary -MODIS/061/MYD09GQ MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09GQ') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-07 -180, -90, 180, 90 False aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ proprietary +MODIS/061/MYD09CMG MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD09CMG') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-10 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, aqua https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09CMG proprietary +MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-09 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary +MODIS/061/MYD09GQ MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09GQ') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-09 -180, -90, 180, 90 False aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ proprietary MODIS/061/MYD09Q1 MYD09Q1.061 Aqua Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09Q1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-11-24 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09q1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09Q1 proprietary -MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2024-12-08 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary -MODIS/061/MYD11A1 MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-07 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 proprietary +MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2024-12-10 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary +MODIS/061/MYD11A1 MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-10 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 proprietary MODIS/061/MYD11A2 MYD11A2.061 Aqua Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A2') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-11-24 -180, -90, 180, 90 False 8_day, aqua, emissivity, global, lst, modis, myd11a2, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A2 proprietary MODIS/061/MYD13A1 MYD13A1.061 Aqua Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD13A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-11-08 -180, -90, 180, 90 False 16_day, aqua, evi, global, modis, myd13a1, nasa, ndvi, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A1 proprietary MODIS/061/MYD13A2 MYD13A2.061 Aqua Vegetation Indices 16-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MYD13A2') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-11-08 -180, -90, 180, 90 False 16_day, aqua, evi, global, modis, myd13a2, nasa, ndvi, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2 proprietary @@ -479,9 +479,9 @@ MODIS/061/MYD14A2 MYD14A2.061: Aqua Thermal Anomalies & Fire 8-Day Global 1km im MODIS/061/MYD15A2H MYD15A2H.061: Aqua Leaf Area Index/FPAR 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD15A2H') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-11-24 -180, -90, 180, 90 False 8_day, aqua, fpar, global, lai, modis, myd15a2h, nasa, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD15A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD15A2H proprietary MODIS/061/MYD17A2H MYD17A2H.061: Aqua Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD17A2H') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-11-24 -180, -90, 180, 90 False 8_day, aqua, global, gpp, modis, myd17a2, nasa, photosynthesis, productivity, psn, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A2H proprietary MODIS/061/MYD17A3HGF MYD17A3HGF.061: Aqua Net Primary Production Gap-Filled Yearly Global 500m image_collection ee.ImageCollection('MODIS/061/MYD17A3HGF') NASA LP DAAC at the USGS EROS Center 2001-01-01 2023-01-01 -180, -90, 180, 90 False aqua, global, gpp, nasa, npp, photosynthesis, productivity, psn, usgs, yearly https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A3HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3HGF proprietary -MODIS/061/MYD21A1D MYD21A1D.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1D proprietary -MODIS/061/MYD21A1N MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-07 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N proprietary -MODIS/061/MYD21C1 MYD21C1.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-09 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C1 proprietary +MODIS/061/MYD21A1D MYD21A1D.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1D proprietary +MODIS/061/MYD21A1N MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N proprietary +MODIS/061/MYD21C1 MYD21C1.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-10 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C1 proprietary MODIS/061/MYD21C2 MYD21C2.061 Aqua Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-24 -180, -90, 180, 90 False aqua, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C2 proprietary MODIS/061/MYD21C3 MYD21C3.061 Aqua Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-11-01 -180, -90, 180, 90 False aqua, emissivity, global, lst, monthly, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C3 proprietary MODIS/MCD43A1 MCD43A1.005 BRDF-Albedo Model Parameters 16-Day L3 Global 500m [deprecated] image_collection ee.ImageCollection('MODIS/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2017-03-14 -180, -90, 180, 90 True 16_day, albedo, brdf, global, mcd43a1, modis, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A1 proprietary @@ -538,16 +538,16 @@ MODIS/MYD13A1 MYD13A1.005 Vegetation Indices 16-Day L3 Global 500m [deprecated] MODIS/MYD13Q1 MYD13Q1.005 Vegetation Indices 16-Day Global 250m [deprecated] image_collection ee.ImageCollection('MODIS/MYD13Q1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2017-03-14 -180, -90, 180, 90 True 16_day, aqua, evi, global, modis, myd13q1, ndvi, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD13Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD13Q1 proprietary MODIS/NTSG/MOD16A2/105 MOD16A2: MODIS Global Terrestrial Evapotranspiration 8-Day Global 1km image_collection ee.ImageCollection('MODIS/NTSG/MOD16A2/105') Numerical Terradynamic Simulation Group, The University of Montana 2000-01-01 2014-12-27 -180, -90, 180, 90 False 8_day, evapotranspiration, global, mod16a2, modis https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_NTSG_MOD16A2_105.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_NTSG_MOD16A2_105 proprietary NASA/ASTER_GED/AG100_003 AG100: ASTER Global Emissivity Dataset 100-meter V003 image ee.Image('NASA/ASTER_GED/AG100_003') NASA LP DAAC at the USGS EROS Center 2000-01-01 2008-12-31 -180, -59, 180, 80 False aster, caltech, elevation, emissivity, ged, geophysical, infrared, jpl, lst, nasa, ndvi, temperature, thermal https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ASTER_GED_AG100_003.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ASTER_GED_AG100_003 proprietary -NASA/EMIT/L1B/RAD EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m image_collection ee.ImageCollection('NASA/EMIT/L1B/RAD') NASA Jet Propulsion Laboratory 2022-08-09 2024-12-01 -180, -90, 180, 90 False daily, emit, nasa, radiance https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L1B_RAD.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L1B_RAD proprietary -NASA/EMIT/L2A/RFL EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks 60 m image_collection ee.ImageCollection('NASA/EMIT/L2A/RFL') NASA Jet Propulsion Laboratory 2022-08-09 2024-12-01 -180, -90, 180, 90 False daily, emit, nasa, reflectance https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2A_RFL.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2A_RFL proprietary +NASA/EMIT/L1B/RAD EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m image_collection ee.ImageCollection('NASA/EMIT/L1B/RAD') NASA Jet Propulsion Laboratory 2022-08-09 2024-12-04 -180, -90, 180, 90 False daily, emit, nasa, radiance https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L1B_RAD.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L1B_RAD proprietary +NASA/EMIT/L2A/RFL EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks 60 m image_collection ee.ImageCollection('NASA/EMIT/L2A/RFL') NASA Jet Propulsion Laboratory 2022-08-09 2024-12-04 -180, -90, 180, 90 False daily, emit, nasa, reflectance https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2A_RFL.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2A_RFL proprietary NASA/EMIT/L2B/CH4ENH Earth Surface Mineral Dust Source Investigation- Methane Enhancement image_collection ee.ImageCollection('NASA/EMIT/L2B/CH4ENH') NASA Jet Propulsion Laboratory 2022-08-10 2024-11-30 -180, -90, 180, 90 False daily, emit, nasa, methane https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2B_CH4ENH.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4ENH proprietary NASA/EMIT/L2B/CH4PLM Earth Surface Mineral Dust Source Investigation- Methane Plume Complexes image_collection ee.ImageCollection('NASA/EMIT/L2B/CH4PLM') NASA Jet Propulsion Laboratory 2022-08-10 2024-10-26 -180, -90, 180, 90 False daily, emit, nasa, methane https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2B_CH4PLM.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4PLM proprietary NASA/FLDAS/NOAH01/C/GL/M/V001 FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System image_collection ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001') NASA GES DISC at NASA Goddard Space Flight Center 1982-01-01 2024-10-01 -180, -60, 180, 90 False climate, evapotranspiration, famine, fldas, humidity, ldas, monthly, nasa, runoff, snow, soil_moisture, soil_temperature, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 proprietary NASA/GDDP-CMIP6 NEX-GDDP-CMIP6: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/GDDP-CMIP6') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, gddp, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GDDP-CMIP6.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GDDP-CMIP6 various -NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2024-12-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary -NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2024-12-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary -NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2024-12-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary -NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2024-12-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary +NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2024-12-11 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary +NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2024-12-11 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary +NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2024-12-11 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary +NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2024-12-11 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary NASA/GIMMS/3GV0 GIMMS NDVI From AVHRR Sensors (3rd Generation) image_collection ee.ImageCollection('NASA/GIMMS/3GV0') NASA/NOAA 1981-07-01 2013-12-16 -180, -90, 180, 90 False avhrr, gimms, nasa, ndvi, noaa, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GIMMS_3GV0.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0 proprietary NASA/GLDAS/V021/NOAH/G025/T3H GLDAS-2.1: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H') NASA GES DISC at NASA Goddard Space Flight Center 2000-01-01 2024-11-13 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H proprietary NASA/GLDAS/V022/CLSM/G025/DA1D GLDAS-2.2: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V022/CLSM/G025/DA1D') NASA GES DISC at NASA Goddard Earth Sciences Data and Information Services Center 2003-01-01 2024-06-30 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V022_CLSM_G025_DA1D proprietary @@ -555,7 +555,7 @@ NASA/GLDAS/V20/NOAH/G025/T3H Reprocessed GLDAS-2.0: Global Land Data Assimilatio NASA/GPM_L3/IMERG_MONTHLY_V06 GPM: Monthly Global Precipitation Measurement (GPM) v6 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V06') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2021-09-01 -180, -90, 180, 90 False climate, geophysical, gpm, imerg, jaxa, monthly, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06 proprietary NASA/GPM_L3/IMERG_MONTHLY_V07 GPM: Monthly Global Precipitation Measurement (GPM) vRelease 07 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V07') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-06-01 -180, -90, 180, 90 False climate, geophysical, gpm, imerg, jaxa, monthly, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_MONTHLY_V07.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V07 proprietary NASA/GPM_L3/IMERG_V06 GPM: Global Precipitation Measurement (GPM) Release 06 [deprecated] image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_V06') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-06-02 -180, -90, 180, 90 True climate, geophysical, gpm, half_hourly, imerg, jaxa, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V06.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V06 proprietary -NASA/GPM_L3/IMERG_V07 GPM: Global Precipitation Measurement (GPM) Release 07 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_V07') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-12-10 -180, -90, 180, 90 False climate, geophysical, gpm, half_hourly, imerg, jaxa, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V07.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V07 proprietary +NASA/GPM_L3/IMERG_V07 GPM: Global Precipitation Measurement (GPM) Release 07 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_V07') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-12-12 -180, -90, 180, 90 False climate, geophysical, gpm, half_hourly, imerg, jaxa, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V07.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V07 proprietary NASA/GRACE/MASS_GRIDS/LAND GRACE Monthly Mass Grids - Land [deprecated] image_collection ee.ImageCollection('NASA/GRACE/MASS_GRIDS/LAND') NASA Jet Propulsion Laboratory 2002-04-01 2017-01-07 -180, -90, 180, 90 True crs, gfz, grace, gravity, jpl, land, mass, nasa, tellus, water https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_LAND.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_LAND proprietary NASA/GRACE/MASS_GRIDS/MASCON GRACE Monthly Mass Grids - Global Mascons [deprecated] image_collection ee.ImageCollection('NASA/GRACE/MASS_GRIDS/MASCON') NASA Jet Propulsion Laboratory 2002-03-31 2017-05-22 -180, -90, 180, 90 True grace, gravity, jpl, mascon, mass, nasa, tellus, water https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_MASCON.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON proprietary NASA/GRACE/MASS_GRIDS/MASCON_CRI GRACE Monthly Mass Grids - Global Mascon (CRI Filtered) [deprecated] image_collection ee.ImageCollection('NASA/GRACE/MASS_GRIDS/MASCON_CRI') NASA Jet Propulsion Laboratory 2002-03-31 2017-05-22 -180, -90, 180, 90 True grace, gravity, jpl, mascon, mass, nasa, tellus, water https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_MASCON_CRI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON_CRI proprietary @@ -571,16 +571,16 @@ NASA/GSFC/MERRA/flx/2 MERRA-2 M2T1NXFLX: Surface Flux Diagnostics V5.12.4 image_ NASA/GSFC/MERRA/lnd/2 MERRA-2 M2T1NXLND: Land Surface Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/lnd/2') NASA/MERRA 1980-01-01 2024-11-01 -180, -90, 180, 90 False evaporation, ice, merra, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_lnd_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_lnd_2 proprietary NASA/GSFC/MERRA/rad/2 MERRA-2 M2T1NXRAD: Radiation Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/rad/2') NASA/MERRA 1980-01-01 2024-11-01 -180, -90, 180, 90 False albedo, emissivity, merra, shortwave, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_rad_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_rad_2 proprietary NASA/GSFC/MERRA/slv/2 MERRA-2 M2T1NXSLV: Single-Level Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/slv/2') NASA/MERRA 1980-01-01 2024-11-01 -180, -90, 180, 90 False condensation, humidity, merra, nasa, omega, pressure, slv, temperature, vapor, water, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_slv_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_slv_2 proprietary -NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2024-12-05 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary +NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2024-12-10 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary NASA/JPL/global_forest_canopy_height_2005 Global Forest Canopy Height, 2005 image ee.Image('NASA/JPL/global_forest_canopy_height_2005') NASA/JPL 2005-05-20 2005-06-23 -180, -90, 180, 90 False canopy, forest, geophysical, glas, jpl, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_JPL_global_forest_canopy_height_2005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005 proprietary -NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2024-12-09 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary -NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2024-12-09 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary +NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2024-12-11 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary +NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2024-12-11 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary NASA/MEASURES/GFCC/TC/v3 Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m image_collection ee.ImageCollection('NASA/MEASURES/GFCC/TC/v3') NASA LP DAAC at the USGS EROS Center 2000-01-01 2015-01-01 -180, -90, 180, 90 False forest, glcf, landsat_derived, nasa, umd https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3 proprietary NASA/NASADEM_HGT/001 NASADEM: NASA NASADEM Digital Elevation 30m image ee.Image('NASA/NASADEM_HGT/001') NASA / USGS / JPL-Caltech 2000-02-11 2000-02-22 -180, -56, 180, 60 False dem, elevation, geophysical, nasa, nasadem, srtm, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NASADEM_HGT_001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001 proprietary NASA/NEX-DCP30 NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 53.74 False cag, climate, cmip5, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30 proprietary NASA/NEX-DCP30_ENSEMBLE_STATS NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30_ENSEMBLE_STATS') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 49.93 False cag, climate, cmip5, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30_ENSEMBLE_STATS.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30_ENSEMBLE_STATS proprietary NASA/NEX-GDDP NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-GDDP') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, cmip5, gddp, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-GDDP.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP proprietary -NASA/NLDAS/FORA0125_H002 NLDAS-2: North American Land Data Assimilation System Forcing Fields image_collection ee.ImageCollection('NASA/NLDAS/FORA0125_H002') NASA GES DISC at NASA Goddard Space Flight Center 1979-01-01 2024-12-08 -125.15, 24.85, -66.85, 53.28 False climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NLDAS_FORA0125_H002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002 proprietary +NASA/NLDAS/FORA0125_H002 NLDAS-2: North American Land Data Assimilation System Forcing Fields image_collection ee.ImageCollection('NASA/NLDAS/FORA0125_H002') NASA GES DISC at NASA Goddard Space Flight Center 1979-01-01 2024-12-09 -125.15, 24.85, -66.85, 53.28 False climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NLDAS_FORA0125_H002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002 proprietary NASA/OCEANDATA/MODIS-Aqua/L3SMI Ocean Color SMI: Standard Mapped Image MODIS Aqua Data image_collection ee.ImageCollection('NASA/OCEANDATA/MODIS-Aqua/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 2002-07-03 2022-02-28 -180, -90, 180, 90 False biology, chlorophyll, climate, modis, nasa, ocean, oceandata, reflectance, sst, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_MODIS-Aqua_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI proprietary NASA/OCEANDATA/MODIS-Terra/L3SMI Ocean Color SMI: Standard Mapped Image MODIS Terra Data image_collection ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 2000-02-24 2022-02-28 -180, -90, 180, 90 False biology, chlorophyll, climate, modis, nasa, ocean, oceandata, reflectance, sst, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_MODIS-Terra_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Terra_L3SMI proprietary NASA/OCEANDATA/SeaWiFS/L3SMI Ocean Color SMI: Standard Mapped Image SeaWiFS Data image_collection ee.ImageCollection('NASA/OCEANDATA/SeaWiFS/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 1997-09-04 2010-12-10 -180, -90, 180, 90 False biology, chlorophyll, climate, nasa, ocean, oceandata, reflectance, seawifs, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_SeaWiFS_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_SeaWiFS_L3SMI proprietary @@ -589,21 +589,21 @@ NASA/ORNL/DAYMET_V4 Daymet V4: Daily Surface Weather and Climatological Summarie NASA/ORNL/biomass_carbon_density/v1 Global Aboveground and Belowground Biomass Carbon Density Maps image_collection ee.ImageCollection('NASA/ORNL/biomass_carbon_density/v1') NASA ORNL DAAC at Oak Ridge National Laboratory 2010-01-01 2010-12-31 -180, -61.1, 180, 84 False aboveground, belowground, biomass, carbon, density, forest, nasa, ornl, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_biomass_carbon_density_v1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_biomass_carbon_density_v1 proprietary NASA/ORNL/global_forest_classification_2020/V1 Global 2020 Forest Classification for IPCC Aboveground Biomass Tier 1 Estimates, V1 image_collection ee.ImageCollection('NASA/ORNL/global_forest_classification_2020/V1') NASA ORNL DAAC at Oak Ridge National Laboratory 2020-01-01 2020-12-31 -180, -90, 180, 90 False aboveground, biomass, carbon, classification, forest, ipcc, nasa, primary_forest, secondary_forest https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_global_forest_classification_2020_V1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_global_forest_classification_2020_V1 proprietary NASA/SMAP/SPL3SMP_E/005 SPL3SMP_E.005 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') Google and NSIDC 2015-03-31 2023-12-03 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_005 proprietary -NASA/SMAP/SPL3SMP_E/006 SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006') Google and NSIDC 2023-12-04 2024-12-08 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_006.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006 proprietary -NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2024-12-08 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary -NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2024-12-05 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary +NASA/SMAP/SPL3SMP_E/006 SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006') Google and NSIDC 2023-12-04 2024-12-10 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_006.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006 proprietary +NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2024-12-09 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary +NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2024-12-10 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary NASA/VIIRS/002/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-11-24 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09H1 proprietary -NASA/VIIRS/002/VNP13A1 VNP13A1.002: VIIRS Vegetation Indices 16-Day 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP13A1') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-11-16 -180, -90, 180, 90 False 16_day, evi, nasa, ndvi, noaa, npp, vegetation, viirs, vnp13a1 https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP13A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP13A1 proprietary -NASA/VIIRS/002/VNP14A1 VNP14A1.002: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP14A1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-08 -180, -90, 180, 90 False fire, land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP14A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP14A1 proprietary +NASA/VIIRS/002/VNP13A1 VNP13A1.002: VIIRS Vegetation Indices 16-Day 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP13A1') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-11-24 -180, -90, 180, 90 False 16_day, evi, nasa, ndvi, noaa, npp, vegetation, viirs, vnp13a1 https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP13A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP13A1 proprietary +NASA/VIIRS/002/VNP14A1 VNP14A1.002: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP14A1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-10 -180, -90, 180, 90 False fire, land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP14A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP14A1 proprietary NASA/VIIRS/002/VNP15A2H VNP15A2H: LAI/FPAR 8-Day L4 Global 500m SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP15A2H') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-11-24 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP15A2H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP15A2H proprietary -NASA/VIIRS/002/VNP21A1D VNP21A1D.002: Day Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1D') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-05 -180, -90, 180, 90 False daily, day, land, nasa, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1D proprietary -NASA/VIIRS/002/VNP21A1N VNP21A1N.002: Night Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1N') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-05 -180, -90, 180, 90 False daily, land, nasa, night, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1N.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1N proprietary +NASA/VIIRS/002/VNP21A1D VNP21A1D.002: Day Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1D') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-10 -180, -90, 180, 90 False daily, day, land, nasa, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1D proprietary +NASA/VIIRS/002/VNP21A1N VNP21A1N.002: Night Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1N') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-10 -180, -90, 180, 90 False daily, land, nasa, night, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1N.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1N proprietary NASA_USDA/HSL/SMAP10KM_soil_moisture NASA-USDA Enhanced SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_soil_moisture') NASA GSFC 2015-04-02 2022-08-02 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture proprietary NASA_USDA/HSL/SMAP_soil_moisture NASA-USDA SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP_soil_moisture') NASA GSFC 2015-04-02 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture proprietary NASA_USDA/HSL/soil_moisture NASA-USDA Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/soil_moisture') NASA GSFC 2010-01-13 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smos, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture proprietary -NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2024-12-08 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary -NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2024-12-08 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary -NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2024-12-08 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary +NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2024-12-09 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary +NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2024-12-09 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary +NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2024-12-09 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary NOAA/CDR/ATMOS_NEAR_SURFACE/V2 NOAA CDR: Ocean Near-Surface Atmospheric Properties, Version 2 image_collection ee.ImageCollection('NOAA/CDR/ATMOS_NEAR_SURFACE/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False air_temperature, atmospheric, cdr, hourly, humidity, noaa, ocean, osb, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_ATMOS_NEAR_SURFACE_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2 proprietary NOAA/CDR/AVHRR/AOT/V3 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v03 [deprecated] image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V3') NOAA 1981-01-01 2022-03-31 -180, -90, 180, 90 True aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V3.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3 proprietary NOAA/CDR/AVHRR/AOT/V4 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v04 image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V4') NOAA 1981-01-01 2024-09-30 -180, -90, 180, 90 False aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V4 proprietary @@ -616,38 +616,38 @@ NOAA/CDR/AVHRR/SR/V5 NOAA CDR AVHRR: Surface Reflectance, Version 5 image_collec NOAA/CDR/GRIDSAT-B1/V2 NOAA CDR GRIDSAT-B1: Geostationary IR Channel Brightness Temperature image_collection ee.ImageCollection('NOAA/CDR/GRIDSAT-B1/V2') NOAA 1980-01-01 2024-03-31 -180, -90, 180, 90 False brightness, cdr, fundamental, geostationary, infrared, isccp, noaa, reflectance, sr https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_GRIDSAT-B1_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2 proprietary NOAA/CDR/HEAT_FLUXES/V2 NOAA CDR: Ocean Heat Fluxes, Version 2 image_collection ee.ImageCollection('NOAA/CDR/HEAT_FLUXES/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, flux, heat, hourly, noaa, ocean, osb https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_HEAT_FLUXES_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2 proprietary NOAA/CDR/OISST/V2 NOAA CDR OISST v2: Optimum Interpolation Sea Surface Temperature [deprecated] image_collection ee.ImageCollection('NOAA/CDR/OISST/V2') NOAA 1981-09-01 2020-04-26 -180, -90, 180, 90 True avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2 proprietary -NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2024-12-09 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary +NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2024-12-10 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary NOAA/CDR/PATMOSX/V53 NOAA CDR PATMOSX: Cloud Properties, Reflectance, and Brightness Temperatures, Version 5.3 image_collection ee.ImageCollection('NOAA/CDR/PATMOSX/V53') NOAA 1979-01-01 2022-01-01 -180, -90, 180, 90 False atmospheric, avhrr, brightness, cdr, cloud, metop, noaa, optical, poes, reflectance, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_PATMOSX_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53 proprietary NOAA/CDR/SST_PATHFINDER/V53 NOAA AVHRR Pathfinder Version 5.3 Collated Global 4km Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/SST_PATHFINDER/V53') NOAA 1981-08-24 2023-12-30 -180, -90, 180, 90 False avhrr, noaa, pathfinder, sea_ice, sst, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_PATHFINDER_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53 proprietary NOAA/CDR/SST_WHOI/V2 NOAA CDR WHOI: Sea Surface Temperature, Version 2 image_collection ee.ImageCollection('NOAA/CDR/SST_WHOI/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, hourly, noaa, ocean, oisst, osb, sst, whoi https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_WHOI_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2 proprietary -NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-12-11 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary -NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2024-12-11 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary -NOAA/CPC/Precipitation CPC Global Unified Gauge-Based Analysis of Daily Precipitation image_collection ee.ImageCollection('NOAA/CPC/Precipitation') NOAA Physical Sciences Laboratory 2006-01-01 2024-12-09 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Precipitation.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Precipitation proprietary -NOAA/CPC/Temperature CPC Global Unified Temperature image_collection ee.ImageCollection('NOAA/CPC/Temperature') NOAA Physical Sciences Laboratory 1979-01-01 2024-12-10 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Temperature.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Temperature proprietary +NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-12-12 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary +NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2024-12-12 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary +NOAA/CPC/Precipitation CPC Global Unified Gauge-Based Analysis of Daily Precipitation image_collection ee.ImageCollection('NOAA/CPC/Precipitation') NOAA Physical Sciences Laboratory 2006-01-01 2024-12-10 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Precipitation.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Precipitation proprietary +NOAA/CPC/Temperature CPC Global Unified Temperature image_collection ee.ImageCollection('NOAA/CPC/Temperature') NOAA Physical Sciences Laboratory 1979-01-01 2024-12-11 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Temperature.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Temperature proprietary NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4 DMSP OLS: Global Radiance-Calibrated Nighttime Lights Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1996-03-16 2011-07-31 -180, -65, 180, 75 False calibrated, dmsp, eog, imagery, lights, nighttime, ols, radiance, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4 proprietary NOAA/DMSP-OLS/NIGHTTIME_LIGHTS DMSP OLS: Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1992-01-01 2014-01-01 -180, -65, 180, 75 False dmsp, eog, imagery, lights, nighttime, ols, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS proprietary -NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2024-12-11 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary -NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2024-12-11 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary -NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2024-12-11 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary -NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2024-12-11 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary -NOAA/GOES/16/MCMIPF GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPF') NOAA 2017-07-10 2024-12-11 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF proprietary -NOAA/GOES/16/MCMIPM GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPM') NOAA 2017-07-10 2024-12-11 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM proprietary +NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2024-12-12 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary +NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2024-12-12 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary +NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2024-12-12 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary +NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2024-12-12 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary +NOAA/GOES/16/MCMIPF GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPF') NOAA 2017-07-10 2024-12-12 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF proprietary +NOAA/GOES/16/MCMIPM GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPM') NOAA 2017-07-10 2024-12-12 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM proprietary NOAA/GOES/17/FDCC GOES-17 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/17/FDCC') NOAA 2018-08-27 2023-01-10 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_17, goes_s, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCC proprietary NOAA/GOES/17/FDCF GOES-17 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/FDCF') NOAA 2018-08-27 2023-01-10 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_17, goes_s, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCF proprietary NOAA/GOES/17/MCMIPC GOES-17 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPC') NOAA 2018-12-04 2023-01-10 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPC proprietary NOAA/GOES/17/MCMIPF GOES-17 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPF') NOAA 2018-12-04 2023-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPF proprietary NOAA/GOES/17/MCMIPM GOES-17 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPM') NOAA 2018-12-04 2023-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPM proprietary -NOAA/GOES/18/FDCC GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/18/FDCC') NOAA 2022-10-13 2024-12-11 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC proprietary -NOAA/GOES/18/FDCF GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/FDCF') NOAA 2022-10-13 2024-12-11 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF proprietary -NOAA/GOES/18/MCMIPC GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPC') NOAA 2018-12-04 2024-12-11 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC proprietary -NOAA/GOES/18/MCMIPF GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPF') NOAA 2018-12-04 2024-12-11 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF proprietary -NOAA/GOES/18/MCMIPM GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPM') NOAA 2018-12-04 2024-12-11 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM proprietary +NOAA/GOES/18/FDCC GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/18/FDCC') NOAA 2022-10-13 2024-12-12 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC proprietary +NOAA/GOES/18/FDCF GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/FDCF') NOAA 2022-10-13 2024-12-12 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF proprietary +NOAA/GOES/18/MCMIPC GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPC') NOAA 2018-12-04 2024-12-12 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC proprietary +NOAA/GOES/18/MCMIPF GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPF') NOAA 2018-12-04 2024-12-12 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF proprietary +NOAA/GOES/18/MCMIPM GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPM') NOAA 2018-12-04 2024-12-12 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM proprietary NOAA/IBTrACS/v4 International Best Track Archive for Climate Stewardship Project table ee.FeatureCollection('NOAA/IBTrACS/v4') NOAA NCEI 1842-10-25 2024-05-19 -180, 0.4, 180, 63.1 False hurricane, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_IBTrACS_v4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_IBTrACS_v4 proprietary NOAA/NCEP_DOE_RE2/total_cloud_coverage NCEP-DOE Reanalysis 2 (Gaussian Grid), Total Cloud Coverage image_collection ee.ImageCollection('NOAA/NCEP_DOE_RE2/total_cloud_coverage') NOAA 1979-01-01 2024-11-30 -180, -90, 180, 90 False atmosphere, climate, cloud, geophysical, ncep, noaa, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NCEP_DOE_RE2_total_cloud_coverage.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NCEP_DOE_RE2_total_cloud_coverage proprietary NOAA/NGDC/ETOPO1 ETOPO1: Global 1 Arc-Minute Elevation image ee.Image('NOAA/NGDC/ETOPO1') NOAA 2008-08-01 2008-08-01 -180, -90, 180, 90 False bedrock, dem, elevation, geophysical, ice, noaa, topography https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NGDC_ETOPO1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1 proprietary NOAA/NHC/HURDAT2/atlantic NOAA NHC HURDAT2 Atlantic Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/atlantic') NOAA NHC 1851-06-25 2018-11-04 -109.5, 7.2, 63, 81 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_atlantic.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_atlantic proprietary NOAA/NHC/HURDAT2/pacific NOAA NHC HURDAT2 Pacific Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/pacific') NOAA NHC 1949-06-11 2018-11-09 -180, 0.4, 180, 63.1 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_pacific.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_pacific proprietary -NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2024-12-11 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary +NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2024-12-12 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary NOAA/PERSIANN-CDR PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record image_collection ee.ImageCollection('NOAA/PERSIANN-CDR') NOAA NCDC 1983-01-01 2024-03-31 -180, -60, 180, 60 False cdr, climate, geophysical, ncdc, noaa, persiann, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_PERSIANN-CDR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR proprietary NOAA/VIIRS/001/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09GA') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-16 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA proprietary NOAA/VIIRS/001/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-09 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09H1 proprietary @@ -660,7 +660,7 @@ NOAA/VIIRS/001/VNP22Q2 VNP22Q2: Land Surface Phenology Yearly L3 Global 500m SIN NOAA/VIIRS/001/VNP43IA1 VNP43IA1: BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP43IA1') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-06-09 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP43IA1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP43IA1 proprietary NOAA/VIIRS/001/VNP43IA2 VNP43IA2: BRDF/Albedo Quality Daily L3 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP43IA2') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-06-09 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP43IA2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP43IA2 proprietary NOAA/VIIRS/001/VNP46A1 VNP46A1: VIIRS Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP46A1') NASA LAADS DAAC 2012-01-19 2024-12-08 -180, -90, 180, 90 False daily, dnb, nasa, noaa, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A1 proprietary -NOAA/VIIRS/001/VNP46A2 VNP46A2: VIIRS Lunar Gap-Filled BRDF Nighttime Lights Daily L3 Global 500m image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP46A2') NASA LAADS DAAC 2012-01-19 2024-11-28 -180, -90, 180, 90 False brdf, daily, nasa, noaa, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A2 proprietary +NOAA/VIIRS/001/VNP46A2 VNP46A2: VIIRS Lunar Gap-Filled BRDF Nighttime Lights Daily L3 Global 500m image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP46A2') NASA LAADS DAAC 2012-01-19 2024-12-01 -180, -90, 180, 90 False brdf, daily, nasa, noaa, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A2 proprietary NOAA/VIIRS/001/VNP64A1 VNP64A1: Burned Area Monthly L4 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP64A1') NASA LP DAAC at the USGS EROS Center 2014-01-01 2019-01-01 -180, -90, 180, 90 False burn, change_detection, land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP64A1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP64A1 proprietary NOAA/VIIRS/DNB/ANNUAL_V21 VIIRS Nighttime Day/Night Annual Band Composites V2.1 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/ANNUAL_V21') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2012-04-01 2021-01-01 -180, -65, 180, 75 False annual, dnb, eog, lights, nighttime, noaa, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_ANNUAL_V21.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V21 proprietary NOAA/VIIRS/DNB/ANNUAL_V22 VIIRS Nighttime Day/Night Annual Band Composites V2.2 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/ANNUAL_V22') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2012-04-01 2023-01-01 -180, -65, 180, 75 False annual, dnb, eog, lights, nighttime, noaa, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_ANNUAL_V22.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V22 proprietary @@ -668,7 +668,7 @@ NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG VIIRS Nighttime Day/Night Band Composites Versi NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2014-01-01 2024-07-01 -180, -65, 180, 75 False dnb, eog, lights, monthly, nighttime, noaa, stray_light, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG proprietary NRCan/CDEM Canadian Digital Elevation Model image_collection ee.ImageCollection('NRCan/CDEM') NRCan 1945-01-01 2011-01-01 -142, 41, -52, 84 False canada, cdem, dem, elevation, geophysical, nrcan, topography https://storage.googleapis.com/earthengine-stac/catalog/NRCan/NRCan_CDEM.json https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM OGL-Canada-2.0 Netherlands/Beeldmateriaal/LUCHTFOTO_RGB Netherlands orthophotos image_collection ee.ImageCollection('Netherlands/Beeldmateriaal/LUCHTFOTO_RGB') Beeldmateriaal Nederland 2021-01-01 2022-12-31 3.2, 50.75, 7.22, 53.7 False orthophoto, rgb, netherlands https://storage.googleapis.com/earthengine-stac/catalog/Netherlands/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB.json https://developers.google.com/earth-engine/datasets/catalog/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB CC-BY-4.0 -OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2024-12-08 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary +OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2024-12-09 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary OREGONSTATE/PRISM/AN81m PRISM Monthly Spatial Climate Dataset AN81m image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81m') PRISM / OREGONSTATE 1895-01-01 2024-11-01 -125, 24, -66, 50 False climate, geophysical, monthly, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m proprietary OREGONSTATE/PRISM/Norm81m PRISM Long-Term Average Climate Dataset Norm81m [deprecated] image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm81m') PRISM / OREGONSTATE 1981-01-01 2010-12-31 -125, 24, -66, 50 True 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m proprietary OREGONSTATE/PRISM/Norm91m PRISM Long-Term Average Climate Dataset Norm91m image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm91m') PRISM / OREGONSTATE 1991-01-01 2020-12-31 -125, 24, -66, 50 False 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm91m proprietary @@ -731,7 +731,7 @@ TIGER/2018/States TIGER: US Census States 2018 table ee.FeatureCollection('TIGER TIGER/2020/BG TIGER: US Census Block Groups (BG) 2020 table ee.FeatureCollection('TIGER/2020/BG') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_BG.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_BG proprietary TIGER/2020/TABBLOCK20 TIGER: 2020 Tabulation (Census) Block table ee.FeatureCollection('TIGER/2020/TABBLOCK20') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TABBLOCK20.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TABBLOCK20 proprietary TIGER/2020/TRACT TIGER: US Census Tracts table ee.FeatureCollection('TIGER/2020/TRACT') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TRACT.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TRACT proprietary -TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2024-12-09 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary +TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2024-12-10 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary TRMM/3B42 TRMM 3B42: 3-Hourly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B42') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-31 -180, -50, 180, 50 False 3_hourly, climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B42.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42 proprietary TRMM/3B43V7 TRMM 3B43: Monthly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B43V7') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-01 -180, -50, 180, 50 False climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B43V7.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7 proprietary TUBerlin/BigEarthNet/v1 TUBerlin/BigEarthNet/v1 image_collection ee.ImageCollection('TUBerlin/BigEarthNet/v1') BigEarthNet 2017-06-01 2018-05-31 -9, 36.9, 31.6, 68.1 False chip, copernicus, corine_derived, label, ml, sentinel, tile https://storage.googleapis.com/earthengine-stac/catalog/TUBerlin/TUBerlin_BigEarthNet_v1.json https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1 proprietary @@ -820,7 +820,7 @@ USGS/WBD/2017/HUC06 HUC06: USGS Watershed Boundary Dataset of Basins table ee.Fe USGS/WBD/2017/HUC08 HUC08: USGS Watershed Boundary Dataset of Subbasins table ee.FeatureCollection('USGS/WBD/2017/HUC08') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC08.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC08 proprietary USGS/WBD/2017/HUC10 HUC10: USGS Watershed Boundary Dataset of Watersheds table ee.FeatureCollection('USGS/WBD/2017/HUC10') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC10.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC10 proprietary USGS/WBD/2017/HUC12 HUC12: USGS Watershed Boundary Dataset of Subwatersheds table ee.FeatureCollection('USGS/WBD/2017/HUC12') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC12.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC12 proprietary -UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2024-12-09 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0 +UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2024-12-11 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0 VITO/PROBAV/C1/S1_TOC_100M PROBA-V C1 Top Of Canopy Daily Synthesis 100m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_100M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M proprietary VITO/PROBAV/C1/S1_TOC_333M PROBA-V C1 Top Of Canopy Daily Synthesis 333m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_333M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M proprietary VITO/PROBAV/S1_TOC_100M PROBA-V C0 Top Of Canopy Daily Synthesis 100m [deprecated] image_collection ee.ImageCollection('VITO/PROBAV/S1_TOC_100M') Vito / ESA 2013-10-17 2016-12-14 -180, -90, 180, 90 True esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_S1_TOC_100M proprietary @@ -894,7 +894,7 @@ projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020 F projects/forestdatapartnership/assets/community_palm/20240312 Palm Probability v20240312 [deprecated] image_collection ee.ImageCollection('projects/forestdatapartnership/assets/community_palm/20240312') Produced by Google for the Forest Data Partnership 2020-01-01 2023-12-31 92.99, -11.94, 132.71, 11.71 True deforestation, eudr, biodiversity, conservation, crop, landuse, palm, plantation https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_community_palm_20240312.json https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_community_palm_20240312 CC-BY-4.0 projects/forestdatapartnership/assets/palm/model_2024a Palm Probability model 2024a image_collection ee.ImageCollection('projects/forestdatapartnership/assets/palm/model_2024a') Produced by Google for the Forest Data Partnership 2020-01-01 2023-12-31 -180, -90, 180, 90 False eudr, biodiversity, conservation, crop, landuse, palm, plantation https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2024a.json https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2024a CC-BY-NC-4.0 projects/forestdatapartnership/assets/rubber/model_2024a Rubber Tree Probability model 2024a image_collection ee.ImageCollection('projects/forestdatapartnership/assets/rubber/model_2024a') Produced by Google for the Forest Data Partnership 2020-01-01 2023-12-31 -180, -90, 180, 90 False eudr, biodiversity, conservation, crop, landuse, rubber, plantation, pre_review https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2024a.json https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2024a CC-BY-NC-4.0 -projects/gcp-public-data-weathernext/assets/59572747_4_0 WeatherNext Graph Forecasts image_collection ee.ImageCollection('projects/gcp-public-data-weathernext/assets/59572747_4_0') Google 2020-01-01 2024-12-11 -180, -90, 180, 90 False weather, weathernext, forecast, temperature, precipitation, wind https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0.json https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_59572747_4_0 proprietary +projects/gcp-public-data-weathernext/assets/59572747_4_0 WeatherNext Graph Forecasts image_collection ee.ImageCollection('projects/gcp-public-data-weathernext/assets/59572747_4_0') Google 2020-01-01 2024-12-12 -180, -90, 180, 90 False weather, weathernext, forecast, temperature, precipitation, wind https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0.json https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_59572747_4_0 proprietary projects/geoscience-aus-cat/assets/NIDEM National Intertidal Digital Elevation Model 25m 1.0.0 image ee.Image('projects/geoscience-aus-cat/assets/NIDEM') Geoscience Australia 1986-08-16 2017-07-31 108.81, -44.41, 157.82, -9.13 False australia, ga, dem, landsat_derived https://storage.googleapis.com/earthengine-stac/catalog/geoscience-aus-cat/projects_geoscience-aus-cat_assets_NIDEM.json https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_NIDEM CC-BY-4.0 projects/geoscience-aus-cat/assets/ga_ls5t_nbart_gm_cyear_3 DEA Geometric Median and Median Absolute Deviation - Landsat 5 3.1.0 image_collection ee.ImageCollection('projects/geoscience-aus-cat/assets/ga_ls5t_nbart_gm_cyear_3') Geoscience Australia 1998-01-01 2012-01-01 108.81, -44.41, 157.82, -9.13 False australia, ga, landsat_derived https://storage.googleapis.com/earthengine-stac/catalog/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls5t_nbart_gm_cyear_3.json https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_ga_ls5t_nbart_gm_cyear_3 CC-BY-4.0 projects/geoscience-aus-cat/assets/ga_ls7e_nbart_gm_cyear_3 DEA Geometric Median and Median Absolute Deviation - Landsat 7 3.1.0 image_collection ee.ImageCollection('projects/geoscience-aus-cat/assets/ga_ls7e_nbart_gm_cyear_3') Geoscience Australia 2000-01-01 2021-01-01 108.81, -44.41, 157.82, -9.13 False australia, ga, landsat_derived https://storage.googleapis.com/earthengine-stac/catalog/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls7e_nbart_gm_cyear_3.json https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_ga_ls7e_nbart_gm_cyear_3 CC-BY-4.0 diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json index fabcfce..24fa332 100644 --- a/nasa_cmr_catalog.json +++ b/nasa_cmr_catalog.json @@ -26,16 +26,16 @@ "license": "proprietary" }, { - "id": "016f577b631a429a8558796a74983154_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 5.0", + "id": "024292dcda5d42ceb326850f89f8b40d_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "catalog": "FEDEO STAC Catalog", "state_date": "1997-09-04", - "end_date": "2020-12-31", + "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143519-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143519-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/016f577b631a429a8558796a74983154_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 5.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359340-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359340-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/024292dcda5d42ceb326850f89f8b40d_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "license": "proprietary" }, { @@ -91,16 +91,16 @@ "license": "proprietary" }, { - "id": "07eeca6888c645d89a7ef91de0290eca_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 4.2", + "id": "0875b4675f1e46ebadb526e0b95505c5_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0", "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", + "state_date": "1997-09-04", + "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142626-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142626-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/07eeca6888c645d89a7ef91de0290eca_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359430-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359430-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/0875b4675f1e46ebadb526e0b95505c5_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "license": "proprietary" }, { @@ -174,11 +174,11 @@ "catalog": "FEDEO STAC Catalog", "state_date": "2018-08-30", "end_date": "", - "bbox": "-180, -52, 180, 52", + "bbox": "-180, -52, 180, 56", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458058-FEDEO.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458058-FEDEO.html", "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA", - "description": "The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-13614/", + "description": "The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/de/eoc/forschung-transfer/projekte-und-missionen/desis", "license": "proprietary" }, { @@ -2157,6 +2157,19 @@ "description": "This record comprises composite 10Be concentrations from three Law Dome ice cores (DSS0506-core, DSS0809-core and DSS0910-core). Sample dating is revised from that presented in Pedro et al., clim. Past 7, 707-721, 2011 by accounting for sub-seasonal variability in snow accumulation. The accumulation record was derived from the European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim). See Appendix 1 of Pedro et al., J. Geophys. Res. 116, D23120, 2011 for details of method.", "license": "proprietary" }, + { + "id": "1115d8946ba74c7f8a9fc3bfee5513a0_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00", + "catalog": "FEDEO STAC Catalog", + "state_date": "2002-07-25", + "end_date": "2012-04-08", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359280-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359280-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/1115d8946ba74c7f8a9fc3bfee5513a0_NA", + "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", + "license": "proprietary" + }, { "id": "1162_4_IPEV_FR", "title": "Adult integument colour - MDO Alaska", @@ -2235,19 +2248,6 @@ "description": "Optimally interpolated atmospheric surface pressure over the Arctic Ocean Basin. Temporal format - twice daily (0Z and 12Z) Spatial format - 2 degree latitude x 10 degree longitude - latitude: 70 N - 90 N - longitude: 0 E - 350 E", "license": "proprietary" }, - { - "id": "12d6f4bdabe144d7836b0807e65aa0e2_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142978-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142978-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/12d6f4bdabe144d7836b0807e65aa0e2_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "142052b9dc754f6da47a631e35ec4609_NA", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Time series of gridded Sea Level Anomalies (SLA), Version 2.0", @@ -2287,19 +2287,6 @@ "description": "In Southern Chile, plate configuration is characterized by ridge-ridge-trench collision in correspondence of Taitao peninsula (Chile triple junction). The different converging rates of Nazca and Antarctic plates favored the formation of a forearc sliver (Chiloe block) limited to west by a dextral transcurrent fault system, Known as Liquine-Ofqui fault system (LOFS). During the Quatenary time, a series of monogenetic volcanic centers, as Puyuhuapi volcanic centers (PVC), formed along the LOFS. The PVC lavas have a primitive character; two groups and can be distinguihed. Group-1 rocks show a K-AlKaline affinity and are nepheline normative with olivine and plagioclase as dominant phases. Group-2 lavas have Na-affinity with olivine and hyperstene in the norm; olivine is the most abundant mineral phase. In contrast with overall alkaline affinity of PVC, the products from the neighboring central composite volcanoes are generally calcalkaline with the exception of the lavas from Maca Volcano, which show tholeiitic affinity.", "license": "proprietary" }, - { - "id": "159649796f2943689a836999016188f0_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142794-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142794-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/159649796f2943689a836999016188f0_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 3.1 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", - "license": "proprietary" - }, { "id": "16920eb2-2eaf-4629-8337-3626e70e4770", "title": "Africa - Photovoltaic Solar Electricity Potential", @@ -2313,6 +2300,19 @@ "description": "The map displays the quantity of energy that reached equator-oriented photovoltaic modules that are optimally-inclined to maximise yearly electricity yields. This map is computed from observations made by meteorological satellites. Click on map to enlarge. If you use this map, mention this copyright please: PVGIS copyright European Commission 2001-2008 and HelioClim-1 copyright Mines ParisTech / Armines 2001-2008.", "license": "proprietary" }, + { + "id": "16c633f003ef4d8481420f052356c11c_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00", + "catalog": "FEDEO STAC Catalog", + "state_date": "1995-08-01", + "end_date": "2003-06-22", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359482-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359482-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/16c633f003ef4d8481420f052356c11c_NA", + "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u00e2\u0080\u0093 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", + "license": "proprietary" + }, { "id": "1747-ESDD", "title": "Alaskan Geologic Photography Collection from USGS", @@ -3210,19 +3210,6 @@ "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products.", "license": "proprietary" }, - { - "id": "1f84f9465e65416ca45cd20bc415b522_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142553-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142553-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/1f84f9465e65416ca45cd20bc415b522_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", - "license": "proprietary" - }, { "id": "200001010_1", "title": "Aurora Australis Voyage 1 2000-01 Underway Data", @@ -4796,6 +4783,19 @@ "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2019/20 season. Purpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support. Voyage Leader: Ms Nicki Wicksl Deputy Voyage Leader: Mr Chris Hill Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).", "license": "proprietary" }, + { + "id": "20ec12f5d1f94e99aff2ed796264ee65_NA", + "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-01-01", + "end_date": "2021-12-31", + "bbox": "-180, 25, 180, 85", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359729-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359729-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/20ec12f5d1f94e99aff2ed796264ee65_NA", + "description": "This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", + "license": "proprietary" + }, { "id": "22254b5608ab430fa360d0ff7e71c34e_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1", @@ -4809,19 +4809,6 @@ "description": "This dataset contains a time series of ice velocities for the Petermann glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 16/08/1991 and 01/06/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 1 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "license": "proprietary" }, - { - "id": "222cf11f49a94d2da8a6da239df2efc4_NA", - "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions (2002-2018) from the JASON satellites, v1.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "2002-01-15", - "end_date": "2018-05-30", - "bbox": "-30, -45, 160, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143583-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143583-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/222cf11f49a94d2da8a6da239df2efc4_NA", - "description": "This dataset contains along-track sea level anomalies derived from satellite altimetry. Altimeter along-track sea level measurements from the Jason-1, Jason -2 and Jason-3 satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). These six time series cover the period from 15 January 2002 to 30 May 2018.The product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. The main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). During the project, the product will be extended in spatial coverage and with additional altimeter missions. This version of the dataset is v1.1. (DOI: 10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005)", - "license": "proprietary" - }, { "id": "2282b4aeb9f24bc3a1e0961e4d545427_NA", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1", @@ -4952,6 +4939,19 @@ "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps.", "license": "proprietary" }, + { + "id": "2ac9a3e7bdeb41b58b226a2fa612a4a3_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00", + "catalog": "FEDEO STAC Catalog", + "state_date": "2002-08-01", + "end_date": "2012-03-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359539-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359539-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/2ac9a3e7bdeb41b58b226a2fa612a4a3_NA", + "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage for the monthly dataset starts from August 2002 and ends March 2012. There is a twelve day gap in the underlying data due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", + "license": "proprietary" + }, { "id": "2bb39206-6988-4127-89e5-85a0430e20cc", "title": "Earthquakes frequency for MMI categories higher than 9 1973-2007", @@ -5109,23 +5109,23 @@ "license": "proprietary" }, { - "id": "3534bbf43fa14e40bc61944eaf664511_NA", - "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2", + "id": "330b7c922a37420fabb3425671d7d7c6_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00", "catalog": "FEDEO STAC Catalog", - "state_date": "2017-11-10", - "end_date": "2018-12-31", + "state_date": "2016-05-01", + "end_date": "2020-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142898-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142898-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/3534bbf43fa14e40bc61944eaf664511_NA", - "description": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 \u00c2\u00b5m spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD.The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.This data was produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360235-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360235-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/330b7c922a37420fabb3425671d7d7c6_NA", + "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "license": "proprietary" }, { "id": "35ea8189e75e4b6f95e7c86812080ecb_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v1.4", "catalog": "FEDEO STAC Catalog", - "state_date": "2002-03-31", + "state_date": "2002-04-01", "end_date": "2017-06-30", "bbox": "-80, 60, -10, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143445-FEDEO.umm_json", @@ -5173,19 +5173,6 @@ "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides seasonal maps.", "license": "proprietary" }, - { - "id": "37e8a29d208d4a87ae4dbe1d16b2c0ef_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143560-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143560-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/37e8a29d208d4a87ae4dbe1d16b2c0ef_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.", - "license": "proprietary" - }, { "id": "38725_Not Applicable", "title": "Assessment of Existing Information for Atlantic Coastal Fish Habitat Partnership (ACFHP)", @@ -6994,16 +6981,16 @@ "license": "proprietary" }, { - "id": "3b3fd2daf3d34c1bb4a09efeaf3b8ea9_NA", - "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2019), version 1.0", + "id": "3bdb21a4cd004e5f8cc148fea5f1d4e3_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0", "catalog": "FEDEO STAC Catalog", - "state_date": "2000-02-24", - "end_date": "2019-12-31", + "state_date": "1997-09-04", + "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142546-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142546-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/3b3fd2daf3d34c1bb4a09efeaf3b8ea9_NA", - "description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 \u00e2\u0080\u0093 2019. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00c3\u00a4m\u00c3\u00a4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00c3\u00a4m\u00c3\u00a4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 \u00c2\u00b5m, and an emissive band centred at about 11 \u00c2\u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359686-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359686-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/3bdb21a4cd004e5f8cc148fea5f1d4e3_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", "license": "proprietary" }, { @@ -7179,7 +7166,7 @@ "id": "46d136149d0a4f1cb8de7efbe8abf4b2_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-03-31", + "state_date": "2009-04-01", "end_date": "2015-12-30", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142763-FEDEO.umm_json", @@ -7201,6 +7188,19 @@ "description": "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is \"Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487.\" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes.", "license": "proprietary" }, + { + "id": "474ac06235e54e6cb0ec6eed635e1213_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359219-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359219-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/474ac06235e54e6cb0ec6eed635e1213_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", + "license": "proprietary" + }, { "id": "48fc3d1e8ada405c8486ada522dae9e8_NA", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0", @@ -7279,32 +7279,6 @@ "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the products coverage size of 370 km x 370 km make AWiFS products a valuable source for application fields such forestry and environmental monitoring", "license": "proprietary" }, - { - "id": "51fc11a9438b466db2ec8bd098efe7d5_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143188-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143188-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/51fc11a9438b466db2ec8bd098efe7d5_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", - "license": "proprietary" - }, - { - "id": "52266ccfbc3348a8afc27b67d6bbc6c2_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142498-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142498-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/52266ccfbc3348a8afc27b67d6bbc6c2_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 3.1 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", - "license": "proprietary" - }, { "id": "53554204-282b-457e-b36d-a168679a0c1f_NA", "title": "TerraSAR-X - Stripmap Images (TerraSAR-X StripMap)", @@ -7318,32 +7292,6 @@ "description": "This collection contains radar image products of the German national TerraSAR-X mission acquired in StripMap mode. StripMap imaging allows for a spatial resolution of up to 3 m at a scene size of 30 km (across swath) x 50-1650 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space.\t\t\tFor more information concerning the TerraSAR-X mission, the reader is referred to:\t\t\thttps://www.dlr.de/content/de/missionen/terrasar-x.html", "license": "proprietary" }, - { - "id": "5400de38636d43de9808bfc0b500e863_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142630-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142630-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5400de38636d43de9808bfc0b500e863_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, - { - "id": "5484dc1392bc43c1ace73ba38a22ac56_NA", - "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2019), version1.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1995-08-01", - "end_date": "2003-06-22", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142797-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142797-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5484dc1392bc43c1ace73ba38a22ac56_NA", - "description": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1982-2019. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00c3\u00a4m\u00c3\u00a4ki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 \u00c2\u00b5m (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 \u00c2\u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years.", - "license": "proprietary" - }, { "id": "54e2ee0803764b4e84c906da3f16d81b_NA", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0", @@ -7370,24 +7318,11 @@ "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.", "license": "proprietary" }, - { - "id": "55c20c0cb35b4a7c8ef8b65694fe46e2_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142509-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142509-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/55c20c0cb35b4a7c8ef8b65694fe46e2_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 3.1 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", - "license": "proprietary" - }, { "id": "56f81895cb094bd8a1638aa12d6c7499_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-04-17", + "state_date": "2009-04-18", "end_date": "2015-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142501-FEDEO.umm_json", @@ -7422,19 +7357,6 @@ "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational BrO (Bromine monoxide) total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to https://atmos.eoc.dlr.de/app/missions/gome2", "license": "proprietary" }, - { - "id": "584d4028633a4b7e9fa36da72dbd91c7_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143369-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143369-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/584d4028633a4b7e9fa36da72dbd91c7_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", - "license": "proprietary" - }, { "id": "58f00d8814064b79a0c49662ad3af537_NA", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1", @@ -7510,20 +7432,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2207457992-FEDEO.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2207457992-FEDEO.html", "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5a168a35-8cd2-4960-a134-2f319bb06760_NA", - "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tThe operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tThe new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", - "license": "proprietary" - }, - { - "id": "5ab5267b17254152bcdbc055747faa02_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142826-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142826-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5ab5267b17254152bcdbc055747faa02_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", + "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tThe new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "license": "proprietary" }, { @@ -7592,16 +7501,29 @@ "license": "proprietary" }, { - "id": "5eecdf4c-de57-4624-99e9-60086b032aea_NA", - "title": "TanDEM-X - Digital Elevation Model (DEM) - Global, 12m", + "id": "5f331c418e9f4935b8eb1b836f8a91b8_NA", + "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3", "catalog": "FEDEO STAC Catalog", - "state_date": "2010-12-12", - "end_date": "2015-01-16", - "bbox": "-180, -90, 180, 84", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2207457995-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2207457995-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5eecdf4c-de57-4624-99e9-60086b032aea_NA", - "description": "TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical separation between the satellites of 120m to 500m a global Digital Elevation Model (DEM) has been generated. The main objective of the TanDEM-X mission is to create a precise 3D map of the Earth's land surfaces that is homogeneous in quality and unprecedented in accuracy. The data acquisition was completed in 2015 and production of the global DEM was completed in September 2016. The absolute height error is with about 1m an order of magnitude below the 10m requirement.The TanDEM-X 12m DEM is the nominal product variant of the global Digital Elevation Model (DEM) acquired in the frame of the German TanDEM-X mission between 2010 and 2015 with a spatial resolution of 0.4 arcseconds (12m at the equator). It covers all Earth\u2019s landmasses from pole to pole. For more information concerning the TanDEM-X mission, the reader is referred to: https://www.dlr.de/dlr/en/desktopdefault.aspx/tabid-10378/", + "state_date": "2010-01-01", + "end_date": "2020-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360191-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360191-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5f331c418e9f4935b8eb1b836f8a91b8_NA", + "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR instrument and JAXA\u00e2\u0080\u0099s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", + "license": "proprietary" + }, + { + "id": "5f66a881adf846bfaad58b0e6068f0ea_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00", + "catalog": "FEDEO STAC Catalog", + "state_date": "2018-11-17", + "end_date": "2020-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360139-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360139-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/5f66a881adf846bfaad58b0e6068f0ea_NA", + "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "license": "proprietary" }, { @@ -7617,19 +7539,6 @@ "description": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This version of the product is v2.1, which is an extension of the version 2.0 Sea_Ice_cci dataset and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 25km grid spacing is also available.", "license": "proprietary" }, - { - "id": "612a615afb5d48459b385380b440b545_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142614-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142614-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/612a615afb5d48459b385380b440b545_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2020.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.", - "license": "proprietary" - }, { "id": "62866635ab074e07b93f17fbf87a2c1a_NA", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1", @@ -7647,7 +7556,7 @@ "id": "62c0f97b1eac4e0197a674870afe1ee6_NA", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1", "catalog": "FEDEO STAC Catalog", - "state_date": "1981-08-31", + "state_date": "1981-09-01", "end_date": "2016-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142903-FEDEO.umm_json", @@ -7682,19 +7591,6 @@ "description": "The map (risk map) presents the results of cyclonic wind probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. ", "license": "proprietary" }, - { - "id": "66534da90ed44abebfc1b08adca4f9c3_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143489-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143489-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/66534da90ed44abebfc1b08adca4f9c3_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", - "license": "proprietary" - }, { "id": "67a3f8c8dc914ef99f7f08eb0d997e23_NA", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0", @@ -7708,6 +7604,19 @@ "description": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.Case A: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.", "license": "proprietary" }, + { + "id": "690fdf8f229c4d04a2aa68de67beb733_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359002-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359002-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/690fdf8f229c4d04a2aa68de67beb733_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.", + "license": "proprietary" + }, { "id": "6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA", "title": "METOP GOME-2 - Sulfur Dioxide (SO2) - Global", @@ -7718,7 +7627,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458007-FEDEO.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458007-FEDEO.html", "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA", - "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.\t\t\tThe operational SO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. GDP 4.x performs a DOAS fit for SO2 slant column followed by an AMF / VCD computation using a single wavelength. Corrections are applied to the slant column for equatorial offset, interference of SO2 and SO2 absorption, and SZA dependence.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", + "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\t\t\tThe operational SO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. GDP 4.x performs a DOAS fit for SO2 slant column followed by an AMF / VCD computation using a single wavelength. Corrections are applied to the slant column for equatorial offset, interference of SO2 and SO2 absorption, and SZA dependence.\t\t\tFor more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "license": "proprietary" }, { @@ -7790,7 +7699,7 @@ "id": "7db4459605da4665b6ab9a7102fb4875_NA", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1", "catalog": "FEDEO STAC Catalog", - "state_date": "1981-08-23", + "state_date": "1981-08-24", "end_date": "2016-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142734-FEDEO.umm_json", @@ -7851,6 +7760,19 @@ "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 gridded stratospheric aerosol properties from the GOMOS instrument on the ENVISAT satellite. This version of the data is version 3.00, and has been derived using the AERGOM algorithm by BIRA (Belgian Institute for Space Aeronomy). For further details about these data products please see the linked documentation.", "license": "proprietary" }, + { + "id": "7fc9df8070d34cacab8092e45ef276f1_NA", + "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.1", + "catalog": "FEDEO STAC Catalog", + "state_date": "1992-09-26", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360101-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360101-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/7fc9df8070d34cacab8092e45ef276f1_NA", + "description": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.1.0 of the dataset.The six thematic climate variables included in this dataset are:\u00e2\u0080\u00a2 Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\u00e2\u0080\u00a2 Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\u00e2\u0080\u00a2 Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\u00e2\u0080\u00a2 Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\u00e2\u0080\u00a2 Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\u00e2\u0080\u00a2 Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.Satellite sensors associated with the thematic climate variables are as follows:LWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; LWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; LIC: Terra/Aqua MODIS; LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Cr\u00c3\u00a9taux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z", + "license": "proprietary" + }, { "id": "802569b8-fb56-4d78-a2e8-3e4549ff475b_NA", "title": "AVHRR - Sea Surface Temperature (SST) - Europe", @@ -7864,19 +7786,6 @@ "description": "The AVHRR Mulitchannel Sea Surface Temperature Map (MCSST) was the first result of DLR's AVHRR pathfinder activities. The goal of the product is to provide the user with actual Sea Surface Temperature (SST) maps in a defined format easy to access with the highest possible reliability on the thematic quality. After a phase of definition, the operational production chain was launched in March 1993 covering the entire Mediterranean Sea and the Black Sea. Since then, daily, weekly, and monthly data sets have been available until September 13, 1994, when the AVHRR on board the NOAA-11 spacecraft failed. The production of daily, weekly and monthly SST maps was resumed in February, 1995, based on NOAA-14 AVHRR data. The NOAA-14 AVHRR sensor became some technical difficulties, so the generation was stopped on October 3, 2001. Since March 2002, NOAA-16 AVHRR SST maps are available again. With the beginning of January 2004, the data of AVHRR on board of NOAA-16 exhibited some anormal features showing strips in the scenes. Facing the \u201cbar coded\u201d images of NOAA16-AVHRR which occurred first in September 2003, continued in January 2004 for the second time and appeared in April 2004 again, DFD has decided to stop the reception of NOAA16 data on April 6th, 2004, and to start the reception of NOAA-17 data on this day. On April 7th, 2004, the production of all former NOAA16-AVHRR products as e.g. the SST composites was successully established. NOAA-17 is an AM sensor which passes central Europe about 2 hours earlier than NOAA-16 (about 10:00 UTC instead of 12:00 UTC for NOAA-16). In spring 2007, the communication system of NOAA-17 has degraded or is operating with limitations. Therefore, DFD has decided to shift the production of higher level products (NDVI, LST and SST) from NOAA-17 to NOAA-18 in April 2007. In order to test the performance of our processing chains, we processed simultaneously all NOAA-17 and NOAA-18 data from January 1st, 2007 till March 29th, 2007. All products are be available via EOWEB. Please remember that NOAA-18 is a PM sensor which passes central Europe about 1.5 hours later than NOAA-17 (about 11:30 UTC instead of 10:00 UTC for NOAA17). The SST product is intended for climate modelers, oceanographers, and all geo science-related disciplines dealing with ocean surface parameters. In addition, SST maps covering the North Atlantic, the Baltic Sea, the North Sea and the Western Atlantic equivalent to the Mediterranean MCSST maps are available since August 1994. The most important aspects of the MCSST maps are a) correct image registration and b) reasonable cloud screening to ensure that only cloud free pixels are taken for the later processing and compositing c) for deriving MCSST, only channel 4 and 5 are used.. The SST product consists of one 8 bit channel. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/", "license": "proprietary" }, - { - "id": "806b30b9dc7f44e6bd56a46d8bccf279_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143296-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143296-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/806b30b9dc7f44e6bd56a46d8bccf279_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", - "license": "proprietary" - }, { "id": "810631f4-c311-44f2-9ced-c2260df2bc06_NA", "title": "METOP GOME-2 - Cloud Optical Thickness (COT) - Global", @@ -7904,16 +7813,16 @@ "license": "proprietary" }, { - "id": "8154e881452f49c1ba86982ed88b20f0_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 5.0", + "id": "8175ede3a1d642deba8f4cce49d7bda8_NA", + "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - October 2023", "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", + "state_date": "2017-11-13", + "end_date": "2023-10-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143104-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143104-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/8154e881452f49c1ba86982ed88b20f0_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 5.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359018-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359018-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/8175ede3a1d642deba8f4cce49d7bda8_NA", + "description": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 \u00c2\u00b5m spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication: Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669\u00e2\u0080\u0093694, https://doi.org/10.5194/amt-16-669-2023, 2023.", "license": "proprietary" }, { @@ -7942,25 +7851,12 @@ "description": "This dataset contains a time series of ice velocities for the Storstrommen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 06/10/1991 and 20/03/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "license": "proprietary" }, - { - "id": "83e51cf29821434ea14db56c564946d5_NA", - "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1982-01-01", - "end_date": "2010-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142628-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142628-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/83e51cf29821434ea14db56c564946d5_NA", - "description": "This v2.1 SST_cci Climatology Data Record (CDR) consists of Level 4 daily climatology files gridded on a 0.05 degree grid. The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x", - "license": "proprietary" - }, { "id": "84403d09cef3485883158f4df2989b0c_NA", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2", "catalog": "FEDEO STAC Catalog", "state_date": "2010-01-01", - "end_date": "2018-12-31", + "end_date": "2020-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142753-FEDEO.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142753-FEDEO.html", @@ -7995,16 +7891,16 @@ "license": "proprietary" }, { - "id": "8545a026-2e0c-466f-b6de-99faa639e3c0_NA", - "title": "TanDEM-X - Digital Elevation Model (DEM) - Global, 30m", + "id": "86d360431f3b4184b89cdd1cd707bb33_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0", "catalog": "FEDEO STAC Catalog", - "state_date": "2010-12-12", - "end_date": "2015-01-16", - "bbox": "-180, -90, 180, 84", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458027-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458027-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/8545a026-2e0c-466f-b6de-99faa639e3c0_NA", - "description": "TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical separation between the satellites of 120m to 500m a global Digital Elevation Model (DEM) has been generated. The main objective of the TanDEM-X mission is to create a precise 3D map of the Earth's land surfaces that is homogeneous in quality and unprecedented in accuracy. The data acquisition was completed in 2015 and production of the global DEM was completed in September 2016. The absolute height error is with about 1m an order of magnitude below the 10m requirement.The TanDEM-X 30m DEM is a product variant of the global Digital Elevation Model (DEM) acquired in the frame of the German TanDEM-X mission between 2010 and 2015, and has a reduced pixel spacing of 1 arcsecond (30m at the equator). It covers all Earth\u2019s landmasses from pole to pole. For more information concerning the TanDEM-X mission, the reader is referred to: https://www.dlr.de/dlr/en/desktopdefault.aspx/tabid-10378/", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360264-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360264-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/86d360431f3b4184b89cdd1cd707bb33_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", "license": "proprietary" }, { @@ -8020,19 +7916,6 @@ "description": "The data set provides calving front locations of 28 major outlet glaciers of the Greenland Ice Sheet, produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change. The calving front location has been derived by manual delineation using SAR (Synthetic Aperture Radar) data from the ERS-1/2, Envisat and Sentinel-1 satellites and satellite imagery from LANDSAT 5,7,8. The digitized calving fronts are stored in ESRI vector shape-file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The product was generated by ENVEO (Environmental Earth Observation Information Technology GmbH)", "license": "proprietary" }, - { - "id": "88c2bc7af4f0402d8ceecad611c58cc5_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142619-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142619-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/88c2bc7af4f0402d8ceecad611c58cc5_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, this the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "88d02eb5a6c14952aa88028894d8a69c_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the D\u00c3\u00b8cker Smith Glacier between 2016-05-08 and 2016-05-18, generated using Sentinel-2 data, v1.0", @@ -8072,6 +7955,19 @@ "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. For further details about these data products please see the linked documentation.", "license": "proprietary" }, + { + "id": "8b9d461f245b4efd8ea9fa080366e3b1_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360650-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360650-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/8b9d461f245b4efd8ea9fa080366e3b1_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", + "license": "proprietary" + }, { "id": "8d475d7d92894765ad1ddda16de0e610_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik glacier from ERS-1, ERS-2, Envisat and PALSAR data for 1992-2010, v1.2", @@ -8085,6 +7981,19 @@ "description": "This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat and PALSAR data aquired between 02/01/1992 and 22/08/2010. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).", "license": "proprietary" }, + { + "id": "8ecae26f390b4938b67a97cbce3ecd8b_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359225-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359225-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/8ecae26f390b4938b67a97cbce3ecd8b_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", + "license": "proprietary" + }, { "id": "8f5623a85d2e4b9b8ab5313f65a7c994_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the IMAP-DOAS algorithm (CH4_SCI_IMAP), v7.2", @@ -8099,16 +8008,29 @@ "license": "proprietary" }, { - "id": "915d2340b178494f987a6942e263a2eb_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 3.1", + "id": "90049a6555d1480bb5ce9637051dede8_NA", + "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2", "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", + "state_date": "2002-01-01", + "end_date": "2019-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142807-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142807-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/915d2340b178494f987a6942e263a2eb_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359934-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359934-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/90049a6555d1480bb5ce9637051dede8_NA", + "description": "This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called \u00e2\u0080\u0098retracking\u00e2\u0080\u0099) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856). The dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Leg\u00c3\u00a9r Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Ni\u00c3\u00b1o Fernando, Legeais Jean Fran\u00c3\u00a7ois, Oelsmann Julius, Benveniste J\u00c3\u00a9r\u00c3\u00b4me (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354In addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean Fran\u00c3\u00a7ois, Oelsmann Julius, Restano Marco, Benveniste J\u00c3\u00a9r\u00c3\u00b4me (2022). Sea level along the world\u00e2\u0080\u0099s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z - Benveniste J\u00c3\u00a9r\u00c3\u00b4me, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean Fran\u00c3\u00a7ois, L\u00c3\u00a9ger Fabien, Ni\u00c3\u00b1o Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002\u00e2\u0080\u00932018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w", + "license": "proprietary" + }, + { + "id": "90682bac7d0e4e418085f30eba43dfba_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360219-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360219-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/90682bac7d0e4e418085f30eba43dfba_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", "license": "proprietary" }, { @@ -8128,7 +8050,7 @@ "id": "9255faeb392f41debf5402caa40dada8_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-04-17", + "state_date": "2009-04-18", "end_date": "2015-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143169-FEDEO.umm_json", @@ -8150,6 +8072,19 @@ "description": "This dataset contains a time series of ice velocity maps for the Kangerlussuag Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "license": "proprietary" }, + { + "id": "93444bc1c4364a59869e004bf9bfd94a_NA", + "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-01-01", + "end_date": "2021-12-31", + "bbox": "-180, 25, 180, 85", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360075-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360075-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/93444bc1c4364a59869e004bf9bfd94a_NA", + "description": "This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", + "license": "proprietary" + }, { "id": "93587051-2f12-4d37-a97b-520af56144ce_NA", "title": "MERIS - Vegetation Index (NDVI) - Europe, 10-Day", @@ -8245,7 +8180,7 @@ "id": "96d5b75ea29946c5aab8214ddbab252b_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-03-31", + "state_date": "2009-04-01", "end_date": "2015-12-30", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142712-FEDEO.umm_json", @@ -8280,19 +8215,6 @@ "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "license": "proprietary" }, - { - "id": "97aebb95404a4bde8405e9cf7e32b9f8_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142652-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142652-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/97aebb95404a4bde8405e9cf7e32b9f8_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 3.1 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "97ea298b-c382-46ef-9e36-926dead6a19d_NA", "title": "METOP GOME-2 - Tropospheric Nitrogen Dioxide (NO2) - Global", @@ -8306,19 +8228,6 @@ "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational NO2 tropospheric column products are generated using the algorithm GDP (GOME Data Processor) version 4.x for NO2 [Valks et al. (2011)] integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region using the DOAS method. An additional algorithm is applied to derive the tropospheric NO2 column: after subtracting the estimated stratospheric component from the total column, the tropospheric NO2 column is determined using an air mass factor based on monthly climatological NO2 profiles from the MOZART-2 model. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "license": "proprietary" }, - { - "id": "99348189bd33459cbd597a58c30d8d10_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142544-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142544-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/99348189bd33459cbd597a58c30d8d10_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", - "license": "proprietary" - }, { "id": "99b2c1d2-f2fd-4133-848a-8de849b958f7", "title": "Cyclones Winds - Hazard, Wind Speed 1000RP", @@ -8349,7 +8258,7 @@ "id": "9ed2813d2eda4d958e92ab3ce1ab1fe6_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-05-31", + "state_date": "2009-06-01", "end_date": "2014-05-30", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143059-FEDEO.umm_json", @@ -31553,26 +31462,26 @@ { "id": "ATL04_006", "title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL04_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL04_006", "description": "ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, { "id": "ATL04_006", "title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL04_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL04_006", "description": "ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, @@ -31618,26 +31527,26 @@ { "id": "ATL07_006", "title": "ATLAS/ICESat-2 L3A Sea Ice Height V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-14", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL07_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL07_006", "description": "The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, { "id": "ATL07_006", "title": "ATLAS/ICESat-2 L3A Sea Ice Height V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-14", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL07_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL07_006", "description": "The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.", "license": "proprietary" }, @@ -31826,78 +31735,78 @@ { "id": "ATL13_006", "title": "ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL13_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL13_006", "description": "This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).", "license": "proprietary" }, { "id": "ATL13_006", "title": "ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL13_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL13_006", "description": "This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).", "license": "proprietary" }, { "id": "ATL14_003", "title": "ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2019-03-29", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL14_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL14_003", "description": "ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.", "license": "proprietary" }, { "id": "ATL14_003", "title": "ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2019-03-29", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL14_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL14_003", "description": "ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.", "license": "proprietary" }, { "id": "ATL14_004", "title": "ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2019-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL14_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL14_004", "description": "This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).", "license": "proprietary" }, { "id": "ATL14_004", "title": "ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2019-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL14_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL14_004", "description": "This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).", "license": "proprietary" }, @@ -32008,52 +31917,52 @@ { "id": "ATL19_003", "title": "ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -88, 180, 88", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL19_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL19_003", "description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.", "license": "proprietary" }, { "id": "ATL19_003", "title": "ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-13", "end_date": "", "bbox": "-180, -88, 180, 88", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL19_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL19_003", "description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.", "license": "proprietary" }, { "id": "ATL20_004", "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2018-10-14", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL20_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL20_004", "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.", "license": "proprietary" }, { "id": "ATL20_004", "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2018-10-14", "end_date": "", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL20_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL20_004", "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.", "license": "proprietary" }, @@ -32332,7 +32241,7 @@ }, { "id": "ATTREX-Aircraft_Radiation_Measurements_1", - "title": "ATTREX-Aircraft_Radiation_Measurements", + "title": "ATTREX Global Hawk UAS Radiation Measurements", "catalog": "LARC_ASDC STAC Catalog", "state_date": "2011-10-01", "end_date": "2013-03-01", @@ -32345,7 +32254,7 @@ }, { "id": "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1", - "title": "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements", + "title": "ATTREX Global Hawk UAS Remote Sensing Temperature Measurements", "catalog": "LARC_ASDC STAC Catalog", "state_date": "2011-10-01", "end_date": "2013-03-01", @@ -32358,7 +32267,7 @@ }, { "id": "ATTREX-Aircraft_insitu_Cloud_property_Measurements_1", - "title": "ATTREX-Aircraft_insitu_Cloud_property_Measurements", + "title": "ATTREX Global Hawk UAS In-Situ Cloud Property Measurements", "catalog": "LARC_ASDC STAC Catalog", "state_date": "2011-10-01", "end_date": "2013-03-01", @@ -32371,7 +32280,7 @@ }, { "id": "ATTREX-Aircraft_insitu_TraceGas_Measurements_1", - "title": "ATTREX-Aircraft_insitu_TraceGas_Measurements", + "title": "ATTREX Global Hawk UAS In-Situ Trace Gas Measurements", "catalog": "LARC_ASDC STAC Catalog", "state_date": "2011-10-01", "end_date": "2013-03-01", @@ -32384,7 +32293,7 @@ }, { "id": "ATTREX-Aircraft_navigational_and_meteorological_Measurements_1", - "title": "ATTREX-Aircraft_navigational_and_meteorological_Measurements", + "title": "ATTREX Global Hawk UAS Meteorological and Navigational Measurements", "catalog": "LARC_ASDC STAC Catalog", "state_date": "2011-10-01", "end_date": "2013-03-01", @@ -42038,7 +41947,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763266338-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763266338-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30EM_002", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information. ", "license": "proprietary" }, { @@ -42051,7 +41960,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2600365286-LPDAAC_ECS.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2600365286-LPDAAC_ECS.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPDAAC_ECS/collections/CAM5K30EM_003", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information. ", "license": "proprietary" }, { @@ -42077,7 +41986,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763266343-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763266343-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/CAM5K30UC_002", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. ", "license": "proprietary" }, { @@ -42090,7 +41999,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2600365287-LPDAAC_ECS.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2600365287-LPDAAC_ECS.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPDAAC_ECS/collections/CAM5K30UC_003", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. ", "license": "proprietary" }, { @@ -57404,7 +57313,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3168830666-POCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3168830666-POCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections/CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2", - "description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.

This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.", + "description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.

This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.

This product is recommended for operational use. For science applications, we recommend the use of the Berkeley-RWAWC monthly product instead: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1 Note that the daily product consist of maps constructed using the most recent 31 days of data to rapidly capture surface water dynamics without relying on historical data. While the oldest data within this 31 day-period is weighted less and replaced by newer observations as they become available, extreme flood events may still be detected with a delay due to the incorporation of prior days\u2019 data into the algorithm. The incorporation of older data is necessary to maintain the spatial scale. ", "license": "proprietary" }, { @@ -57498,6 +57407,19 @@ "description": "This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).", "license": "proprietary" }, + { + "id": "CZCS_L0_1", + "title": "Nimbus-7 CZCS Level-0 Raw Science Data, version 1", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "1978-10-30", + "end_date": "1986-06-22", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327156612-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327156612-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/CZCS_L0_1", + "description": "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds.", + "license": "proprietary" + }, { "id": "CZCS_L1_1", "title": "Nimbus-7 Coastal Zone Color Scanner (CZCS) Data Regional Data", @@ -79634,7 +79556,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763259410-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763259410-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFCC30FCC_001", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", "license": "proprietary" }, { @@ -79647,7 +79569,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261610-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261610-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFCC30SR_001", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available \u201cleaf-on\u201d date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during \u201cleaf-off\u201d seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection. For each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available \u201cleaf-on\u201d date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during \u201cleaf-off\u201d seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection. For each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", "license": "proprietary" }, { @@ -79660,7 +79582,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763266352-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763266352-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFCC30TC_003", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", "license": "proprietary" }, { @@ -79673,7 +79595,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261619-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261619-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFCC30WC_001", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ", "license": "proprietary" }, { @@ -79712,7 +79634,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261621-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261621-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD1KCD_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data. The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data. The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012. ", "license": "proprietary" }, { @@ -79725,7 +79647,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261632-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261632-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD1KCM_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012. ", "license": "proprietary" }, { @@ -79738,7 +79660,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261633-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261633-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30AFCE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AFCE data product uses two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), and one object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AFCE data product uses two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), and one object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", "license": "proprietary" }, { @@ -79751,7 +79673,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261638-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261638-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30AUNZCNMOCE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", "license": "proprietary" }, { @@ -79764,7 +79686,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261648-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261648-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30EUCEARUMECE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", "license": "proprietary" }, { @@ -79777,7 +79699,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261670-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261670-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30NACE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over North America for nominal year 2010 at 30 meter resolution (GFSAD30NACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30NACE data product uses a combination of the pixel-based supervised classifier, Random Forest (RF), and the object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30NACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over North America for nominal year 2010 at 30 meter resolution (GFSAD30NACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30NACE data product uses a combination of the pixel-based supervised classifier, Random Forest (RF), and the object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30NACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", "license": "proprietary" }, { @@ -79790,7 +79712,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261689-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261689-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30SAAFGIRCE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", "license": "proprietary" }, { @@ -79803,7 +79725,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261708-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261708-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30SACE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area.", "license": "proprietary" }, { @@ -79816,7 +79738,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261715-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763261715-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/GFSAD30SEACE_001", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area. ", "license": "proprietary" }, { @@ -80238,26 +80160,26 @@ { "id": "GLAH01_033", "title": "GLAS/ICESat L1A Global Altimetry Data (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH01_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH01_033", "description": "Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH01_033", "title": "GLAS/ICESat L1A Global Altimetry Data (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH01_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH01_033", "description": "Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product.", "license": "proprietary" }, @@ -80290,52 +80212,52 @@ { "id": "GLAH03_033", "title": "GLAS/ICESat L1A Global Engineering Data (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH03_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH03_033", "description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.", "license": "proprietary" }, { "id": "GLAH03_033", "title": "GLAS/ICESat L1A Global Engineering Data (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH03_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH03_033", "description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.", "license": "proprietary" }, { "id": "GLAH04_033", "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH04_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH04_033", "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.", "license": "proprietary" }, { "id": "GLAH04_033", "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH04_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH04_033", "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.", "license": "proprietary" }, @@ -80394,78 +80316,78 @@ { "id": "GLAH07_033", "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH07_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH07_033", "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH07_033", "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH07_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH07_033", "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH08_033", "title": "GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH08_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH08_033", "description": "GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH08_033", "title": "GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH08_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH08_033", "description": "GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH09_033", "title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH09_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH09_033", "description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH09_033", "title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH09_033", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH09_033", "description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.", "license": "proprietary" }, @@ -80550,26 +80472,26 @@ { "id": "GLAH13_034", "title": "GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH13_034", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH13_034", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "license": "proprietary" }, { "id": "GLAH13_034", "title": "GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2003-02-20", "end_date": "2009-10-11", "bbox": "-180, -86, 180, 86", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH13_034", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH13_034", "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.", "license": "proprietary" }, @@ -86176,6 +86098,19 @@ "description": "The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 44-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an \u2018as available\u2019 basis.", "license": "proprietary" }, + { + "id": "GlobFireCarbon_1", + "title": "Global Fire carbon emissions from CMS-Flux inversions assimilating atmospheric carbon monoxide observations", + "catalog": "GES_DISC STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2023-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3278574317-GES_DISC.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3278574317-GES_DISC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/GlobFireCarbon_1", + "description": "This dataset provides the Carbon Flux for Fires. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.", + "license": "proprietary" + }, { "id": "Global_Biomass_1950-2010_1296_1", "title": "Global 1-degree Maps of Forest Area, Carbon Stocks, and Biomass, 1950-2010", @@ -87476,6 +87411,32 @@ "description": "The Australian Antarctic Division identified areas that required hydrographic surveying. (See map available in the download at \\Plans and Instructions\\HPS Supplied Data\\davis_plan_2019_2020 version 5.1.pdf and a shapefile of the identified areas at FSD\\ArcGIS\\Pink V2\\AOI_Unproject_wgs84.shp) A team from the Maritime Geospatial Warfare Unit, of the Australian Hydrographic Service, was at Davis in early February 2020. Single beam and side scanning survey data was collected on the water, beach profiles collected and rock data. Single beam and side scanning survey data Areas A, D, F, H, I, J and K were ice free. Area J was further broken down into four areas, J1, J2, J3 and J4. Areas A, D and F were thoroughly surveyed with 10m mainline spacing with 20m X-line spacing. Areas I, J3 and J4 were surveyed but due to time constraints were surveyed at approximately 40m line spacing to provide 200% sea floor coverage with the SSS to detect any features dangerous to navigation with one shoal detected in area I which is mentioned in Section I. Area H was too shallow to survey at any other time except high tide and it was decided to focus on other areas as the survey of this area would not value add to the required results of the survey. Area J1, J2 and K were not surveyed due to time constraints. RTK corrections or access to the CORS network couldn't be made to the CEESCOPE survey system. Instead positioning during the survey was recorded exclusively with the NovaTel GNSS 850 Antenna. No post processing was conducted. The team wasn't able to determine why the CEESCOPE was unable to connect to the CORS network or Base Station to gain RTK corrections, despite considerable effort spent problem solving and conducting a number of trials. Tide data collected was applied to the data and all tidal information is explained in section F of the report. A map showing the surveyed areas can be found in the report. Raw data in caris format is available from the Australian Hydrographic Office (AHO). Sounding data, stored as a shapefile, is available as a download file. Beach profiles Sites were also surveyed with 5m line spacing to maximise seafloor coverage, at 5 beach locations, 4 in area A and 1 in Area F. ArcGIS projects and PDF documents displaying the depth data and significant rocks are included in the download. Please note the ArcGIS projects do not include the AHO chart, due to distribution restrictions on digital charts. It is included in the PDF documents. These documents refer to images taken from the survey boat and spreadsheets displaying gradients data. Rock data A shapefile recording conspicuous rocks as well as photographs is available for downloading. Bench mark positions were reclaimed using Trimble R10 and post processed with AUSPOS. Abbreviations used in the download directories ROS = Report of Survey, FSD = Final Survey Data A detailed report can be found at /ROS/ Projection\u2026\u2026..\u2026...\u2026...\u2026\u2026\u2026\u2026.\u2026.\u2026\u2026..Universal Transverse Mercator (UTM) Zone 43 South Horizontal Datum\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026World Geodetic System 1984 (WGS84) Vertical Datum\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.....Approximated Lowest Astronomical Tide (LAT) Sounding Depths.\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Metres (m) Survey Date\u2026\u2026\u2026\u2026\u2026\u2026..\u2026\u2026\u2026\u2026\u2026\u2026\u2026.6th - 18th Feb 2020 Bathymetric Accuracy Horizontal\u2026\u2026\u2026\u2026\u2026\u00b1 0.8m Bathymetric Accuracy Vertical\u2026\u2026\u2026\u2026\u2026\u2026\u00b10.46m Sounding Density\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..2m Surface Chart Reference\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026AUS 451, 602\u200b ITRF 2014 and GRS80 were utilised for static observations of bench marks and levelling to the tide pole for establishment of approximate LAT. Hypack v19.1.11.0 which was used to gather all bathymetric data does not have the option to use the ITRF datum and the WGS84 Datum was used.", "license": "proprietary" }, + { + "id": "HICO_L0_1", + "title": "ISS HICO Level-0 Raw Science Data, version 1", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2009-09-25", + "end_date": "2014-09-13", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327154959-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327154959-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/HICO_L0_1", + "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", + "license": "proprietary" + }, + { + "id": "HICO_L1_2", + "title": "ISS HICO Level-1B Data, version 2", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2009-09-25", + "end_date": "2014-09-13", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327154985-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327154985-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/HICO_L1_2", + "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", + "license": "proprietary" + }, { "id": "HICO_L1_2", "title": "ISS Hyperspectral Imager for the Coastal Ocean (HICO) L1 Full-Resolution Calibrated Science Data", @@ -87489,6 +87450,19 @@ "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", "license": "proprietary" }, + { + "id": "HICO_L2_OC_2022.0", + "title": "ISS HICO Level-2 Regional Ocean Color (OC) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2009-09-25", + "end_date": "2014-09-13", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327155006-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327155006-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/HICO_L2_OC_2022.0", + "description": "The Hyperspectral Imager for the Coastal Ocean (HICO\u2122) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world.", + "license": "proprietary" + }, { "id": "HIC_NMOrthos_1", "title": "Heard Island Coastal Orthophotos derived from Non-Metric Photography", @@ -151462,19 +151436,6 @@ "description": "This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.", "license": "proprietary" }, - { - "id": "OMCLDRR_003", - "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC", - "catalog": "GES_DISC STAC Catalog", - "state_date": "2004-10-01", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMCLDRR_003", - "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.", - "license": "proprietary" - }, { "id": "OMCLDRR_003", "title": "OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT", @@ -151488,6 +151449,19 @@ "description": "The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/", "license": "proprietary" }, + { + "id": "OMCLDRR_003", + "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC", + "catalog": "GES_DISC STAC Catalog", + "state_date": "2004-10-01", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMCLDRR_003", + "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.", + "license": "proprietary" + }, { "id": "OMCLDRR_004", "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC", @@ -152645,19 +152619,6 @@ "description": "The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes.", "license": "proprietary" }, - { - "id": "OMTO3e_003", - "title": "OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT", - "catalog": "OMINRT STAC Catalog", - "state_date": "2004-07-15", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMTO3e_003", - "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) .", - "license": "proprietary" - }, { "id": "OMTO3e_003", "title": "OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC", @@ -152671,6 +152632,19 @@ "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.", "license": "proprietary" }, + { + "id": "OMTO3e_003", + "title": "OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT", + "catalog": "OMINRT STAC Catalog", + "state_date": "2004-07-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMTO3e_003", + "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) .", + "license": "proprietary" + }, { "id": "OMUANC_004", "title": "Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUANC) at GES DISC", @@ -153646,97 +153620,6 @@ "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. 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", "license": "proprietary" }, - { - "id": "PACE_OCI_L0_DARK_1", - "title": "PACE OCI Level-0 Dark Data, version 1", - "catalog": "OB_CLOUD STAC Catalog", - "state_date": "2024-02-08", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2804798299-OB_CLOUD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2804798299-OB_CLOUD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/PACE_OCI_L0_DARK_1", - "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", - "license": "proprietary" - }, { "id": "PACE_OCI_L0_DIAG_1", "title": "PACE OCI Level-0 Diagnostic/Calibaration Data, version 1", @@ -153854,45 +153698,6 @@ "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", "license": "proprietary" }, - { - "id": "PACE_OCI_L0_LIN_1", - "title": "PACE OCI Level-0 Linearity Calibration Data, version 1", - "catalog": "OB_CLOUD STAC Catalog", - "state_date": "2024-02-08", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2804798304-OB_CLOUD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2804798304-OB_CLOUD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/PACE_OCI_L0_LIN_1", - "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. 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As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", - "license": "proprietary" - }, - { - "id": "PACE_OCI_L3M_RRS_2.0", - "title": "PACE OCI Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2.0", - "catalog": "OB_CLOUD STAC Catalog", - "state_date": "2024-02-25", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3020924694-OB_CLOUD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3020924694-OB_CLOUD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/PACE_OCI_L3M_RRS_2.0", - "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", - "license": "proprietary" - }, { "id": "PACE_OCI_L3M_RRS_NRT_2.0", "title": "PACE OCI Level-3 Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version 2.0", @@ -155336,19 +153984,6 @@ "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", "license": "proprietary" }, - { - "id": "PACE_OCI_L3M_SFREFL_2.0", - "title": "PACE OCI Level-3 Global Mapped Surface Reflectance Data, version 2.0", - "catalog": "OB_CLOUD STAC Catalog", - "state_date": "2024-02-25", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3020924782-OB_CLOUD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3020924782-OB_CLOUD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/PACE_OCI_L3M_SFREFL_2.0", - "description": "The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.", - "license": "proprietary" - }, { "id": "PACE_OCI_L3M_SFREFL_NRT_2.0", "title": "PACE OCI Level-3 Global Mapped Surface Reflectance - Near Real-time (NRT) Data, version 2.0", @@ -168651,52 +167286,52 @@ { "id": "SPL2SMAP_003", "title": "SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_003", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL2SMAP_003", "title": "SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_003", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL2SMAP_S_003", "title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -60, 180, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "license": "proprietary" }, { "id": "SPL2SMAP_S_003", "title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -60, 180, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "license": "proprietary" }, @@ -168781,26 +167416,26 @@ { "id": "SPL2SMP_E_006", "title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "license": "proprietary" }, { "id": "SPL2SMP_E_006", "title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "license": "proprietary" }, @@ -168950,26 +167585,26 @@ { "id": "SPL3SMA_003", "title": "SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMA_003", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL3SMA_003", "title": "SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMA_003", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, @@ -168989,26 +167624,26 @@ { "id": "SPL3SMP_009", "title": "SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMP_009", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL3SMP_009", "title": "SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMP_009", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, @@ -169106,26 +167741,26 @@ { "id": "SPL4SMGP_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMGP_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, @@ -169932,7 +168567,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268442-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268442-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/SRTMGL3S_003", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) sub-sampled product. The 3 arc second data was derived from the 1 arc second using sampling methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTMGL3 data were sub-sampled from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL3S.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (http://dx.doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM). The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) sub-sampled product. The 3 arc second data was derived from the 1 arc second using sampling methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTMGL3 data were sub-sampled from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL3S.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (http://dx.doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM). The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", "license": "proprietary" }, { @@ -169997,7 +168632,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268443-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268443-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/SRTMIMGM_003", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) combined (merged) image data product. (See User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.2.) The combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) combined (merged) image data product. (See User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.2.) The combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", "license": "proprietary" }, { @@ -170010,7 +168645,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268444-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268444-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/SRTMIMGR_003", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects)) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) swath (raw) image data product. (See [User Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.1) The SRTM swath image data set consists of radar image files containing brightness values, as well as quality assurance (incidence angle) files for each of four overlapping sub-swaths that passes through a 1 degree by 1 degree tile. Data from each sub-swath is included as a separate file. Some files may contain only partial data; however, every image pixel acquired by SRTM is included in this data set. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) swath (raw) image data product. (See [User Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.1) The SRTM swath image data set consists of radar image files containing brightness values, as well as quality assurance (incidence angle) files for each of four overlapping sub-swaths that passes through a 1 degree by 1 degree tile. Data from each sub-swath is included as a separate file. Some files may contain only partial data; however, every image pixel acquired by SRTM is included in this data set. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).", "license": "proprietary" }, { @@ -170023,7 +168658,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268445-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268445-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/SRTMSWBD_003", - "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. ", + "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. ", "license": "proprietary" }, { @@ -196530,7 +195165,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268446-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268446-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VIP01_004", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.", "license": "proprietary" }, { @@ -196543,7 +195178,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268449-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268449-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VIP07_004", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP07 VI data product is a composite of seven daily images with 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP07 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP07 VI data product is a composite of seven daily images with 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP07 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.", "license": "proprietary" }, { @@ -196556,7 +195191,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268450-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268450-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VIP15_004", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP15 VI data product is provided twice monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP15 VI product contains 12 Science Datasets (SDS) which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. The VIP15 dataset includes two composites per month. The first composite is generated from day 1 to 15, and the second composite includes the remaining days of the month. This dataset consists of 24 files per year.", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP15 VI data product is provided twice monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP15 VI product contains 12 Science Datasets (SDS) which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. The VIP15 dataset includes two composites per month. The first composite is generated from day 1 to 15, and the second composite includes the remaining days of the month. This dataset consists of 24 files per year.", "license": "proprietary" }, { @@ -196569,7 +195204,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268451-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268451-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VIP30_004", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.", "license": "proprietary" }, { @@ -196582,7 +195217,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268453-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268453-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VIPPHEN_EVI2_004", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.", "license": "proprietary" }, { @@ -196595,7 +195230,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268456-LPCLOUD.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763268456-LPCLOUD.html", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/VIPPHEN_NDVI_004", - "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 \u2013 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data. ", + "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 \u2013 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data. ", "license": "proprietary" }, { @@ -202500,6 +201135,19 @@ "description": "This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Sch\u00fcrholt, K., Kowalski, J., L\u00f6we, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics", "license": "proprietary" }, + { + "id": "a0782135bcd04d77a1dae4aa71fba47c_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360338-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360338-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/a0782135bcd04d77a1dae4aa71fba47c_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", + "license": "proprietary" + }, { "id": "a0d9764a3068439b997c42928ef739d2_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2", @@ -202513,6 +201161,19 @@ "description": "This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010. It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage. It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity. The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH).", "license": "proprietary" }, + { + "id": "a13994c5-8d10-4627-90b8-60077ab5de40_NA", + "title": "EnMAP HSI - Level 0 / Quicklook Images - Global", + "catalog": "FEDEO STAC Catalog", + "state_date": "2022-04-27", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3326864967-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3326864967-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/a13994c5-8d10-4627-90b8-60077ab5de40_NA", + "description": "The EnMAP HSI L0 Quicklooks collection contains the VNIR and SWIR quicklook images as well as the quality masks for haze, cloud, or snow; based on the latest atmospheric correction methodology of the land processor. It allows users to get an overview which L0 data has been acquired and archived since the operational start of the EnMAP mission and which data is potentially available for on-demand processing into higher level products with specific processing parameters via the EOWEB-GeoPortal. The database is constantly updated with newly acquired L0 data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth\u2019s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making. For more information, please see the mission website: https://www.enmap.org/mission/", + "license": "proprietary" + }, { "id": "a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA", "title": "Resourcesat-2 - Multispectral Images (LISS-IV) - Europe, Multispectral Mode", @@ -202526,19 +201187,6 @@ "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale.", "license": "proprietary" }, - { - "id": "a386504aa8ae492f9f2af04c109346e9_NA", - "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): A database of coastal sea level anomalies and associated trends from Jason satellite altimetry from 2002 to 2018", - "catalog": "FEDEO STAC Catalog", - "state_date": "2002-01-15", - "end_date": "2018-05-30", - "bbox": "-30, -45, 160, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142874-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142874-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/a386504aa8ae492f9f2af04c109346e9_NA", - "description": "This dataset contains 17-year-long (June 2002 to May 2018 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of six regions: Mediterranean Sea, Northeast Atlantic, West Africa, North Indian Ocean, Southeast Asia and Australia. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from the ESA SL_cci+ v1.1 dataset of coastal sea level anomalies (also available in the catalogue, DOI:10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005), which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called \u00e2\u0080\u0098retracking\u00e2\u0080\u0099) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series. This large amount of coastal sea level estimates has been further analysed to produce the present dataset: it consists in a selection of 429 portions of satellite tracks crossing land for which valid sea level time series are provided at monthly interval together with the associated sea level trends over the 17-year time span at each along-track 20-Hz point, from 20 km offshore to the coast.The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset has a DOI: https://doi.org/10.17882/74354", - "license": "proprietary" - }, { "id": "a6efcb0868664248b9cb212aba44313d_NA", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2", @@ -202621,7 +201269,7 @@ "id": "aa09603e91b44f3cb1573c9dd415e8a8_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0", "catalog": "FEDEO STAC Catalog", - "state_date": "2002-09-30", + "state_date": "2002-10-01", "end_date": "2011-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142551-FEDEO.umm_json", @@ -202631,16 +201279,16 @@ "license": "proprietary" }, { - "id": "aab98144131244f58ce1b56e7342ff3e_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 4.2", + "id": "aa8268e2ca0e48d98aee372795722253_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00", "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", + "state_date": "2016-05-01", + "end_date": "2020-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142832-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142832-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/aab98144131244f58ce1b56e7342ff3e_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 4.2 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359664-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359664-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/aa8268e2ca0e48d98aee372795722253_NA", + "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "license": "proprietary" }, { @@ -203137,19 +201785,6 @@ "description": "The South African Bird Ringing Unit (SAFRING) administers bird ringing in southern Africa, supplying rings, ringing equipment and services to volunteer and professional ringers in South Africa and neighbouring countries. All ringing records are curated by SAFRING, which is an essential arm of the Animal Demography Unit. Contact is maintained by the SAFRING Project Coordinator with all ringers (banders in North American or Australian terminology). The Bird Ringing Scheme in South Africa was initiated in 1948, so 1998 saw the 50th anniversary of the scheme. During this period over 1.7 million birds of 852 species were ringed. There have been a total of 16 800 ring recoveries since the inception of the scheme. This gives an overall recovery rate for rings in southern Africa of marginally less than 1%, averaged across all species. This probability varies enormously across species.", "license": "proprietary" }, - { - "id": "aeae1a19608347f7b802691db6984343_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142920-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142920-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/aeae1a19608347f7b802691db6984343_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 4.2 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "aerial_casa_2010_11_1", "title": "Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11", @@ -203358,6 +201993,19 @@ "description": "Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.", "license": "proprietary" }, + { + "id": "af60720c1e404a9e9d2c145d2b2ead4e_NA", + "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4", + "catalog": "FEDEO STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2020-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359400-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359400-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/af60720c1e404a9e9d2c145d2b2ead4e_NA", + "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR instrument and JAXA\u00e2\u0080\u0099s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.", + "license": "proprietary" + }, { "id": "afforestation-stillberg_1.0", "title": "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland", @@ -205152,6 +203800,19 @@ "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the Swansea University (SU) algorithm, version 4.3. It covers the period from 2002 - 2012.For further details about these data products please see the linked documentation.", "license": "proprietary" }, + { + "id": "b0ec72a28b6a4829a33ed9adc215d5bc_NA", + "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-09-04", + "end_date": "2022-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360215-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327360215-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/b0ec72a28b6a4829a33ed9adc215d5bc_NA", + "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", + "license": "proprietary" + }, { "id": "b1bd715112ca43ab948226d11d72b85e_NA", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1", @@ -205231,16 +203892,16 @@ "license": "proprietary" }, { - "id": "b64b1a0ad7874fb39791e99c57b944bc_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 3.1", + "id": "b54d5f1c08594879a05929ce09951c56_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00", "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", + "state_date": "2018-12-01", + "end_date": "2020-12-31", "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142812-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142812-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/b64b1a0ad7874fb39791e99c57b944bc_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327358997-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327358997-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/b54d5f1c08594879a05929ce09951c56_NA", + "description": "This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage runs from December 2018 to December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "license": "proprietary" }, { @@ -205516,6 +204177,19 @@ "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.", "license": "proprietary" }, + { + "id": "bf535053562141c6bb7ad831f5998d77_NA", + "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01", + "catalog": "FEDEO STAC Catalog", + "state_date": "2010-01-01", + "end_date": "2021-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359101-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359101-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/bf535053562141c6bb7ad831f5998d77_NA", + "description": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat\u00e2\u0080\u0099s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA\u00e2\u0080\u0099s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.This version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude.", + "license": "proprietary" + }, { "id": "bf5eae2a052848aab2abf93d96e7e9aa_NA", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ensemble product), Version 2.6", @@ -206846,7 +205520,7 @@ "id": "c65ce27928f34ebd92224c451c2a8bed_NA", "title": "ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1", "catalog": "FEDEO STAC Catalog", - "state_date": "1991-08-31", + "state_date": "1991-09-01", "end_date": "2010-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143126-FEDEO.umm_json", @@ -207999,6 +206673,19 @@ "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. The data covers the period from 1995 - 2003.For further details about these data products please see the linked documentation.", "license": "proprietary" }, + { + "id": "d34330ce3f604e368c06d76de1987ce5_NA", + "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1997-01-01", + "end_date": "2021-12-31", + "bbox": "-180, 25, 180, 85", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359586-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359586-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/d34330ce3f604e368c06d76de1987ce5_NA", + "description": "This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30\u00c2\u00b0) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.", + "license": "proprietary" + }, { "id": "d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA", "title": "IRS-1D - Multispectral Images (LISS-III) - Europe", @@ -208025,32 +206712,6 @@ "description": "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/", "license": "proprietary" }, - { - "id": "d6d0d7b4cf3540448b4ddcaed2f54b81_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143153-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143153-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/d6d0d7b4cf3540448b4ddcaed2f54b81_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", - "license": "proprietary" - }, - { - "id": "d9df331e346f4a50b18bcf41a64b98c7_NA", - "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2019), version 1.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1982-01-01", - "end_date": "2019-06-30", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142699-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142699-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/d9df331e346f4a50b18bcf41a64b98c7_NA", - "description": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1982-2019. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00c3\u00a4m\u00c3\u00a4ki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 \u00c2\u00b5m (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 \u00c2\u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years.", - "license": "proprietary" - }, { "id": "da2b8512312a4f14a928766f7f632d36_NA", "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ORAC algorithm), Version 4.01", @@ -208545,19 +207206,6 @@ "description": "Bathymetric Contours and height range polygons of approaches to Davis Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings.", "license": "proprietary" }, - { - "id": "db32212d86f9431dae67076dd122565e_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 4.2", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142706-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142706-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/db32212d86f9431dae67076dd122565e_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", - "license": "proprietary" - }, { "id": "dc8ammr_1", "title": "CAMEX-3 DC-8 Airborne Multichannel Microwave Radiometer (AMMR) V1", @@ -208714,19 +207362,6 @@ "description": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "license": "proprietary" }, - { - "id": "de75072edfca44bfaaec0ed171d86bde_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142555-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142555-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/de75072edfca44bfaaec0ed171d86bde_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "de883c15-85f6-435a-b5aa-3f6468ba919a_1", "title": "Annual methane emission from livestock (KG./SQ.KM)", @@ -209208,32 +207843,6 @@ "description": "This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.", "license": "proprietary" }, - { - "id": "e2c223cdcb4844f9a1ffe9759b61eaf4_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143024-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143024-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/e2c223cdcb4844f9a1ffe9759b61eaf4_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", - "license": "proprietary" - }, - { - "id": "e2f9d8f61a02431997361a8827eaf558_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142848-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142848-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/e2f9d8f61a02431997361a8827eaf558_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)", - "license": "proprietary" - }, { "id": "e3dbdc32f7b6476e949d52d8d3990205_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae Glacier for 2015-2017 from Sentinel-1 data, v1.1", @@ -209264,8 +207873,8 @@ "id": "e493802d83c846c8b76f817866fb74cc_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0", "catalog": "FEDEO STAC Catalog", - "state_date": "2002-09-30", - "end_date": "2012-04-07", + "state_date": "2002-10-01", + "end_date": "2012-04-08", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142824-FEDEO.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142824-FEDEO.html", @@ -209290,7 +207899,7 @@ "id": "e61704b00267405082fbd41bb710dd74_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-03-31", + "state_date": "2009-04-01", "end_date": "2015-12-30", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143069-FEDEO.umm_json", @@ -209351,32 +207960,6 @@ "description": "This dataset consists of time series of surface reflectance from the MERIS instrument on the ENVISAT satellite, produced as part of the ESA Land Cover Climate Change Initiative (CCI) project. The time series are a temporal syntheses obtained over a 7-day compositing period, and encompass 13 of the 15 MERIS spectral channels (not including bands 11 and 15). The spatial resolution is 300m for the Full Resolution (FR) data and 1000m for the Reduced Resolution (RR) data.Given the amount and size of the MERIS surface reflectance archive (10 To), the Land Cover CCI team make the data available on request, through your own disks. Please contact contact@esa-landcover-cci.org", "license": "proprietary" }, - { - "id": "e94f2810c0794175b834153a71ac3182_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142989-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142989-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/e94f2810c0794175b834153a71ac3182_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).", - "license": "proprietary" - }, - { - "id": "e9f82908fd9c48138b31e5cfaa6d692b_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142759-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142759-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/e9f82908fd9c48138b31e5cfaa6d692b_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites) covering the period 1997 - 2020. Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "eMASL1B_1", "title": "Enhanced MODIS Airborne Simulator (eMAS) Calibrated, Geolocated Radiances L1B 50m Data", @@ -209585,19 +208168,6 @@ "description": "The data set contains the data used in the publication \"On snow stability interpretation of Extended Column Test results\" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences.", "license": "proprietary" }, - { - "id": "edaa7e7324e849f683d3726088a0c7bd_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 3.1", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-03", - "end_date": "2016-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142506-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142506-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/edaa7e7324e849f683d3726088a0c7bd_NA", - "description": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, this the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)", - "license": "proprietary" - }, { "id": "edc_landcover_xdeg_930_1", "title": "ISLSCP II IGBP DISCover and SiB Land Cover, 1992-1993", @@ -209702,19 +208272,6 @@ "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data.", "license": "proprietary" }, - { - "id": "ef8eb5ff84994f2ca416dbb2df7f72c7_NA", - "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2019), version 1.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "2000-02-24", - "end_date": "2019-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143047-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143047-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/ef8eb5ff84994f2ca416dbb2df7f72c7_NA", - "description": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 \u00e2\u0080\u0093 2019. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00c3\u00a4m\u00c3\u00a4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00c3\u00a4m\u00c3\u00a4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 \u00c2\u00b5m, and an emissive band centred at about 11 \u00c2\u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.", - "license": "proprietary" - }, { "id": "effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0", "title": "Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice", @@ -210820,6 +209377,19 @@ "description": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717\u00e2\u0080\u0093739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "license": "proprietary" }, + { + "id": "f1445bde2f1249c99bb5a59b71e9a9d7_NA", + "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00", + "catalog": "FEDEO STAC Catalog", + "state_date": "1995-08-01", + "end_date": "2003-06-22", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327358896-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327358896-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/f1445bde2f1249c99bb5a59b71e9a9d7_NA", + "description": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India \u00e2\u0080\u0093 further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01\u00c2\u00b0 longitude and 0.01\u00c2\u00b0 latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", + "license": "proprietary" + }, { "id": "f17f146a31b14dfd960cde0874236ee5_NA", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1", @@ -210859,19 +209429,6 @@ "description": "This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for September.", "license": "proprietary" }, - { - "id": "f30495d4425f46c489765a2f84dd6862_NA", - "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 5.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1997-09-04", - "end_date": "2020-12-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142951-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142951-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/f30495d4425f46c489765a2f84dd6862_NA", - "description": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).", - "license": "proprietary" - }, { "id": "f31e8e988c4144bebe13892b53d08e42_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the 79Fjord Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1", @@ -210911,6 +209468,19 @@ "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for OSIRIS in 2008.", "license": "proprietary" }, + { + "id": "f4654030223445b0bac63a23aaa60620_NA", + "title": "ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0", + "catalog": "FEDEO STAC Catalog", + "state_date": "1982-01-01", + "end_date": "2019-06-30", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359735-FEDEO.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3327359735-FEDEO.html", + "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/f4654030223445b0bac63a23aaa60620_NA", + "description": "This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05\u00c2\u00b0 grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The CryoClim FSC time series provides daily products for the period 1982 \u00e2\u0080\u0093 2019. The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.For the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.The algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008).", + "license": "proprietary" + }, { "id": "f4c34f4f0f1d4d0da06d771f6972f180_NA", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0", @@ -210941,7 +209511,7 @@ "id": "f9154243fd8744bdaf2a59c39033e659_NA", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0", "catalog": "FEDEO STAC Catalog", - "state_date": "2009-04-17", + "state_date": "2009-04-18", "end_date": "2015-12-31", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143252-FEDEO.umm_json", @@ -210976,19 +209546,6 @@ "description": "The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA\u2019s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-days maps.", "license": "proprietary" }, - { - "id": "fa20aaa2060e40cabf5fedce7a9716d0_NA", - "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 \u00e2\u0080\u0093 2018), version 1.0", - "catalog": "FEDEO STAC Catalog", - "state_date": "1979-01-06", - "end_date": "2018-05-31", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142587-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2548142587-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/fa20aaa2060e40cabf5fedce7a9716d0_NA", - "description": "Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces; in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979 to 2018. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration\u00e2\u0080\u0099s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.The dataset was aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2018, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.", - "license": "proprietary" - }, { "id": "fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA", "title": "TerraSAR-X - Spotlight Images (TerraSAR-X Spotlight)", @@ -211158,19 +209715,6 @@ "description": "The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; W\u00c3\u00bcrzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.", "license": "proprietary" }, - { - "id": "fb4b4be0-a4a3-4dcd-b381-bacde381d3eb_NA", - "title": "MERIS - Gap Free Leaf Area Index (LAI) - Global", - "catalog": "FEDEO STAC Catalog", - "state_date": "2003-01-01", - "end_date": "2011-01-31", - "bbox": "-180, -60, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458057-FEDEO.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2207458057-FEDEO.html", - "href": "https://cmr.earthdata.nasa.gov/stac/FEDEO/collections/fb4b4be0-a4a3-4dcd-b381-bacde381d3eb_NA", - "description": "This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data.", - "license": "proprietary" - }, { "id": "fbfae06e787b4fefb4b03cba2fd04bc3_NA", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0", @@ -211227,7 +209771,7 @@ "id": "ff4bfe39b7fe42fc993341d3cebdabb5_NA", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5", "catalog": "FEDEO STAC Catalog", - "state_date": "2002-03-31", + "state_date": "2002-04-01", "end_date": "2016-06-30", "bbox": "-80, 60, -10, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548143051-FEDEO.umm_json", diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index 92f030b..f13d48c 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -1,18 +1,18 @@ id title catalog state_date end_date bbox url description license 001aab54-295d-4fb3-b269-748b8e0b9a04_NA Resourcesat-2 - Panchromatic Images (LISS-IV) - Europe, Mono Mode FEDEO STAC Catalog 2004-01-18 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458048-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary 004fd44ff5124174ad3c03dd2c67d548_NA ESA Cloud Climate Change Initiative (Cloud_cci): AVHRR-PM monthly gridded cloud properties, version 3.0 FEDEO STAC Catalog 1982-01-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143187-FEDEO.umm_json The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements.This dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003. proprietary -016f577b631a429a8558796a74983154_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143519-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 5.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary +024292dcda5d42ceb326850f89f8b40d_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359340-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 0470e96f2d8245549ef2ba81842cdfd8_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change grid from SARAL-AltiKa for 2013-2017, v0.1 FEDEO STAC Catalog 2013-03-31 2017-03-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142935-FEDEO.umm_json This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from SARAL-AltiKa for 2013-2017. This new experimental product of surface elevation change is based on data from the AltiKa-instrument onboard the France (CNES)/Indian (ISRO) SARAL satellite. The AktiKa altimeter utilizes Ka-band radar signals, which have less penetration in the upper snow. However, the surface slope and roughness has an imprint in the derived signal and the new product is only available for the flatter central parts of the Greenland ice sheet.The corresponding SEC grid from Cryosat-2 is included for comparison. The algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012. The approach used here corresponds to Least Squares Method (LSM) 5 described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 20 km. proprietary 04bc222136f7429eb04d3eb3543ef3e8_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01 FEDEO STAC Catalog 1995-06-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143224-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ORAC algorithm, version 4.01. It covers the period from 1995-2003For further details about these data products please see the linked documentation. proprietary 057dd6c36f0741d3b97f9eee688b7835_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2 FEDEO STAC Catalog 1978-11-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143472-FEDEO.umm_json The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 proprietary 065f6040ef08485db989cbd89d536167_NA ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 1.1 FEDEO STAC Catalog 2016-01-01 2016-12-31 -20, -35, 55, 25 https://cmr.earthdata.nasa.gov/search/concepts/C2548142692-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset. proprietary -07eeca6888c645d89a7ef91de0290eca_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142626-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary +0875b4675f1e46ebadb526e0b95505c5_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359430-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary 0944645 Age and Composition of the East Antarctic Shield SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214605206-SCIOPS.umm_json We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis. Petrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the ‘backside’ of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids. proprietary 0ac98747-eb94-4c9f-aef8-56f9d3a04740 Earthquake Risk-Annual Average Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848506-CEOS_EXTRA.umm_json The map (risk map) presents the results of earthquake annual average losses (AAL) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (AAL-absolute value) and millar (AAL/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary 0b23b3c771db4fff8958196432d978cb_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release) FEDEO STAC Catalog 1995-09-02 1996-03-29 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143114-FEDEO.umm_json This dataset contains ice velocities for the Greenland margin for winter 1995-1996, which have been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data were derived from intensity-tracking of ERS-2 data acquired between 03-09-1995 and 29-03-1996. It provides components of the ice velocity and the magnitude of the velocity.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing. For further information please see the product user guide.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product. proprietary 0d2260ad4e2c42b6b14fe5b3308f5eaa_NA ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01 FEDEO STAC Catalog 1996-03-31 2011-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143081-FEDEO.umm_json This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI). The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011. proprietary 0e289294f2c141bca545cd9d7fcb62d0_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-06-09 2017-03-21 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143491-FEDEO.umm_json This dataset contains a time series of ice velocities for the Helheim Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between between June 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary -0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA DESIS - Hyperspectral Images - Global FEDEO STAC Catalog 2018-08-30 -180, -52, 180, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2207458058-FEDEO.umm_json The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-13614/ proprietary +0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA DESIS - Hyperspectral Images - Global FEDEO STAC Catalog 2018-08-30 -180, -52, 180, 56 https://cmr.earthdata.nasa.gov/search/concepts/C2207458058-FEDEO.umm_json The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/de/eoc/forschung-transfer/projekte-und-missionen/desis proprietary 10-16904-10_1.0 DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.92665, 46.71291, 9.92665, 46.71291 https://cmr.earthdata.nasa.gov/search/concepts/C2789814554-ENVIDAT.umm_json This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface. The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015. Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals. proprietary 10-16904-19_1.0 DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland. ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.8523577, 46.7001529, 9.9359287, 46.7393031 https://cmr.earthdata.nasa.gov/search/concepts/C2789814574-ENVIDAT.umm_json "The data presented here corresponds to the publication ""A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation"" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains: * Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution. * ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability). * LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m). * meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015. * TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans. For more details about the simulation and observation data, see Gerber et al., 2017. __Publication__: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres." proprietary 10-16904-1_7 WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.809568, 46.829598, 9.809568, 46.829598 https://cmr.earthdata.nasa.gov/search/concepts/C2789814541-ENVIDAT.umm_json Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site. __Publication__ Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015. proprietary @@ -165,18 +165,18 @@ id title catalog state_date end_date bbox url description license 10.7289/v5zs2th4_Not Applicable American Horseshoe Crab Abundance in the Northern Central Gulf of Mexico from 2012-05-21 to 2013-08-20 (NCEI Accession 0149391) NOAA_NCEI STAC Catalog 2012-05-21 2013-08-20 -88.75416, 30.2, -87.96343, 30.25214 https://cmr.earthdata.nasa.gov/search/concepts/C2089378911-NOAA_NCEI.umm_json This dataset contains sightings of American horseshoe crab, Limulus polyphemus, during shoreline surveys conducted in late spring and summer in 2012 and 2013. The study area was in the northern Gulf of Mexico extending from Fort Morgan peninsula of the Alabama coast west to Horn Island off the Mississippi coast, which covers a total distance from east to west of about 60 km. Live crabs, dead crabs, and molts are included. proprietary 10.7289/v5zs2tt5_Not Applicable Carbon dioxide, hydrographic and chemical data collected from profile discrete samples during the R/V Knorr cruise (EXPOCODE 316N20071003) in Davis Strait from 2007-10-03 to 2007-10-21 (NCEI Accession 0173248) NOAA_NCEI STAC Catalog 2007-10-03 2007-10-21 -63.3, 61.9, -52.2, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089378563-NOAA_NCEI.umm_json This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, dissolved inorganic carbon, total alkalinity other measurements obtained during the R/V Knorr cruise (EXPOCODE 316N20071003) in Davis Strait from 2007-10-03 to 2007-10-21. proprietary 10Be-Law-Dome-10-year-composite_1 High Resolution ice core 10Be records from Law Dome, Antarctica: 10-year composite (revised dating) AU_AADC STAC Catalog 1999-12-01 2009-12-31 112.8, -66.77, 112.8, -66.77 https://cmr.earthdata.nasa.gov/search/concepts/C1214305617-AU_AADC.umm_json This record comprises composite 10Be concentrations from three Law Dome ice cores (DSS0506-core, DSS0809-core and DSS0910-core). Sample dating is revised from that presented in Pedro et al., clim. Past 7, 707-721, 2011 by accounting for sub-seasonal variability in snow accumulation. The accumulation record was derived from the European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim). See Appendix 1 of Pedro et al., J. Geophys. Res. 116, D23120, 2011 for details of method. proprietary +1115d8946ba74c7f8a9fc3bfee5513a0_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00 FEDEO STAC Catalog 2002-07-25 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359280-FEDEO.umm_json This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary 1162_4_IPEV_FR Adult integument colour - MDO Alaska SCIOPS STAC Catalog 2004-05-01 146.333, 59.452, 146.333, 59.452 https://cmr.earthdata.nasa.gov/search/concepts/C1214598260-SCIOPS.umm_json - Spectrograms or pictures of gape, tongue, eye-ring and bill of each adult that was caught on the tower in Middleton island. - Colour data obtained from those spectrograms and pictures. proprietary 118cc853-52a2-46e2-a5be-40e1f58ab46d_1 HUMAN POPULATION AND ADMINISTRATIVE BOUNDARIES DATABASE FOR THE RUSSIAN FED. CEOS_EXTRA STAC Catalog 1970-01-01 -36, 41.19658, 180, 81.85193 https://cmr.earthdata.nasa.gov/search/concepts/C2232847474-CEOS_EXTRA.umm_json The Russian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. The database was prepared at UNEP/GRID-Geneva in collaboration with Denis Eckert (Centre National de la Recherche Scientifique / France) and the Centre Universitaire d'Ecologie Humaine/Université de Genève. BOUNDARY AND POPULATION DATA 2486 third-level administrative units (1863 raions, 325 gorods, 298 gorsoviets) digitized by D. Eckert have been matched to the national boundaries of the widely used Digital Chart of the World. 649 cities have been digitized from various sources and adjusted to the administrative map. Population figures of the administrative units refer to the 1993 official figures whereas urban data was compiled from heterogeneous sources and projected to the year 1995. INTERPOLATION The interpolation of the vectorial data to a population density raster grid at a resolution of 2.5 arc-minutes was carried out according to a model developed by Uwe Deichman for a previous work on Asia (UNEP/GRID-Geneva). The basic assumption upon which the model is based is that population densities are strongly correlated with accessibility. A complex transport network (more than 170'000 arcs) was used to distribute populations and densities to a raster grid. OUTPUTS - a vector dataset containing the 2846 administrative units fitted to the Digital Chart of the World used as template. Population figures of the 1993 official figures and subsequent projections to year 1995 are stored in the polygon attribute file. - a raster dataset of the interpolated and distributed population totals at a resolution of 2.5 arc-minutes - a raster dataset of the interpolated and distributed population densities at a resolution of 2.5 arc-minutes GIF images for display on the Web proprietary 11c5f6df1abc41968d0b28fe36393c9d_NA ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO STAC Catalog 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143004-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation. proprietary 12-hourly_interpolated_surface_position_from_buoys 12-Hourly Interpolated Surface Position from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-01 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.umm_json This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z. proprietary 12-hourly_interpolated_surface_velocity_from_buoys 12-Hourly Interpolated Surface Velocity from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-02 -180, 74, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.umm_json This data set contains 12-hourly interpolated surface velocity data from buoys. Point grid: Latitude 74N to 90N - 4 degree increment Longitude 0E to 320E - 20 and 40 degree increment. proprietary 12_hourly_interpolated_surface_air_pressure_from_buoys 12 Hourly Interpolated Surface Air Pressure from Buoys SCIOPS STAC Catalog 1979-01-01 2007-11-30 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600618-SCIOPS.umm_json Optimally interpolated atmospheric surface pressure over the Arctic Ocean Basin. Temporal format - twice daily (0Z and 12Z) Spatial format - 2 degree latitude x 10 degree longitude - latitude: 70 N - 90 N - longitude: 0 E - 350 E proprietary -12d6f4bdabe144d7836b0807e65aa0e2_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142978-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary 142052b9dc754f6da47a631e35ec4609_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): Time series of gridded Sea Level Anomalies (SLA), Version 2.0 FEDEO STAC Catalog 1993-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142503-FEDEO.umm_json As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a multi-satellite merged time series of monthly gridded Sea Level Anomalies (SLA) has been produced from satellite altimeter measurements. The Sea Level Anomaly grids have been calculated after merging the altimetry mission measurements together into monthly grids, with a spatial resolution of 0.25 degrees. This version of the product is Version 2.0. The following DOI can be used to reference the monthly Sea Level Anomaly product: DOI: 10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org proprietary 14c_of_soil_co2_from_ipy_itex_cross_site_comparison 14C of soil CO2 from IPY ITEX Cross Site Comparison SCIOPS STAC Catalog 2008-01-16 2008-01-21 -157.4, -36.9, 147.29, 71.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214602443-SCIOPS.umm_json Study sites: Toolik Lake Field Station Alaska, USA 68.63 N, 149.57 W; Atqasuk, Alaska USA 70.45 N, 157.40 W; Barrow, Alaska, USA 71.30 N, 156.67 W; Latnjajaure, Sweden 68.35 N, 18.50 E; Falls Creek, Australia: Site 2-unburned 36.90 S 147.29 E; Site 3-burned 36.89 S 147.28 E. Additional sites will be added summer 2008, but the exact sites are not finalized. Purpose: Collect soil CO2 for analysis of radiocarbon to evaluate the age of the carbon respired in controls and warmed plots from across the ITEX network. Treatments: control and ITEX OTC warming experiment (1994-2007). Design: 5 replicates of each treatment at dry site and moist site. Sampling frequency: Once per peak season. proprietary 159-96_03 Alkali basalt volcanism along a subduction-related magmatic arc: The case of Puyuhuapi Quaternary volcanic line, Southern Andes (44deg20minS) SCIOPS STAC Catalog 1998-02-01 1998-05-02 -72.36, -42.22, -72.31, -42.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214615254-SCIOPS.umm_json In Southern Chile, plate configuration is characterized by ridge-ridge-trench collision in correspondence of Taitao peninsula (Chile triple junction). The different converging rates of Nazca and Antarctic plates favored the formation of a forearc sliver (Chiloe block) limited to west by a dextral transcurrent fault system, Known as Liquine-Ofqui fault system (LOFS). During the Quatenary time, a series of monogenetic volcanic centers, as Puyuhuapi volcanic centers (PVC), formed along the LOFS. The PVC lavas have a primitive character; two groups and can be distinguihed. Group-1 rocks show a K-AlKaline affinity and are nepheline normative with olivine and plagioclase as dominant phases. Group-2 lavas have Na-affinity with olivine and hyperstene in the norm; olivine is the most abundant mineral phase. In contrast with overall alkaline affinity of PVC, the products from the neighboring central composite volcanoes are generally calcalkaline with the exception of the lavas from Maca Volcano, which show tholeiitic affinity. proprietary -159649796f2943689a836999016188f0_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142794-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 3.1 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary 16920eb2-2eaf-4629-8337-3626e70e4770 Africa - Photovoltaic Solar Electricity Potential SCIOPS STAC Catalog 2001-01-01 2008-12-31 -24.960938, -35.859375, 61.523438, 46.40625 https://cmr.earthdata.nasa.gov/search/concepts/C1214604070-SCIOPS.umm_json The map displays the quantity of energy that reached equator-oriented photovoltaic modules that are optimally-inclined to maximise yearly electricity yields. This map is computed from observations made by meteorological satellites. Click on map to enlarge. If you use this map, mention this copyright please: PVGIS copyright European Commission 2001-2008 and HelioClim-1 copyright Mines ParisTech / Armines 2001-2008. proprietary +16c633f003ef4d8481420f052356c11c_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00 FEDEO STAC Catalog 1995-08-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359482-FEDEO.umm_json This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary 1747-ESDD Alaskan Geologic Photography Collection from USGS CEOS_EXTRA STAC Catalog 1898-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549077-CEOS_EXTRA.umm_json Collection of Alaskan photography taken throughout the state by explorers and field geologists. Subjects include geology and geologic phenomenon, earthquake damage, landscapes and people. Collection contains both photographs and slides. Library indexed by subject, locality, and year. Written requests accepted. Give as much information as possible to ensure successful search. Lists of many of the photographs submitted by Alaskan geologists are held in the technical data unit of the USGS branch of Alaskan geology in Anchorage, Alaska. proprietary 1751a072-d00b-42e8-8c7d-dc078f2ee40a Cyclones Winds - Hazard, Wind Speed 250RP CEOS_EXTRA STAC Catalog 1970-01-01 -180, -55, 179.7721, 59.768925 https://cmr.earthdata.nasa.gov/search/concepts/C2232848078-CEOS_EXTRA.umm_json "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 250 years return period. It was generated by integration of the intensity values contained in the files ""Wind_Atlantic.AME"", ""Wind_EastPacific.AME"", ""Wind_NorthIndian.AME"", ""Wind_SudIndian.AME"", ""Wind_SudPacific.AME"" and ""Wind_WestPacific.AME""." proprietary 17767027aa484505b7b732aee6619c74_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim glacier from ERS-1, ERS-2 and Envisat data for 1996-2010, v1.1 FEDEO STAC Catalog 1996-05-28 2010-02-26 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143018-FEDEO.umm_json This dataset contains a time series of ice velocities for the Helheim glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 29/05/1996 and 26/2/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle of 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary @@ -246,7 +246,6 @@ id title catalog state_date end_date bbox url description license 1dd4c30a78d84e628cd8097bae3148fd_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Storstroemmen Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-01-24 2017-03-22 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142890-FEDEO.umm_json This dataset contains a time series of ice velocities for the Storstromemmen glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 24/1/2015 and 22/03/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary 1e3fcdc14e2246c69fc54f0e1fe7a6ca_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Helheim Glacier between 2017-05-01 and 2017-08-29, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-04-30 2017-08-29 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143190-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Helheim Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-08-29. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway. proprietary 1f1940e4-ec31-4925-8fa8-942a59531888_NA IRS-1C - Multispectral Images (LISS-III) - Europe FEDEO STAC Catalog 1996-01-19 2004-01-31 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207457984-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products. proprietary -1f84f9465e65416ca45cd20bc415b522_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142553-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 200001010_1 Aurora Australis Voyage 1 2000-01 Underway Data AU_AADC STAC Catalog 2000-10-03 2000-11-19 60, -70, 150, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214305506-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage 1 2000-01. This voyage departed Hobart for Port Arthur to carry out calibrations prior to travelling on to Davis, Mawson, Heard Island, the McDonald Islands and then Fremantle. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL given below). For further information, see the Marine Science Support Data Quality Report at the Related URL below. proprietary 200001040_1 Aurora Australis Voyage 4 2000-2001 Underway Data AU_AADC STAC Catalog 2000-11-20 2000-12-28 60, -70, 150, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214305535-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage 4 2000-01. This voyage departed Fremantle and visited Heard Island, Mawson, Davis and Sansom Island prior to returning to Hobart. Underway (meteorological, fluorometer and thermosalinograph) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section. proprietary 200001060_1 Aurora Australis Voyage 6 2000-2001 Underway Data AU_AADC STAC Catalog 2001-01-01 2001-03-09 60, -70, 150, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214305557-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage 6 2000-01. This was a marine science voyage that visited Mawson, Casey and Davis prior to returning to Hobart. Underway (meteorological, fluorometer, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report at the Related URL section. proprietary @@ -368,8 +367,8 @@ id title catalog state_date end_date bbox url description license 201920020_1 Aurora Australis Voyage 2 2019/20 Track and Underway Data AU_AADC STAC Catalog 2019-12-22 2020-01-17 110, -66.5, 147, -42.4 https://cmr.earthdata.nasa.gov/search/concepts/C1834759918-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2019/20 season. Purpose of voyage: Casey Resupply, recover and deploy whale mooring. Leader: Mr. James Moloney Deputy Leader: Miss Anthea Fisher VM Trainee: Ms Gemma Dyke Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary 201920030_1 Aurora Australis Voyage 3 2019/20 Track and Underway Data AU_AADC STAC Catalog 2020-01-21 2020-03-06 62, -67.5, 147, -42.4 https://cmr.earthdata.nasa.gov/search/concepts/C1834759919-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2019/20 season. Purpose of voyage: Mawson over water resupply and refuel, deploy/retrieve personnel to Mawson and Davis, retrieve and deploy whale acoustic mooring.. Leader: Mr. Andy Cianchi Deputy Leader: Ms Amy Young Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary 201920040_1 Aurora Australis Voyage 4 2019/20 Track and Underway Data AU_AADC STAC Catalog 2020-03-10 2020-03-25 147, -54.5, 160, -42.4 https://cmr.earthdata.nasa.gov/search/concepts/C1834759920-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2019/20 season. Purpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support. Voyage Leader: Ms Nicki Wicksl Deputy Voyage Leader: Mr Chris Hill Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary +20ec12f5d1f94e99aff2ed796264ee65_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0 FEDEO STAC Catalog 1997-01-01 2021-12-31 -180, 25, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C3327359729-FEDEO.umm_json This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. proprietary 22254b5608ab430fa360d0ff7e71c34e_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 FEDEO STAC Catalog 1991-08-15 2010-06-01 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142729-FEDEO.umm_json This dataset contains a time series of ice velocities for the Petermann glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 16/08/1991 and 01/06/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 1 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary -222cf11f49a94d2da8a6da239df2efc4_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions (2002-2018) from the JASON satellites, v1.1 FEDEO STAC Catalog 2002-01-15 2018-05-30 -30, -45, 160, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2548143583-FEDEO.umm_json This dataset contains along-track sea level anomalies derived from satellite altimetry. Altimeter along-track sea level measurements from the Jason-1, Jason -2 and Jason-3 satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). These six time series cover the period from 15 January 2002 to 30 May 2018.The product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. The main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). During the project, the product will be extended in spatial coverage and with additional altimeter missions. This version of the dataset is v1.1. (DOI: 10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005) proprietary 2282b4aeb9f24bc3a1e0961e4d545427_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1 FEDEO STAC Catalog 1991-11-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143215-FEDEO.umm_json This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. The L3U products provide these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 and the Long Term product v1.1. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary 234Th_data_0 234Th and POC data in the North Pacific SCIOPS STAC Catalog 1997-11-12 2008-10-28 142.5, 35, 145, 57 https://cmr.earthdata.nasa.gov/search/concepts/C1667896877-SCIOPS.umm_json We had made time-series observations of 234Th and POC in the North Pacific. In this dataset, we present vertical profiles of 234Th, POC, PON, and Chlorophyll a in the North Pacific. These data will help further understanding of particle dynamics at the euphotic layer. proprietary 2457272c747f4d6ca33cb40833bd9cc2_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae and 79Fjord area from ERS-1, ERS-2 and Envisat data for 1991-2011, v1.1 FEDEO STAC Catalog 1991-07-31 2011-02-07 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142956-FEDEO.umm_json This dataset contains a time series of ice velocities for the Zachariae and 79Fjord area in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 01/08/1991 and 07/02/2011. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary @@ -380,6 +379,7 @@ id title catalog state_date end_date bbox url description license 2940cda8-cf01-490a-a7ab-688bd54fb56a Earthquake Risk-Probable Maximum Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848573-CEOS_EXTRA.umm_json The map (risk map) presents the results of earthquake probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary 294b4075ddbc4464bb06742816813bdc_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02 FEDEO STAC Catalog 2003-01-08 2012-03-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142508-FEDEO.umm_json The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT. It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data. A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary 296f4386-4af1-4a73-866c-d9192ec18685_NA MERIS - Water Parameters - North Sea, 10-Day FEDEO STAC Catalog 2006-01-01 2010-03-10 -6.10393, 49.9616, 11.4301, 61.9523 https://cmr.earthdata.nasa.gov/search/concepts/C2207458047-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps. proprietary +2ac9a3e7bdeb41b58b226a2fa612a4a3_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00 FEDEO STAC Catalog 2002-08-01 2012-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359539-FEDEO.umm_json This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage for the monthly dataset starts from August 2002 and ends March 2012. There is a twelve day gap in the underlying data due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary 2bb39206-6988-4127-89e5-85a0430e20cc Earthquakes frequency for MMI categories higher than 9 1973-2007 CEOS_EXTRA STAC Catalog 1973-01-01 2007-12-31 -180, -58, 180, 85.03594 https://cmr.earthdata.nasa.gov/search/concepts/C2232848457-CEOS_EXTRA.umm_json This dataset includes an estimate of earthquake frequency of MMI categories higher than 9 over the period 1973-2007. It is based on Modified Mercalli Intensity map available in the Shakemap Atlas from USGS. Unit is expected average number of events per 1000 years. This product was compiled by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing Shakemap Atlas from USGS, compilation and global hazard distribution UNEP/GRID-Europe. proprietary 2dimpacts_1 Two-Dimensional Video Disdrometer (2DVD) IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-15 2020-02-28 -75.4912, 37.9194, -75.4462, 37.9543 https://cmr.earthdata.nasa.gov/search/concepts/C1995564612-GHRC_DAAC.umm_json The Two-Dimensional Video Disdrometer (2DVD) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. These data consist of the size, equivalent diameter, fall speed, oblateness, cross-sectional area of raindrops, particle concentration, total number of drops, total drop concentration, liquid water content, rain rate, reflectivity, and rain event characteristics. Data files are available from January 15, 2020 through February 28, 2020 in ASCII format. proprietary 2e54b40f184b44c797db36e192d2b679_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier from COSMO-SkyMed for 2012-2014, v1.0 FEDEO STAC Catalog 2012-06-01 2014-12-25 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142500-FEDEO.umm_json This dataset contains ice velocity time series of then Jakobshavn glacier in Greenland, derived from intensity-tracking of COSMO-SkyMed data acquired between 2/6/2012 and 25/12/2014. The ice velocity data is derived using 4-day COSMO-SkyMed offset-tracking pairs. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 250m grid spacing. Image pairs with a repeat cycle of 4 days have been used.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by DTU Space. For further details, please consult the document:T. Nagler, et al., Product User Guide (PUG) for the Greenland_Ice_Sheet_cci project of ESA's Climate Change Initiative, version 2.0. proprietary @@ -392,12 +392,11 @@ id title catalog state_date end_date bbox url description license 31137897d305407c9b83d49d124e4d1d_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.3 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143001-FEDEO.umm_json The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.3 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 proprietary 319691c5-0322-458c-80d3-c2d60cfbb86c_NA MERIS - Water Parameters - Baltic Sea, Daily FEDEO STAC Catalog 2006-01-01 2012-04-08 6.98888, 52.1246, 34.1429, 66.7187 https://cmr.earthdata.nasa.gov/search/concepts/C2207458067-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps. proprietary 326bf808aedd41fd85594fc06678d20a_NA ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR monthly gridded cloud properties, version 3.0 FEDEO STAC Catalog 1995-06-01 2012-04-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142591-FEDEO.umm_json The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the ATSR2-AATSR L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003. To cite the full dataset, please use the following citation: Poulsen, Caroline; McGarragh, Greg; Thomas, Gareth; Stengel, Martin; Christensen, Matthew; Povey, Adam; Proud, Simon; Carboni, Elisa; Hollmann, Rainer; Grainger, Don (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci ATSR2-AATSR L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD) and Rutherford Appleton Laboratory (Dataset Producer), DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 proprietary -3534bbf43fa14e40bc61944eaf664511_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2 FEDEO STAC Catalog 2017-11-10 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142898-FEDEO.umm_json This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD.The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.This data was produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project. proprietary -35ea8189e75e4b6f95e7c86812080ecb_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v1.4 FEDEO STAC Catalog 2002-03-31 2017-06-30 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143445-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2017; and mass trend grids for different 5-year periods between 2003 and 2017. This version (1.4) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016), apart from August 2016 time series which is computed using the CRS-R05 solution.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013. proprietary +330b7c922a37420fabb3425671d7d7c6_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00 FEDEO STAC Catalog 2016-05-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360235-FEDEO.umm_json This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary +35ea8189e75e4b6f95e7c86812080ecb_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v1.4 FEDEO STAC Catalog 2002-04-01 2017-06-30 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143445-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2017; and mass trend grids for different 5-year periods between 2003 and 2017. This version (1.4) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016), apart from August 2016 time series which is computed using the CRS-R05 solution.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013. proprietary 3628cb2fdba443588155e15dee8e5352_NA ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1 FEDEO STAC Catalog 2001-01-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142691-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The MODIS Fire_cci v5.1 grid product described here contains gridded data on global burned area derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2019. This product supercedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has been periodically extended to include 2018 to 2020. This gridded dataset has been derived from the MODIS Fire_cci v5.1 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v2.0.7 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation. proprietary 373638ed9c434e78b521cbe01ace5ef7_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-08-23 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143588-FEDEO.umm_json This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary 376e342e-3fb8-4d98-bd1e-51a204e1268b_NA MERIS - Water Parameters - Baltic Sea, Seasonal FEDEO STAC Catalog 2006-01-01 2012-04-08 6.98888, 52.1246, 34.1429, 66.7187 https://cmr.earthdata.nasa.gov/search/concepts/C2207458029-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides seasonal maps. proprietary -37e8a29d208d4a87ae4dbe1d16b2c0ef_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143560-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided. proprietary 38725_Not Applicable Assessment of Existing Information for Atlantic Coastal Fish Habitat Partnership (ACFHP) NOAA_NCEI STAC Catalog 2009-01-01 2009-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102655685-NOAA_NCEI.umm_json The ACFHP database consist of three primary data tables, joined within SQL Server, a relational DBMS: 1. The Bibliographic table provides information on over 500 selected documents and data sources on Atlantic coastal fish species and habitats. 2. The Assessment table provides information on habitat condition indicators, threats, and conservation actions. 3. The Geospatial table provides location references for information recorded in the Bibliography and Assessment tables. In addition, a separate table enables the many-to-many relationship between bibliographic entries and locations. proprietary 38734_Not Applicable Bioeffects Assessment in Kvichak and Nushagak Bay, Alaska: Characterization of Soft Bottom Benthic Habitats, Fish Body Burdens and Contaminant Baseline Assessment NOAA_NCEI STAC Catalog 2016-01-01 2016-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102655773-NOAA_NCEI.umm_json The goal of this project is to assess habitat conditions that influence biodiversity and distribution of benthic infaunal communities, contaminants, and chemical body burdens of resident organisms as measures of environmental health in Bristol Bay. Bristol Bay boasts one of the largest commercial and subsistence salmon fisheries in the world. Significant mining activities have been proposed within the bay's watershed that could impact Bristol Bay chemistry and biology, but baseline data are lacking. Baseline data will be essential for monitoring pollution control effectiveness in the watershed. The datasets generated from this study will be incorporated into the NOAA's National Status and Trend (NS&T) Program database which has been developing a dynamic quantitative database on contaminants, toxicity and benthic infaunal species distribution assessed in the coastal U.S. since 1991. Therefore, the value of this project stems not only from the importance of the locale, but also from the fact that it will continue to expand the Alaskan data set in a national online database readily accessible to Alaskan coastal managers, scientific and local communities, and which will support the Alaska Fish Monitoring Program. This is a collaborative effort between the NOAA National Centers for Coastal Ocean Science (NCCOS), the Univ. of Alaska Fairbanks (UAF), and the U.S. Fish and Wildlife Service (FWS). NPRB supplemental funding will allow the collaborators to conduct a comprehensive synoptic assessment of Nushagak and Kvichak Bays, which would not be otherwise possible. proprietary 38737_Not Applicable Bocaccio larvae distribution off California NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656008-NOAA_NCEI.umm_json The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary @@ -537,7 +536,7 @@ id title catalog state_date end_date bbox url description license 3DIMG_L2P_WVW INSAT-3D Imager Level-2P WV WINDS ISRO STAC Catalog 2013-10-01 20, -50, 130, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214622581-ISRO.umm_json Suitable tracers are identified in WV(Water Vapour) band imagery and tracked in subsequent half-hourly imageries to determine cloud motion vector proprietary 3He_Exposure_dates_Mt_Waesche 3He exposure dates from Mount Waesche, Marie Byrd Land SCIOPS STAC Catalog 1970-01-01 -126.9, -77.167, -126.9, -77.167 https://cmr.earthdata.nasa.gov/search/concepts/C1214614861-SCIOPS.umm_json The data are 3He exposure ages from lateral moraine bands on Mount Waesche, a volcanic nunatak in Marie Byrd Land, West Antarctica. The proximal part of the moraine is up to 45 meters above the present ice level was deposited approximately 10,000 years ago, well after the glacial maximum in the Ross Embayment. The upper distal part of the moraine may record multiple earlier ice advances. The data are all generated by crushing and melting mineral separates (mostly olivine) in vacuo, and measurements with a noble gas mass spectrometer at Woods Hole Oceanographic Institution. Full details can be found in Ackert et al. (Science, 1999, vol. 286, p.276-280). proprietary 3ac333b828b54e3495c7749f5bce2fe3_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): Oceanic Indicators of Mean Sea Level Changes, Version 2.0 FEDEO STAC Catalog 1993-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143570-FEDEO.umm_json As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a number of oceanic indicators of mean sea level changes have been produced from merging satellite altimetry measurements of sea level anomalies. The oceanic indicators dataset consists of static files covering the whole altimeter period, describing the evolution of the project's monthly sea level anomaly gridded product (see separate dataset record).The oceanic indicators that are provided are: 1) the temporal evolution of the global Mean Sea Level (MSL) DOI: 10.5270/esa-sea_level_cci-IND_MSL_MERGED-1993_2015-v_2.0-201612 ;2) the geographic distribution of Mean Sea Level changes (MSLTR) DOI: 10.5270/esa-sea_level_cci-IND_MSLTR_MERGED-1993_2015-v_2.0-201612 ;3) Maps of the amplitude and phase of the annual cycle (MSLAMPH) DOI: 10.5270/esa-sea_level_cci-IND_MSLAMPH_MERGED-1993_2015-v_2.0-201612.The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612.When using or referring to the SL_cci products, please mention the associated DOIs and also use the following citation where a detailed description of the SL_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these products please email: info-sealevel@esa-sealevel-cci.org proprietary -3b3fd2daf3d34c1bb4a09efeaf3b8ea9_NA ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2019), version 1.0 FEDEO STAC Catalog 2000-02-24 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142546-FEDEO.umm_json This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 – 2019. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps. proprietary +3bdb21a4cd004e5f8cc148fea5f1d4e3_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359686-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary 3bfe0c2d51544f72837a99306a74e359_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143225-FEDEO.umm_json "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., ""Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,"" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." proprietary 3c324bb4ee394d0d876fe2e1db217378_NA ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.0 FEDEO STAC Catalog 1992-09-26 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142668-FEDEO.umm_json "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:• Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.• Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.• Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." proprietary 3d_snow_models_4.0 3D_Snow_Models ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.8471832, 46.8146287, 9.8471832, 46.8146287 https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.umm_json The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel). proprietary @@ -551,81 +550,77 @@ id title catalog state_date end_date bbox url description license 43d73291472444e6b9c2d2420dbad7d6_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143086-FEDEO.umm_json The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 proprietary 43f81a9f-f903-43d4-8333-dcda52b2bc63 Global estimated risk index for flood hazard CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2232847571-CEOS_EXTRA.umm_json This dataset includes an estimate of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe. proprietary 466b48b8-78c4-4009-97a2-c8d70f9075bf_NA MERIS - Water Parameters - Baltic Sea, 10-Day FEDEO STAC Catalog 2006-01-01 2012-04-08 6.98888, 52.1246, 34.1429, 66.7187 https://cmr.earthdata.nasa.gov/search/concepts/C2207458045-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps. proprietary -46d136149d0a4f1cb8de7efbe8abf4b2_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8 FEDEO STAC Catalog 2009-03-31 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142763-FEDEO.umm_json The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document. proprietary +46d136149d0a4f1cb8de7efbe8abf4b2_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8 FEDEO STAC Catalog 2009-04-01 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142763-FEDEO.umm_json The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document. proprietary 47211801-72f3-4064-8c01-715cd2b7dc71_1 Matthews' global vegetation DB for climate studies CEOS_EXTRA STAC Catalog 1984-05-01 1984-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847392-CEOS_EXTRA.umm_json "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is ""Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487."" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes." proprietary +474ac06235e54e6cb0ec6eed635e1213_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359219-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 48fc3d1e8ada405c8486ada522dae9e8_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0 FEDEO STAC Catalog 2010-11-01 2017-04-30 -180, -90, 180, -16 https://cmr.earthdata.nasa.gov/search/concepts/C2548143597-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information. proprietary 4afb736dc395442aa9b327c11f0d704b_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ensemble product), Version 2.6 FEDEO STAC Catalog 1995-08-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142590-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. For further details about these data products please see the documentation. proprietary 4b0773a84e8142c688a628c9ce62d4ec_NA ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 1.1 FEDEO STAC Catalog 2016-01-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142938-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v1.1 (also available), which covers Sub-Saharan Africa for the year 2016, by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. proprietary 4e106bb70a6b42d8a5a86c4635c855b9_NA ESA Ozone Climate Change Initiative (Ozone CCI): MIPAS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2005-01-01 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142918-FEDEO.umm_json "This dataset comprises gridded limb ozone monthly zonal mean profiles from the MIPAS instrument on the ENVISAT satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file ""ESACCI-OZONE-L3-LP-MIPAS_ENVISAT-MZM-2008-fv0001.nc“ contains monthly zonal mean data for MIPAS in 2008." proprietary 4eb4e801424a47f7b77434291921f889_NA ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Nadir Ozone Profile Merged Data Product, version 2 FEDEO STAC Catalog 1997-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142678-FEDEO.umm_json This dataset contains Level 3 nadir profile ozone data from the ESA Ozone Climate Change Initiative (CCI) project. The Level 3 data are monthly averages on a regular 3D grid derived from level 2 ozone profiles. In this version 2 of the dataset, data are available for 1997 and 2007 and 2008 only, and use data from the GOME instrument on ERS (1997) and the GOME-2 instrument on METOP-A (2007, 2008). proprietary 512c252f-34ac-41fd-a156-f2e96a608f79_NA IRS-P6 Resourcesat-1 - Wide Field Sensor Images (AWiFS) - Europe FEDEO STAC Catalog 2010-01-15 2013-01-12 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458023-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the products coverage size of 370 km x 370 km make AWiFS products a valuable source for application fields such forestry and environmental monitoring proprietary -51fc11a9438b466db2ec8bd098efe7d5_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143188-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary -52266ccfbc3348a8afc27b67d6bbc6c2_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142498-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 3.1 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary 53554204-282b-457e-b36d-a168679a0c1f_NA TerraSAR-X - Stripmap Images (TerraSAR-X StripMap) FEDEO STAC Catalog 2007-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458064-FEDEO.umm_json "This collection contains radar image products of the German national TerraSAR-X mission acquired in StripMap mode. StripMap imaging allows for a spatial resolution of up to 3 m at a scene size of 30 km (across swath) x 50-1650 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html" proprietary -5400de38636d43de9808bfc0b500e863_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142630-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary -5484dc1392bc43c1ace73ba38a22ac56_NA ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2019), version1.0 FEDEO STAC Catalog 1995-08-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142797-FEDEO.umm_json This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1982-2019. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years. proprietary 54e2ee0803764b4e84c906da3f16d81b_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0 FEDEO STAC Catalog 2002-09-30 2012-03-31 -180, 50, 180, 81.45 https://cmr.earthdata.nasa.gov/search/concepts/C2548142660-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. proprietary 550d938da3184d0ca44a06a4c0c14ffa_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0 FEDEO STAC Catalog 2002-06-10 2012-03-31 -180, -81.45, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C2548142582-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information. proprietary -55c20c0cb35b4a7c8ef8b65694fe46e2_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142509-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 3.1 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary -56f81895cb094bd8a1638aa12d6c7499_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1 FEDEO STAC Catalog 2009-04-17 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142501-FEDEO.umm_json The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4.The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available.The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG). proprietary +56f81895cb094bd8a1638aa12d6c7499_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1 FEDEO STAC Catalog 2009-04-18 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142501-FEDEO.umm_json The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4.The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available.The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG). proprietary 57485ff5-8523-4251-9d01-2f497a31cc48_NA Resourcesat-2 - Wide Field Sensor Images (AWiFS) - Europe FEDEO STAC Catalog 2014-03-12 2017-04-21 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458044-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the products coverage size of 370 km x 370 km make AWiFS products a valuable source for application fields such forestry and environmental monitoring proprietary 57b4201d-5bf0-4a4a-ab88-5674c7af02ca_NA METOP GOME-2 - Bromine Monoxide (BrO) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457994-FEDEO.umm_json The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational BrO (Bromine monoxide) total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to https://atmos.eoc.dlr.de/app/missions/gome2 proprietary -584d4028633a4b7e9fa36da72dbd91c7_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143369-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 58f00d8814064b79a0c49662ad3af537_NA ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1 FEDEO STAC Catalog 2001-01-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143166-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2020. The Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.Files are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation. proprietary 5940d3fb-860d-4f3e-bc3a-4022639c272a_1 Matthews' global cultivation intensity (land use) CEOS_EXTRA STAC Catalog 1984-05-01 1984-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848577-CEOS_EXTRA.umm_json "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is ""Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487."" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes. " proprietary 5970b33c92ef444793fb6d7e54d1230e_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by TU Dresden, v1.3 FEDEO STAC Catalog 2002-03-31 2016-08-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142627-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.3) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065 proprietary 598f86dc-01da-49e3-824e-c8b8f1089a0e_1 Matthews' seasonal integrated surface albedo CEOS_EXTRA STAC Catalog 1984-05-01 1984-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848146-CEOS_EXTRA.umm_json "The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation type (one out of 32 classes) within each one degree-square latitude/longitude grid cell. Matthews Cultivation Intensity data set is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes. Matthews Seasonal Integrated Albedo data set includes four data files for Winter, Spring, Summer and Autumn (January, April, July and October respectively in the Northern Hemisphere; and July, October, January and April for the Southern Hemisphere). They show the seasonal percentage of incoming radiation reflected into space, integrated across the electro- magnetic spectrum. These are based on the vegetation and cultivation intensity maps, rather than being measured directly, and are for snow- free conditions except for permanently snow-covered continental ice. The proper reference to these data sets is ""Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487."" The Matthews Vegetation, Cultivation Intensity and Seasonal Albedo data files have a spatial resolution of one degree latitude/longitude, are one byte/eight bits per pixel, and consist of 180 rows (lines) by 360 columns (elements/pixels/samples) of data. Their origin point is at 90 degrees North latitude and 180 degrees West longitude, and they extend to 90 degrees South latitude and 180 degrees East longitude. At one degree resolution, each of these data files comprises 64.8 kilobytes. " proprietary 5990f1bd-9fc6-4a22-bbb5-c269312fec06_NA IRS-1D - Wide Field Sensor Images (WiFS) - Europe FEDEO STAC Catalog 1999-01-28 2005-01-27 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458035-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. proprietary -5a168a35-8cd2-4960-a134-2f319bb06760_NA METOP GOME-2 - Ozone (O3) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457992-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary -5ab5267b17254152bcdbc055747faa02_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142826-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary +5a168a35-8cd2-4960-a134-2f319bb06760_NA METOP GOME-2 - Ozone (O3) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457992-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary 5b6033bfb7f241e89132a83fdc3d5364_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0 FEDEO STAC Catalog 2010-11-01 2017-04-30 -180, 50, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2548142879-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017. proprietary 5c9935b8b8854baeb7a256446293c03b_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-01-22 2017-03-22 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143202-FEDEO.umm_json This dataset contains a time series of ice velocities for the Hagen glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary 5db2099606b94e63879d841c87e654ae_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record, version 2.1 FEDEO STAC Catalog 1991-11-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142779-FEDEO.umm_json This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary 5e52c881-6209-438a-a3e3-309fb4d303f6_NA IRS-P6 Resourcesat-1 - Multispectral Images (LISS-III) - Europe FEDEO STAC Catalog 2010-01-15 2013-01-12 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458018-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. proprietary 5e557c5e-a31d-41fb-a60b-98714d0aff86_NA Resourcesat-2 - Multispectral Images (LISS-III) - Europe FEDEO STAC Catalog 2014-01-07 2014-01-08 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458051-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. proprietary -5eecdf4c-de57-4624-99e9-60086b032aea_NA TanDEM-X - Digital Elevation Model (DEM) - Global, 12m FEDEO STAC Catalog 2010-12-12 2015-01-16 -180, -90, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2207457995-FEDEO.umm_json TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical separation between the satellites of 120m to 500m a global Digital Elevation Model (DEM) has been generated. The main objective of the TanDEM-X mission is to create a precise 3D map of the Earth's land surfaces that is homogeneous in quality and unprecedented in accuracy. The data acquisition was completed in 2015 and production of the global DEM was completed in September 2016. The absolute height error is with about 1m an order of magnitude below the 10m requirement.The TanDEM-X 12m DEM is the nominal product variant of the global Digital Elevation Model (DEM) acquired in the frame of the German TanDEM-X mission between 2010 and 2015 with a spatial resolution of 0.4 arcseconds (12m at the equator). It covers all Earth’s landmasses from pole to pole. For more information concerning the TanDEM-X mission, the reader is referred to: https://www.dlr.de/dlr/en/desktopdefault.aspx/tabid-10378/ proprietary +5f331c418e9f4935b8eb1b836f8a91b8_NA ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3 FEDEO STAC Catalog 2010-01-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360191-FEDEO.umm_json This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format. proprietary +5f66a881adf846bfaad58b0e6068f0ea_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00 FEDEO STAC Catalog 2018-11-17 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360139-FEDEO.umm_json This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary 5f75fcb0c58740d99b07953797bc041e_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 50km grid spacing, version 2.1 FEDEO STAC Catalog 2002-05-31 2017-05-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143222-FEDEO.umm_json The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This version of the product is v2.1, which is an extension of the version 2.0 Sea_Ice_cci dataset and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 25km grid spacing is also available. proprietary -612a615afb5d48459b385380b440b545_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142614-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2020.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided. proprietary 62866635ab074e07b93f17fbf87a2c1a_NA ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1 FEDEO STAC Catalog 1982-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143168-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide. proprietary -62c0f97b1eac4e0197a674870afe1ee6_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-08-31 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142903-FEDEO.umm_json This v2.1 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. These data cover the period between 09/1981 and 12/2016.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.The CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary +62c0f97b1eac4e0197a674870afe1ee6_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-09-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142903-FEDEO.umm_json This v2.1 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. These data cover the period between 09/1981 and 12/2016.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.The CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary 63a8f458-da8b-461a-afa4-7166d1cdc817_1 Cloudiness, long-term mean monthly values (IIASA) CEOS_EXTRA STAC Catalog 1990-01-01 1990-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847206-CEOS_EXTRA.umm_json "The IIASA Climate Database was created at the International Institute for Applied System Analyses (IIASA; Laxenburg, Austria) by Rik Leemans and Wolfgang P. Cramer to represent current global climate. There are three variables included in the Database: average monthly cloudiness, precipitation and temperature; and 12 monthly values per variable. These values were calculated from existing historical weather records of a diverse nature, but with the common feature that most cover at least five years during the period between 1930 and 1960. The weather records from up to eight different sources were standardized, ranked in quality, selected, interpolated and smoothed to fit a one-half degree (.5 ) latitude/longitude terrestrial grid surface (there are no values for non-land areas). The three variables have been treated in somewhat different fashion in their processing at GRID, as explained below. The areas with the best data coverage are Europe, the USA, southern Canada, East Asia and Japan, while Africa and Australia have less complete coverage. High latitude, arid and mountainous zones exhibit the least coverage, especially Siberia, northern Canada, South America, China, Mongolia and the Tibetan Plateau. Despite certain data gaps and inconsistencies, the IIASA Climate Database is considered appropriate for use at least at regional scales and above, in various applications relating to agriculture, biogeography, ecology, geography and especially vegetation models. The full and proper reference to the Database is: Leemans, Rik and Wolfgang P. Cramer, 1991. The IIASA Database for Mean Monthly Values of Temperature, Preicipitation and Cloudiness of a Global Terrestrial Grid. IIASA, Laxenburg, Austria, RR-91-18, 62 pages. The original IIASA Climate Database is distributed by Leemans and Cramer in tabular form (a series of ascii files, with binary conversion program) on diskettes. There are three tables (one for cloudiness, precipitation and temperature variables) each having a long series of data records with 14 values as follows: longitude, latitude, 12 monthly values (January to December). GRID-Geneva has converted these tables into separate monthly data files with a standard image format. That is, for each of the three variables/12 months there exists a 360-row (line, record) by 720-column (element, pixel, sample) array of values which can be manipulated as an image. The original data values have been preserved by storing them in four-byte real (floating point) or two-byte integer arrays, where the geographic location (center point) of each pixel is known. GRID has also produced simplified one-byte image arrays for all three variables' data files, which are generalized versions for portrayal on most image display systems, rather than being suitable for analysis. ---------------------------------------------------------------------- Cloudiness Data Set The IIASA mean monthly cloudiness data set is based on fewer stations, and thus contains only about one-quarter the number of data records (approximately 1600) compared with the other two variables. It is often derived from estimated rather than computed data. Cloudiness is defined as the actual number of bright sunshine hours over the potential number, and is thus expressed as a percentage figure. The data set shows slight distortions which probably resulted from the interpolation routine. These are more pronounced with odd patterns in high-latitude zones, where fewer stations were available and more extrapolation was done. The GRID version of this data set includes 12 monthly average cloudiness data files, each in one-byte (eight-bit) image format. The data arrays are all 360 rows (lines, records) by 720 columns (elements, pixels, or samples), and cover the entire globe from 90 degrees North latitude and 180 degrees West longitude, to 90 degrees South latitude and 180 degrees East longitude. The data values are within a range from 0 to 100 (per cent), except for the oceans where values equal 255. The data files are in the Plate Carree (Simple Cylindrical) projection, which is a particular form of the Equirectangular. This projection is described in a book entitled ""Map projections used by the U. S. Geological Survey, Geological Survey Bulletin 1532 (second ed.), U. S. Government Printing Office, Washington D.C., 1982"" p. 89, or by request directly from GRID. " proprietary 65abcfdc-306a-47f6-9696-f1f6c6171def Cyclonic Wind Risk-Probable Maximum Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848382-CEOS_EXTRA.umm_json The map (risk map) presents the results of cyclonic wind probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary -66534da90ed44abebfc1b08adca4f9c3_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143489-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 67a3f8c8dc914ef99f7f08eb0d997e23_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0 FEDEO STAC Catalog 1997-01-01 2019-12-31 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142663-FEDEO.umm_json This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year. proprietary -6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA METOP GOME-2 - Sulfur Dioxide (SO2) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458007-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational SO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. GDP 4.x performs a DOAS fit for SO2 slant column followed by an AMF / VCD computation using a single wavelength. Corrections are applied to the slant column for equatorial offset, interference of SO2 and SO2 absorption, and SZA dependence. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary +690fdf8f229c4d04a2aa68de67beb733_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359002-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided. proprietary +6cec07af-ffcb-4077-bf67-b8b03fc001a8_NA METOP GOME-2 - Sulfur Dioxide (SO2) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458007-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational SO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. GDP 4.x performs a DOAS fit for SO2 slant column followed by an AMF / VCD computation using a single wavelength. Corrections are applied to the slant column for equatorial offset, interference of SO2 and SO2 absorption, and SZA dependence. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary 6e2091cb0c8b4106921b63cd5357c97c_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v3.0 FEDEO STAC Catalog 1997-01-01 2019-12-31 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143128-FEDEO.umm_json This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year. proprietary 723067f77b8b43609079d721e3b4a3c7_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq glacier from ERS-1, ERS-2, Envisat for 1992-2008, v1.0 FEDEO STAC Catalog 1992-01-02 2008-12-17 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143131-FEDEO.umm_json This dataset contains a time series of ice velocities for the Kangerlussuaq glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data aquired between 02/01/1992 and 17/12/2008. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 3 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary 7813eb75a131474a8d908f69c716b031_NA ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v2.31, for 2010 to 2019 FEDEO STAC Catalog 2010-01-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143429-FEDEO.umm_json The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag. proprietary 7ae5a791-b667-4838-9733-a44e4cf2d715_NA Cartosat-1 (IRS-P5) - Panchromatic Images (PAN) - Europe, Stereographic FEDEO STAC Catalog 2007-01-05 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458042-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. proprietary 7bcddca7-f59e-4298-978a-37dd39ac6dba_NA IRS-1C - Wide Field Sensor Images (WiFS) - Europe FEDEO STAC Catalog 1996-01-25 2004-10-31 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458026-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The revisit capability of only 5 days and the product coverage size of 800 km x 800 km make WiFS products a valuable source for application fields such as flood and snow melt monitoring. proprietary -7db4459605da4665b6ab9a7102fb4875_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-08-23 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142734-FEDEO.umm_json This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary +7db4459605da4665b6ab9a7102fb4875_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-08-24 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142734-FEDEO.umm_json This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary 7dd46ee62153409f8e1b2b7b251177c1_NA ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-AQUA monthly gridded cloud properties, version 2.0 FEDEO STAC Catalog 2002-07-31 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143381-FEDEO.umm_json The Cloud_cci MODIS-Aqua dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Aqua) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Aqua dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures. proprietary 7e139108035142a9a1ddd96abcdfff36_NA ESA Land Cover Climate Change Initiative (Land_Cover_cci): Water Bodies Map, v4.0 FEDEO STAC Catalog 2000-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143235-FEDEO.umm_json "As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced. The CCI WB v4.0 is composed of two layers:1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.This product is delivered at 150 m as a stand-alone product but it is consistent with class ""Water Bodies"" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water2. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; D’Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036" proprietary 7f60b26b50c98fab019e9351b45ba946c7d04047 3 year daily average solar exposure map Mali 3Km GRAS June 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603983-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for June. proprietary 7fb8fd2761484b1eae4f7df4a3e65f75_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from GOMOS (AERGOM algorithm), Version 3.00 FEDEO STAC Catalog 2002-04-01 2012-05-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142622-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 gridded stratospheric aerosol properties from the GOMOS instrument on the ENVISAT satellite. This version of the data is version 3.00, and has been derived using the AERGOM algorithm by BIRA (Belgian Institute for Space Aeronomy). For further details about these data products please see the linked documentation. proprietary +7fc9df8070d34cacab8092e45ef276f1_NA ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.1 FEDEO STAC Catalog 1992-09-26 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360101-FEDEO.umm_json This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.1.0 of the dataset.The six thematic climate variables included in this dataset are:• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).• Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.Satellite sensors associated with the thematic climate variables are as follows:LWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; LWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; LIC: Terra/Aqua MODIS; LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z proprietary 802569b8-fb56-4d78-a2e8-3e4549ff475b_NA AVHRR - Sea Surface Temperature (SST) - Europe FEDEO STAC Catalog 1994-08-01 -35, 47.5, 51, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2207458053-FEDEO.umm_json The AVHRR Mulitchannel Sea Surface Temperature Map (MCSST) was the first result of DLR's AVHRR pathfinder activities. The goal of the product is to provide the user with actual Sea Surface Temperature (SST) maps in a defined format easy to access with the highest possible reliability on the thematic quality. After a phase of definition, the operational production chain was launched in March 1993 covering the entire Mediterranean Sea and the Black Sea. Since then, daily, weekly, and monthly data sets have been available until September 13, 1994, when the AVHRR on board the NOAA-11 spacecraft failed. The production of daily, weekly and monthly SST maps was resumed in February, 1995, based on NOAA-14 AVHRR data. The NOAA-14 AVHRR sensor became some technical difficulties, so the generation was stopped on October 3, 2001. Since March 2002, NOAA-16 AVHRR SST maps are available again. With the beginning of January 2004, the data of AVHRR on board of NOAA-16 exhibited some anormal features showing strips in the scenes. Facing the “bar coded” images of NOAA16-AVHRR which occurred first in September 2003, continued in January 2004 for the second time and appeared in April 2004 again, DFD has decided to stop the reception of NOAA16 data on April 6th, 2004, and to start the reception of NOAA-17 data on this day. On April 7th, 2004, the production of all former NOAA16-AVHRR products as e.g. the SST composites was successully established. NOAA-17 is an AM sensor which passes central Europe about 2 hours earlier than NOAA-16 (about 10:00 UTC instead of 12:00 UTC for NOAA-16). In spring 2007, the communication system of NOAA-17 has degraded or is operating with limitations. Therefore, DFD has decided to shift the production of higher level products (NDVI, LST and SST) from NOAA-17 to NOAA-18 in April 2007. In order to test the performance of our processing chains, we processed simultaneously all NOAA-17 and NOAA-18 data from January 1st, 2007 till March 29th, 2007. All products are be available via EOWEB. Please remember that NOAA-18 is a PM sensor which passes central Europe about 1.5 hours later than NOAA-17 (about 11:30 UTC instead of 10:00 UTC for NOAA17). The SST product is intended for climate modelers, oceanographers, and all geo science-related disciplines dealing with ocean surface parameters. In addition, SST maps covering the North Atlantic, the Baltic Sea, the North Sea and the Western Atlantic equivalent to the Mediterranean MCSST maps are available since August 1994. The most important aspects of the MCSST maps are a) correct image registration and b) reasonable cloud screening to ensure that only cloud free pixels are taken for the later processing and compositing c) for deriving MCSST, only channel 4 and 5 are used.. The SST product consists of one 8 bit channel. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/ proprietary -806b30b9dc7f44e6bd56a46d8bccf279_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143296-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary 810631f4-c311-44f2-9ced-c2260df2bc06_NA METOP GOME-2 - Cloud Optical Thickness (COT) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458065-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary 81332b9a10f14bda8a1a83b6463bb6de_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-01-22 2017-03-19 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142988-FEDEO.umm_json This dataset contains a time series of ice velocities for the Petermann Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 22/1/2015-19/3/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary -8154e881452f49c1ba86982ed88b20f0_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143104-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 5.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary +8175ede3a1d642deba8f4cce49d7bda8_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - October 2023 FEDEO STAC Catalog 2017-11-13 2023-10-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359018-FEDEO.umm_json This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication: Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023. proprietary 82e4ede59fe746ba810009d9a30e0153_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2014-2015, v1.0 FEDEO STAC Catalog 2014-11-01 2015-12-01 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142579-FEDEO.umm_json This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2014-2015, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities. proprietary 8381d3f3998143fd9b53c7086b7061e3_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series of the Storstrommen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 FEDEO STAC Catalog 1991-10-05 2010-03-20 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142676-FEDEO.umm_json This dataset contains a time series of ice velocities for the Storstrommen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 06/10/1991 and 20/03/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary -83e51cf29821434ea14db56c564946d5_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.1 FEDEO STAC Catalog 1982-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142628-FEDEO.umm_json This v2.1 SST_cci Climatology Data Record (CDR) consists of Level 4 daily climatology files gridded on a 0.05 degree grid. The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary -84403d09cef3485883158f4df2989b0c_NA ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2 FEDEO STAC Catalog 2010-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142753-FEDEO.umm_json This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)This release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes. proprietary +84403d09cef3485883158f4df2989b0c_NA ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2 FEDEO STAC Catalog 2010-01-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142753-FEDEO.umm_json This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)This release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes. proprietary 84b5cf8380894d719b61deac5abf3bae_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from the PALSAR instrument for 2006-2011, v1.1 (June 2016 version) FEDEO STAC Catalog 2006-12-20 2011-03-17 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143582-FEDEO.umm_json This dataset contains a time series of ice velocities for the Greenland margin from the PALSAR instrument on the ALOS satellite. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. This dataset consists of a time series of ice velocity with yearly sampling, derived from intensity tracking of PALSAR data acquired between 20-12-2016 and 17-03-2011. It provides components of the ice velocity and the magnitude of the velocity. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that the previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by GEUS. For further details, please consult the Product User Guide (v2.0)Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product. proprietary 84faf575c8e841a3a16476b05cbd657d_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Upernavik Glacier between 2017-07-15 and 2017-08-14, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-07-14 2017-08-14 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143118-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Upernavik Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-15 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The product was generated by S[&]T Norway. proprietary -8545a026-2e0c-466f-b6de-99faa639e3c0_NA TanDEM-X - Digital Elevation Model (DEM) - Global, 30m FEDEO STAC Catalog 2010-12-12 2015-01-16 -180, -90, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2207458027-FEDEO.umm_json TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical separation between the satellites of 120m to 500m a global Digital Elevation Model (DEM) has been generated. The main objective of the TanDEM-X mission is to create a precise 3D map of the Earth's land surfaces that is homogeneous in quality and unprecedented in accuracy. The data acquisition was completed in 2015 and production of the global DEM was completed in September 2016. The absolute height error is with about 1m an order of magnitude below the 10m requirement.The TanDEM-X 30m DEM is a product variant of the global Digital Elevation Model (DEM) acquired in the frame of the German TanDEM-X mission between 2010 and 2015, and has a reduced pixel spacing of 1 arcsecond (30m at the equator). It covers all Earth’s landmasses from pole to pole. For more information concerning the TanDEM-X mission, the reader is referred to: https://www.dlr.de/dlr/en/desktopdefault.aspx/tabid-10378/ proprietary +86d360431f3b4184b89cdd1cd707bb33_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360264-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 8889dfe3de45406e815bce13ae8a0c92_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Calving Front Locations, v3.0 FEDEO STAC Catalog 2014-10-01 2020-12-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142650-FEDEO.umm_json The data set provides calving front locations of 28 major outlet glaciers of the Greenland Ice Sheet, produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change. The calving front location has been derived by manual delineation using SAR (Synthetic Aperture Radar) data from the ERS-1/2, Envisat and Sentinel-1 satellites and satellite imagery from LANDSAT 5,7,8. The digitized calving fronts are stored in ESRI vector shape-file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The product was generated by ENVEO (Environmental Earth Observation Information Technology GmbH) proprietary -88c2bc7af4f0402d8ceecad611c58cc5_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142619-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, this the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary 88d02eb5a6c14952aa88028894d8a69c_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Døcker Smith Glacier between 2016-05-08 and 2016-05-18, generated using Sentinel-2 data, v1.0 FEDEO STAC Catalog 2016-05-07 2016-05-18 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143199-FEDEO.umm_json This dataset contains an optical ice velocity time series for the Døcker Smith Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2016-05-08 and 2016-05-18. It is part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway. proprietary 8a587870-2ad7-4626-9228-4caad2fc9246_NA METOP GOME-2 - Cloud Fraction (CF) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457988-FEDEO.umm_json The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/ proprietary 8b63d36f6f1e4efa8aea302b924bc46b_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ORAC Algorithm), Version 4.01 FEDEO STAC Catalog 2002-05-20 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142857-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. For further details about these data products please see the linked documentation. proprietary +8b9d461f245b4efd8ea9fa080366e3b1_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360650-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary 8d475d7d92894765ad1ddda16de0e610_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik glacier from ERS-1, ERS-2, Envisat and PALSAR data for 1992-2010, v1.2 FEDEO STAC Catalog 1992-01-02 2010-08-22 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142859-FEDEO.umm_json This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat and PALSAR data aquired between 02/01/1992 and 22/08/2010. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary +8ecae26f390b4938b67a97cbce3ecd8b_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359225-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary 8f5623a85d2e4b9b8ab5313f65a7c994_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the IMAP-DOAS algorithm (CH4_SCI_IMAP), v7.2 FEDEO STAC Catalog 2003-01-08 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142613-FEDEO.umm_json The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4.The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document. proprietary -915d2340b178494f987a6942e263a2eb_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142807-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary +90049a6555d1480bb5ce9637051dede8_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2 FEDEO STAC Catalog 2002-01-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359934-FEDEO.umm_json "This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856). The dataset should be cited as: Cazenave Anny, Gouzenes Yvan, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354In addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z - Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w" proprietary +90682bac7d0e4e418085f30eba43dfba_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360219-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary 916b93aaf1474ce793171a33ca4c5026_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1 FEDEO STAC Catalog 1991-11-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143116-FEDEO.umm_json This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SST's to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary -9255faeb392f41debf5402caa40dada8_NA ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0 FEDEO STAC Catalog 2009-04-17 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143169-FEDEO.umm_json The CO2_GOS_OCFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the University of Leicester Full-Physics Retrieval Algorithm, which is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the alternative SRFP algorithm, is also available. The OCFP product is considered the GHG_cci baseline product and it is advised that users who aren't sure which of the two products to use, use this product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.The XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG). proprietary +9255faeb392f41debf5402caa40dada8_NA ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0 FEDEO STAC Catalog 2009-04-18 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143169-FEDEO.umm_json The CO2_GOS_OCFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the University of Leicester Full-Physics Retrieval Algorithm, which is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the alternative SRFP algorithm, is also available. The OCFP product is considered the GHG_cci baseline product and it is advised that users who aren't sure which of the two products to use, use this product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.The XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG). proprietary 925e3f0e807243e2936cc492f5207af6_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq Glacier for 2015-2017 from Sentinel-1, v1.1 FEDEO STAC Catalog 2015-01-18 2017-03-21 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142695-FEDEO.umm_json This dataset contains a time series of ice velocity maps for the Kangerlussuag Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary +93444bc1c4364a59869e004bf9bfd94a_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0 FEDEO STAC Catalog 1997-01-01 2021-12-31 -180, 25, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C3327360075-FEDEO.umm_json This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. proprietary 93587051-2f12-4d37-a97b-520af56144ce_NA MERIS - Vegetation Index (NDVI) - Europe, 10-Day FEDEO STAC Catalog 2003-10-15 2010-03-10 -9.4073, 30.9919, 64.0152, 65.0184 https://cmr.earthdata.nasa.gov/search/concepts/C2207458054-FEDEO.umm_json "The ""AVHRR compatible Normalized Difference Vegetation Index derived from MERIS data (MERIS_AVHRR_NDVI)"" was developed in a co-operative effort of DLR (German Remote Sensing Data Centre, DFD) and Brockmann Consult GmbH (BC) in the frame of the MAPP project (MERIS Application and Regional Products Projects). For the generation of regional specific value added MERIS level-3 products, MERIS full-resolution (FR) data are processed on a regular (daily) basis using ESA standard level-1b and level-2 data as input. The regular reception of MERIS-FR data is realized at DFD ground station in Neustrelitz.The Medium Resolution Imaging MERIS on Board ESA's ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index (MERIS_AVHRR_NDVI) derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index with 300 meter resolution for the well-known Normalized Difference Vegetation Index (NDVI) derived from AVHRR (given in 1km spatial resolution). The NDVI is an important factor describing the biological status of canopies. This product is thus used by scientists for deriving plant and canopy parameters. Consultants use time series of the NDVI for advising farmers with best practice.For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-days maps." proprietary 936b319d-5253-425d-bd29-4b6ebce067ff_NA AVHRR - Land Surface Temperature (LST) - Europe, Nighttime FEDEO STAC Catalog 1998-02-23 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458046-FEDEO.umm_json "The ""Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)"" is a fixed grid map (in stereographic projection) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between –39.5°C and +87°C with a radiometric resolution of 0.5°C. A value of –40°C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/" proprietary 94421633457375 Aeromagnetic Survey - Regional Data SCIOPS STAC Catalog 1973-01-01 1987-01-01 -90, -90, -30, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214608606-SCIOPS.umm_json "The British Antarctic Survey (BAS) began regional aeromagnetic surveys over the Antarctic Peninsula in 1973. The first four seasons up to 1980, together with supplementary data from subsequent seasons, provided 36 000 line km of data "" north of 72 degrees S. The survey was extended southwards over southern Palmer Land and Ellsworth Land during 1986. Since 1980, activity has been concentrated farther south. In 1983, data were collected over the Ronne Ice Shelf as part of the BAS Weddell Province Project to investigate the relationship between East and West Antarctica. Two seasons have been completed with US logistical support during the joint BAS-United States Antarctic Research Programme (USARP) project investigating the structure and tectonic history of the area. As part of this work, data were collected from the area of the Ellsworth and Thiel mountains during 1984. Ellsworth Land, the Ellsworth Mountains and Bryan coast were covered during the final survey in 1987. Metadata records for each survey are available by following the Related_URL link to the BAS data catalogue." proprietary @@ -633,15 +628,13 @@ id title catalog state_date end_date bbox url description license 94f3670150de4bac90773806e26646f2_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Petermann Glacier between 2017-05-01 and 2017-09-14, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-04-30 2017-09-14 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143107-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Petermann Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-09-14. It has been produced as part of the ESA Greenland Ice sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway. proprietary 96159374900008 Alexander Island Microclimate Data SCIOPS STAC Catalog 1992-01-01 1997-01-01 -68, -72, -68, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214608608-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Alexander Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary 96159393396972 Adelaide Island Microclimate Data SCIOPS STAC Catalog 1995-01-01 1997-01-01 -68, -68, -68, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214608609-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Adelaide Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary -96d5b75ea29946c5aab8214ddbab252b_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8 FEDEO STAC Catalog 2009-03-31 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142712-FEDEO.umm_json The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary +96d5b75ea29946c5aab8214ddbab252b_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8 FEDEO STAC Catalog 2009-04-01 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142712-FEDEO.umm_json The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary 971dc69b-a7a8-406e-a5bc-fad76b51156f Cyclones Winds - Hazard, Wind Speed 500RP CEOS_EXTRA STAC Catalog 1970-01-01 -180, -55, 179.7721, 59.768925 https://cmr.earthdata.nasa.gov/search/concepts/C2232849324-CEOS_EXTRA.umm_json "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 500 years return period. It was generated by integration of the intensity values contained in the files ""Wind_Atlantic.AME"", ""Wind_EastPacific.AME"", ""Wind_NorthIndian.AME"", ""Wind_SudIndian.AME"", ""Wind_SudPacific.AME"" and ""Wind_WestPacific.AME"". " proprietary 9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA IRS-P6 Resourcesat-1 - Panchromatic Images (LISS-IV) - Europe, Mono Mode FEDEO STAC Catalog 2004-01-27 2010-01-01 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458052-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary -97aebb95404a4bde8405e9cf7e32b9f8_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142652-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 3.1 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary 97ea298b-c382-46ef-9e36-926dead6a19d_NA METOP GOME-2 - Tropospheric Nitrogen Dioxide (NO2) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458040-FEDEO.umm_json The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational NO2 tropospheric column products are generated using the algorithm GDP (GOME Data Processor) version 4.x for NO2 [Valks et al. (2011)] integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region using the DOAS method. An additional algorithm is applied to derive the tropospheric NO2 column: after subtracting the estimated stratospheric component from the total column, the tropospheric NO2 column is determined using an air mass factor based on monthly climatological NO2 profiles from the MOZART-2 model. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/ proprietary -99348189bd33459cbd597a58c30d8d10_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142544-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary 99b2c1d2-f2fd-4133-848a-8de849b958f7 Cyclones Winds - Hazard, Wind Speed 1000RP CEOS_EXTRA STAC Catalog 1970-01-01 -180, -55, 179.7721, 59.768925 https://cmr.earthdata.nasa.gov/search/concepts/C2232849339-CEOS_EXTRA.umm_json "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 1000 years return period. It was generated by integration of the intensity values contained in the files ""Wind_Atlantic.AME"", ""Wind_EastPacific.AME"", ""Wind_NorthIndian.AME"", ""Wind_SudIndian.AME"", ""Wind_SudPacific.AME"" and ""Wind_WestPacific.AME""." proprietary 9bdeb99d91a743fe84623264587ad043_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2013-2014, v1.0 FEDEO STAC Catalog 2014-01-21 2014-04-02 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142863-FEDEO.umm_json This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2013-2014, derived from RADARSAT-2 data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The ice velocity data were derived from intensity-tracking of RADARSAT-2 data aquired between 21/1/2014 and 02/04/2014. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the Eastings and Northings direction of the grid; the vertical displacement, derived from a digital elevation model, is also provided. Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. This product was generated by DTU Space - Microwaves and Remote Sensing. proprietary -9ed2813d2eda4d958e92ab3ce1ab1fe6_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2 FEDEO STAC Catalog 2009-05-31 2014-05-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143059-FEDEO.umm_json The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR). proprietary +9ed2813d2eda4d958e92ab3ce1ab1fe6_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2 FEDEO STAC Catalog 2009-06-01 2014-05-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143059-FEDEO.umm_json The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR). proprietary 9f002827ba7d48f59019fcfd3577a57e_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged CO2 Merged Product generated with the EMMA algorithm (CO2_EMMA), v2.2 FEDEO STAC Catalog 2009-05-31 2014-05-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143245-FEDEO.umm_json The CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v2.2, and forms part of the Climate Research Data Package 4.The EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This merged SCIAMACHY and GOSAT XCO2 Level 2 product is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR). proprietary 9f6324ebe92940b989ebf273d5f8bf33_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ADV Algorithm), Version 2.31 FEDEO STAC Catalog 2002-07-24 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142624-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on ENVISAT, derived using the ADV algorithm, version 2.31. Data is available for the period 2002-2012.For further details about these data products please see the linked documentation. proprietary A Fusion Dataset for Crop Type Classification in Germany_1 A Fusion Dataset for Crop Type Classification in Germany MLHUB STAC Catalog 2020-01-01 2023-01-01 13.6339485, 52.4179888, 14.3529903, 52.8494418 https://cmr.earthdata.nasa.gov/search/concepts/C2781412484-MLHUB.umm_json This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. proprietary @@ -2426,13 +2419,13 @@ ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_ECS STAC Catalo ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary ATL03_ANC_MASKS_1 ATLAS/ICESat-2 ATL03 Ancillary Masks, Version 1 NSIDCV0 STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2278879612-NSIDCV0.umm_json This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively. A detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references. proprietary -ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564427300-NSIDC_ECS.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL07QL_006 ATLAS/ICESat-2 L3A Sea Ice Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL08QL_006 ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary @@ -2447,12 +2440,12 @@ ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NS ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL13QL_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary -ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary -ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary +ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary -ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary +ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_ECS STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary +ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary @@ -2461,10 +2454,10 @@ ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Cata ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary -ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary -ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary +ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary +ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary ATL21_003 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753316241-NSIDC_CPRD.umm_json ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids. proprietary ATL21_003 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737912334-NSIDC_ECS.umm_json ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids. proprietary ATL22_003 ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.umm_json ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers. proprietary @@ -2486,11 +2479,11 @@ ATSM2STF_001 MISR L2 FIRSTLOOK TOA/Cloud Stereo Product subset for the ARCTAS re ATSMIB2E_003 MISR L1B2 Ellipsoid Product subset for the ARCTAS region V003 LARC STAC Catalog 2008-04-02 2008-07-24 -157, 54, -110, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1000000561-LARC.umm_json This file contains Ellipsoid-projected TOA Radiance subset for the ARCTAS region,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 proprietary ATSMIB2T_003 MISR L1B2 Terrain Product subset for the ARCTAS region V003 LARC STAC Catalog 2008-04-02 2008-07-24 -157, 54, -110, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1000000562-LARC.umm_json This file contains Terrain-projected TOA Radiance subset for the ARCTAS region,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22 proprietary ATSMIGEO_002 MISR Geometric Parameters subset for the ARCTAS region V002 LARC STAC Catalog 2008-04-02 2008-07-24 -157, 54, -110, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1000000541-LARC.umm_json This file contains the Geometric Parameters subset for the ARCTAS region which measures the sun and view angles at the reference ellipsoid proprietary -ATTREX-Aircraft_Radiation_Measurements_1 ATTREX-Aircraft_Radiation_Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056464-LARC_ASDC.umm_json ATTREX-Aircraft_Radiation_Measurements are in-situ radiation measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ radiation properties collected by the Solar Spectral Flux Radiometer (SSFR) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary -ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1 ATTREX-Aircraft_RemoteSensing_Temperature_Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056465-LARC_ASDC.umm_json ATTREX-Aircraft_RemoteSensing_Temperature_Measurements are remotely sensed temperature profiles collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of remotely sensed temperature profiles collected by the Microwave Temperature Profiler (MTP) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary -ATTREX-Aircraft_insitu_Cloud_property_Measurements_1 ATTREX-Aircraft_insitu_Cloud_property_Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056461-LARC_ASDC.umm_json ATTREX-Aircraft_insitu_Cloud_property_Measurements are in-situ cloud measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ cloud properties collected by the Hawkeye-FCDP (Hawkeye-Fast Cloud Droplet Probe) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary -ATTREX-Aircraft_insitu_TraceGas_Measurements_1 ATTREX-Aircraft_insitu_TraceGas_Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056462-LARC_ASDC.umm_json ATTREX-Aircraft_insitu_TraceGas_Measurements are in-situ trace gas measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ trace gas measurements collected by the Diode Laser Hygrometer (DLH), UCATS Gas Chromatograph, Advanced Whole Air Sampler (AWAS), Harvard University Picarro Cavity Ringdown Spectrometer, 2 channel internal path Tunable-Diode Laser (TDL) absorption spectrometer, and Dual-channel Ultraviolet (UV) absorption spectrometer for O3 measurements during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary -ATTREX-Aircraft_navigational_and_meteorological_Measurements_1 ATTREX-Aircraft_navigational_and_meteorological_Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056463-LARC_ASDC.umm_json ATTREX-Aircraft_navigational_meteorological_Measurements are in-situ navigational and meteorological measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ meteorological and navigational properties collected by the Meteorological Measurement System (MMS) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary +ATTREX-Aircraft_Radiation_Measurements_1 ATTREX Global Hawk UAS Radiation Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056464-LARC_ASDC.umm_json ATTREX-Aircraft_Radiation_Measurements are in-situ radiation measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ radiation properties collected by the Solar Spectral Flux Radiometer (SSFR) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary +ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1 ATTREX Global Hawk UAS Remote Sensing Temperature Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056465-LARC_ASDC.umm_json ATTREX-Aircraft_RemoteSensing_Temperature_Measurements are remotely sensed temperature profiles collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of remotely sensed temperature profiles collected by the Microwave Temperature Profiler (MTP) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary +ATTREX-Aircraft_insitu_Cloud_property_Measurements_1 ATTREX Global Hawk UAS In-Situ Cloud Property Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056461-LARC_ASDC.umm_json ATTREX-Aircraft_insitu_Cloud_property_Measurements are in-situ cloud measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ cloud properties collected by the Hawkeye-FCDP (Hawkeye-Fast Cloud Droplet Probe) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary +ATTREX-Aircraft_insitu_TraceGas_Measurements_1 ATTREX Global Hawk UAS In-Situ Trace Gas Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056462-LARC_ASDC.umm_json ATTREX-Aircraft_insitu_TraceGas_Measurements are in-situ trace gas measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ trace gas measurements collected by the Diode Laser Hygrometer (DLH), UCATS Gas Chromatograph, Advanced Whole Air Sampler (AWAS), Harvard University Picarro Cavity Ringdown Spectrometer, 2 channel internal path Tunable-Diode Laser (TDL) absorption spectrometer, and Dual-channel Ultraviolet (UV) absorption spectrometer for O3 measurements during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary +ATTREX-Aircraft_navigational_and_meteorological_Measurements_1 ATTREX Global Hawk UAS Meteorological and Navigational Measurements LARC_ASDC STAC Catalog 2011-10-01 2013-03-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1536056463-LARC_ASDC.umm_json ATTREX-Aircraft_navigational_meteorological_Measurements are in-situ navigational and meteorological measurements collected onboard the Global Hawk Uninhabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ meteorological and navigational properties collected by the Meteorological Measurement System (MMS) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete. Even though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedbacks. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively. proprietary ATom_AMP_Instrument_Data_1671_1 ATom: L2 In Situ Measurements of Aerosol Microphysical Properties (AMP) ORNL_CLOUD STAC Catalog 2016-07-29 2018-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3012767955-ORNL_CLOUD.umm_json This dataset provides the number, surface area, and volume concentrations and size distributions of dry aerosol particles measured by the Aerosol Microphysical Properties (AMP) instrument package during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. Five instruments--two nucleation-mode aerosol size spectrometers (NMASS), two ultra-high sensitivity aerosol spectrometers (UHSAS), and a laser aerosol spectrometer (LAS)--comprise the AMP package. The AMP payload provides size distributions with up to one-second time resolution for dry aerosol particles between 0.003 and 4.8 microns in diameter. proprietary ATom_AO2_Instrument_Data_V2_1880_2 ATom: L2 In Situ Measurements from the NCAR Airborne Oxygen Instrument (AO2), V2 ORNL_CLOUD STAC Catalog 2016-07-29 2018-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2677069277-ORNL_CLOUD.umm_json This dataset provides in situ atmospheric oxygen and carbon dioxide concentrations measured by the NCAR Airborne Oxygen Instrument (AO2) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The AO2 Instrument measures O2 concentration using a vacuum-ultraviolet absorption technique. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate. proprietary ATom_ATHOS_Instrument_Data_V2_1930_2 ATom: Measurements from Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS), V2 ORNL_CLOUD STAC Catalog 2016-07-29 2018-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2677118029-ORNL_CLOUD.umm_json This dataset provides the mixing ratios of hydrogen oxides measured by the Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS) during the ATom 1-4 campaigns. ATHOS uses laser-induced fluorescence (LIF) to measure hydroxide (OH) and hydroperoxyl (HO2) simultaneously. The measurements include OH and HO2 mixing ratios and the OH interference determined by chemical removal of OH. The reactivity of OH is measured by the OH Reactivity (OHR) instrument using the discharge flow method and is integrated into the ATHOS electronics. These data provide insights into the oxidative state of the global atmosphere. These data are useful for testing the oxidation chemistry in models and other analytical methods being developed to deduce the atmosphere's oxidative state. proprietary @@ -3233,11 +3226,11 @@ CAM5K30CF_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coe CAM5K30CF_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365285-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab Principal Component (PC) data and the number of PCs used. proprietary CAM5K30COVCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Covariances Climatology Monthly Global 0.25Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274450252-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global covariances climatology product (CAM5K30COVCLIM). The product is provided at 0.25 degree (~25 kilometer) resolution. The CAMEL covariance product includes the mean and variance of the covariance matrixes created for each month from 2003 through 2021 (19 years) on a 0.25 x 0.25 degree grid of 416 spectral points from the V003 CAMEL Emissivity product (CAM5K30EM). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30COVCLIM product are variables for the mean and variance of the emissivity, latitude, longitude, spectral frequencies, and number of observations. proprietary CAM5K30EMCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Climatology Monthly Global 0.05Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274448375-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global emissivity climatology product (CAM5K30EMCLIM). This 0.05 degree (~5 kilometer) resolution product represents the mean emissivity from 2003 through 2021 (19 years). Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Variables provided in the CAM5K30EMCLIM product include latitude, longitude, wavelength, number of samples used to calculate climatology, CAMEL quality flag, snow fraction derived from MODIS (MOD10), and CAMEL Emissivity. proprietary -CAM5K30EM_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266338-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information. proprietary -CAM5K30EM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365286-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information. proprietary +CAM5K30EM_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266338-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information. proprietary +CAM5K30EM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365286-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information. proprietary CAM5K30UCCLIM_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Climatology Monthly Global 0.05Deg V003 LPCLOUD STAC Catalog 2003-01-01 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3274446180-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) data suite has been expanded to include a monthly global uncertainty climatology product (CAM5K30UCCLIM). The product is provided at 0.05 degree (~5 kilometer) resolution. The 13 hinge-point uncertainty climatology is computed by taking an average over each available month from 2003 through 2021 (19 years) and includes three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty climatology is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Corresponding emissivity values can be found in the CAM5K30EMCLIM data product. Provided in the CAM5K30UCCLIM product are variables for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, and total uncertainty quality flag information. proprietary -CAM5K30UC_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266343-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. proprietary -CAM5K30UC_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365287-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. proprietary +CAM5K30UC_002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V002 LPCLOUD STAC Catalog 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266343-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. proprietary +CAM5K30UC_003 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V003 LPDAAC_ECS STAC Catalog 2000-03-01 2024-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600365287-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. proprietary CAMEX4_ER2_MAS_1 Fourth Convection and Moisture Experiment ER2 MODIS Airborne Simulator LARC_ASDC STAC Catalog 2001-08-13 2001-09-26 -121.13, 16.43, -61.8, 39.62 https://cmr.earthdata.nasa.gov/search/concepts/C1535862443-LARC_ASDC.umm_json The Convection And Moisture EXperiment (CAMEX) 4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-fundend aircraft and surface remote sensing instrumentation. These aircraft flew over, through, and around selected hurricanes as they approached landfall in the Caribbean, Gulf of Mexico, and along the East Coast of the United States. This study yields high spatial and temporal information of hurricane structure, dynamics, and motion. The data set contains the measurements collected by the MAS instrument onboard the ER2 aircraft. The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. The MAS spectrometer acquires high spatial resolution imagery in the range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range. A 50-channel digitizer which records all 50 spectral bands at 12 bit resolution became operational in January 1995. The MAS spectrometer is mated to a scanner sub-assembly which collects image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees. proprietary CAML_0 Cyanobacteria Aggregated Manual Labels OB_DAAC STAC Catalog 2013-01-04 2021-12-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2896484540-OB_DAAC.umm_json Continuous monitoring for cyanobacteria blooms in small, inland water bodies via in-situ sampling and analysis can be challenging not only due to the number and locations of water bodies to cover, but also due to the dynamic nature of algal growth and toxin production. Detection targets vary with cyanobacteria strains as well as physical, chemical, and biological factors. Ground monitoring also lacks consistency as sampling methods, frequency, and analytical techniques vary from region to region. However, remote sensing allows systematic data collection over a large area to identify regions with potential harmful algal growth. We introduce the Cyanobacteria Aggregated Manual Labels (CAML), a large dataset of in-situ cyanobacteria measurements for investigations of cyanobacteria detection and severity classification in inland water bodies across the United States. Relevant satellite imagery from publicly available endpoints are applicable to use when applying the CAML dataset to models. The dataset labels ground measurements of cyanobacteria cell counts at 23,570 points in U.S. inland water bodies over 2013 2021. Algorithms trained on this data could be used to estimate cyanobacteria cell counts in water bodies for timely water quality and public health interventions and to gain an understanding of environmental and anthropogenic factors associated with cyanobacteria incidence and proliferation. Data is provided in a comma-separated values (CSV) format. proprietary CAML_Project_Archive.CAML_DNA_Barcoding_1 Census of Antarctic Marine Life (CAML) Archive of Project Documentation - CAML_Project_Archive.CAML_DNA_Barcoding AU_AADC STAC Catalog 2005-01-01 2010-12-31 -180, -80, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1625714381-AU_AADC.umm_json The Census of Antarctic Marine Life (CAML) Project Archive is a collection of scanned documents, maps, videos, and other related material that comprise the organisation and management documentation associated with a major research project of international significance. CAML measured the distribution and abundance of life in the Southern Ocean around Antarctica so that future impacts of climate change and human activities can be better understood. CAML coordinated the largest-ever survey of the Southern Ocean with 18 voyages in Antarctic waters, and inventoried over 16,000 marine species with hundreds new to science, provided DNA barcodes for 1,500 species, and has so far produced more than 600 scientific publications. CAML is a key activity of the Scientific Committee on Antarctic Research (SCAR); a subproject of the Census of Marine Life (CoML); and was a major initiative of the 2007-2009 International Polar Year (IPY). proprietary @@ -4417,7 +4410,7 @@ CYGNSS_L3_MRG_V3.2_3.2 CYGNSS Level 3 MRG Science Data Record Version 3.2 POCLOU CYGNSS_L3_S1.0_1.0 CYGNSS Level 3 Storm Centric Grid Science Data Record Version 1.0 POCLOUD STAC Catalog 2018-08-05 2020-11-18 -180, 0, 0, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2205121698-POCLOUD.umm_json This dataset contains the Version 1.0 Cyclone Global Navigation Satellite System (CYGNSS) Level 3 Storm Centric Grid (SCG) Science Data Record (SDR) which provides the average wind speed combined from aggregated wind speed measurements made by the entire CYGNSS constellation whose specular points are located near a storm of interest in latitude, longitude and time. Data are provided on both a 0.1x0.1 degree latitude by longitude equirectangular grid and storm centric coordinates obtained from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. Storm centric coordinates are derived from the National Hurricane Center (NHC) Best Track dataset to produce a 6 hourly wind speed averaging window. A single netCDF-4 data file is produced for each storm. Each storm is uniquely identified by the year, storm basin, and a storm number. This dataset is intended for historical storm analysis, and as such, this dataset is periodically updated based on the availability of the NHC Best Track storm center information that is typically made available in April for the previous year's hurricane season. SCG files are produced for named storms, as defined by the NHC, that reach hurricane strength (i.e., having a maximum sustained wind speed of at least 65 knots). Due to the dependency on NHC Best Track data, the SCG files produced in this dataset are confined to storms in the Northern Hemisphere within the North Atlantic and East Pacific ocean regions. Wind speed inputs are provided by the CYGNSS Level 2 SDR Version 3.0 (https://doi.org/10.5067/CYGNS-L2X30 ). proprietary CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0 UCAR-CU CYGNSS Level 3 Soil Moisture Version 1.0 POCLOUD STAC Catalog 2017-03-18 -135, -38, 164, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2205122332-POCLOUD.umm_json The CYGNSS Level 3 Soil Moisture Product provides volumetric water content estimates for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics. The data were produced by CYGNSS investigators at the University Corporation for Atmospheric Research (UCAR) and the University Colorado at Boulder (CU), and derive from version 2.1 of the CYGNSS L1 SDR. The soil moisture algorithm uses collocated soil moisture retrievals from SMAP to calibrate CYGNSS observations from the same day. For a given location, a linear relationship between the SMAP soil moisture and CYGNSS reflectivity is determined and used to transform the CYGNSS observations into soil moisture. The data are archived in daily files in netCDF-4 format. Two soil moisture variables report the volumetric water content in units of cm3/cm3. The variable SM_subdaily includes up to four soil moisture estimates per day. Another variable SM_daily provides a daily average. The time series covers the period from March 2017 to present. proprietary CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2 CYGNSS Level 3 Soil Moisture Version 3.2 POCLOUD STAC Catalog 2018-08-01 -135, -38.15, 164, 38.15 https://cmr.earthdata.nasa.gov/search/concepts/C2927902887-POCLOUD.umm_json The CYGNSS Level 3 Soil Moisture V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. It estimates volumetric water content for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics from the V3.2 reflectivity measurements provided in the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.

The soil moisture retrieval algorithm is an update of the previous version developed by UCAR-CU using a linear regression of CYGNSS angle-normalized effective surface reflectivity trained against collocated SMAP soil moisture during the calibration period 8/1/2018 to 11/15/2023. The data are archived in daily files in netCDF-4 format. Volumetric soil moisture water content in units of cm3/cm3 is provided with two gridding resolutions, 9x9 km and 36x36 km. The variable SM_subdaily contains data reported in six hour intervals. The variable SM_daily provides a daily average. The time series covers the period from August 2018 to present. proprietary -CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2 UC Berkeley CYGNSS Level 3 Daily RWAWC Watermask Version 3.2 POCLOUD STAC Catalog 2018-08-01 -180, -37.4, 180, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C3168830666-POCLOUD.umm_json The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.

This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present. proprietary +CYGNSS_L3_UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2 UC Berkeley CYGNSS Level 3 Daily RWAWC Watermask Version 3.2 POCLOUD STAC Catalog 2018-08-01 -180, -37.4, 180, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C3168830666-POCLOUD.umm_json The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.

This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.

This product is recommended for operational use. For science applications, we recommend the use of the Berkeley-RWAWC monthly product instead: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1 Note that the daily product consist of maps constructed using the most recent 31 days of data to rapidly capture surface water dynamics without relying on historical data. While the oldest data within this 31 day-period is weighted less and replaced by newer observations as they become available, extreme flood events may still be detected with a delay due to the incorporation of prior days’ data into the algorithm. The incorporation of older data is necessary to maintain the spatial scale. proprietary CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1 UC Berkeley CYGNSS Level 3 Monthly RWAWC Watermask Version 3.1 POCLOUD STAC Catalog 2018-08-01 -180, -37.4, 180, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C2928282019-POCLOUD.umm_json The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.1 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.

This dataset is derived from version 3.1 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X31), and provides monthly binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with a 1-month latency. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in monthly files in netCDF-4 format and covers the period from August 2018 to present. proprietary CYGNSS_L3_V2.1_2.1 CYGNSS Level 3 Science Data Record Version 2.1 POCLOUD STAC Catalog 2017-03-18 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2251464847-POCLOUD.umm_json This dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) first time availability of wind speeds using the Geophysical Model Function (GMF) calibrated for Young Seas with Limited Fetch (YSLF) conditions; 2) inherits all other improvements made to the version 2.1 Level 2 data intended to improve the quality of the wind speed retrievals and uncertainty estimates. For a full list of improvements to the version 2.1 Level 2 data, please refer to the following dataset information page: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V2.1 proprietary CYGNSS_L3_V3.0_3.0 CYGNSS Level 3 Science Data Record Version 3.0 POCLOUD STAC Catalog 2018-08-01 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2251464874-POCLOUD.umm_json This dataset contains the Version 3.0 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L3X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.0 release inherits all improvements made to the version 3.0 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.0 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X30. proprietary @@ -4425,6 +4418,7 @@ CYGNSS_L3_V3.1_3.1 CYGNSS Level 3 Science Data Record Version 3.1 POCLOUD STAC C CYGNSS_L3_V3.2_3.2 CYGNSS Level 3 Science Data Record Version 3.2 POCLOUD STAC Catalog 2018-08-01 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2832196567-POCLOUD.umm_json This dataset contains the version 3.2 CYGNSS level 3 science data record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.1; https://doi.org/10.5067/CYGNS-L3X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).

The v3.2 L3 gridded wind speed product inherits the v3.2 L2 FDS data as input at the same temporal and spatial resolution as the Level 2 data, sampled on consistent 0.2 by 0.2 degree latitude by longitude grid cells. The L3 gridding algorithm is unchanged. Range Corrected Gain (RCG) has been added to the L3 netcdf files as a new data field.

The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement. proprietary CYGNSS_NOAA_L2_SWSP_25KM_V1.1_1.1 NOAA CYGNSS Level 2 Science Wind Speed 25-km Product Version 1.1 POCLOUD STAC Catalog 2017-05-01 2022-05-28 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2036882072-POCLOUD.umm_json This dataset contains the Version 1.1 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.1 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the first science-quality release produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported sample locations are determined by averaging the specular point locations falling within each 25 km grid cell. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR). proprietary CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2 NOAA CYGNSS Level 2 Science Wind Speed 25-km Product Version 1.2 POCLOUD STAC Catalog 2017-05-01 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2254232941-POCLOUD.umm_json This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR). proprietary +CZCS_L0_1 Nimbus-7 CZCS Level-0 Raw Science Data, version 1 OB_CLOUD STAC Catalog 1978-10-30 1986-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327156612-OB_CLOUD.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary CZCS_L1_1 Nimbus-7 Coastal Zone Color Scanner (CZCS) Data Regional Data OB_DAAC STAC Catalog 1978-10-30 1986-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034503-OB_DAAC.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary CZCS_L1_2 Nimbus-7 CZCS Level-1A Data, version 2 OB_CLOUD STAC Catalog 1978-10-30 1986-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3280967831-OB_CLOUD.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary CZCS_L2_OC_2014 Nimbus-7 Coastal Zone Color Scanner (CZCS) Ocean Color (OC) Regional Data OB_DAAC STAC Catalog 1978-10-30 1986-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034467-OB_DAAC.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary @@ -6127,21 +6121,21 @@ GEWEXSRB_Rel4_1-IP_Longwave_daily_local_1 GEWEX SRB Integrated Product (Rel-4_1) GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local_1 GEWEX SRB Integrated Product (Rel-4_1) Longwave Daily Average by Local Ocean-only Fluxes LARC_ASDC STAC Catalog 2010-01-01 2017-12-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2791474780-LARC_ASDC.umm_json GEWEXSRB_Rel4_1-IP_Longwave_daily_oceanonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Daily Average by local Ocean-only Fluxes data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with ocean-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. These data are averaged by local solar time from 3-hourly values. Data collection for this product is complete. proprietary GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local_1 GEWEX SRB Integrated Product (Rel-4_1) Longwave Monthly Average by local Land-only Fluxes LARC_ASDC STAC Catalog 1983-07-01 1987-12-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2791481349-LARC_ASDC.umm_json GEWEXSRB_Rel4_1-IP_Longwave_monthly_landonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by local data product. It contains land-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4- Integrated Product with land-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, Landflux surface, and MERRA-2 conditionally. The temporal range is July 1983 through December 1987. These are temporally averaged on local solar time. Data collection for this product is complete. proprietary GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local_1 GEWEX SRB Integrated Product (Rel-4_1) Longwave Monthly Average by local Ocean-only Fluxes LARC_ASDC STAC Catalog 2010-01-01 2017-06-30 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2791478791-LARC_ASDC.umm_json GEWEXSRB_Rel4_1-IP_Longwave_monthly_oceanonly_local is the Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) Integrated Product (Rel-4) Longwave Monthly Average by local Ocean-only data product. It contains ocean-only global fields of 26 longwave surface, Top of Atmosphere (TOA), and atmospheric profile radiative parameters derived with the Longwave algorithm of the NASA World Climate Research Programme/Global Energy and Water-Cycle Experiment (WCRP/GEWEX) Surface Radiation Budget (SRB) Project. This version is an extension of Release 4-Integrated Product with ocean-only fluxes due to a missing key input from the main data set. The fluxes include all-sky, clear-sky, and pristine-sky TOA upward fluxes (outgoing longwave radiation, OLR), all-sky, clear-sky, and pristine-sky upward and downward fluxes at the tropopause, 200hPa, 500hPa, and surface. A status flag of filled cloud properties is also included. Inputs to the longwave algorithm are cloud information based on ISCCP HXS, meteorology from ISCCP nnHIRS, SeaFlux SST and surface, and MERRA-2 conditionally. The temporal range is January 2010 through June 2017. Averaging is done by local solar time. Data collection for this product is complete. proprietary -GFCC30FCC_001 Global Forest Cover Change Forest Cover Change Multi-Year Global 30m V001 LPCLOUD STAC Catalog 1990-01-01 2004-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763259410-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary -GFCC30SR_001 Global Forest Cover Change Surface Reflectance Estimates Multi-Year Global 30m V001 LPCLOUD STAC Catalog 1984-03-12 2011-12-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261610-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available “leaf-on” date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during “leaf-off” seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection. For each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary -GFCC30TC_003 Global Forest Cover Change Tree Cover Multi-Year Global 30m V003 LPCLOUD STAC Catalog 2000-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266352-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary -GFCC30WC_001 Global Forest Cover Change Water Cover 2000 Global 30m V001 LPCLOUD STAC Catalog 1999-06-29 2003-01-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261619-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary +GFCC30FCC_001 Global Forest Cover Change Forest Cover Change Multi-Year Global 30m V001 LPCLOUD STAC Catalog 1990-01-01 2004-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763259410-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary +GFCC30SR_001 Global Forest Cover Change Surface Reflectance Estimates Multi-Year Global 30m V001 LPCLOUD STAC Catalog 1984-03-12 2011-12-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261610-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available “leaf-on” date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during “leaf-off” seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection. For each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary +GFCC30TC_003 Global Forest Cover Change Tree Cover Multi-Year Global 30m V003 LPCLOUD STAC Catalog 2000-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266352-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary +GFCC30WC_001 Global Forest Cover Change Water Cover 2000 Global 30m V001 LPCLOUD STAC Catalog 1999-06-29 2003-01-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261619-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). proprietary GFEI_CH4_1 Global Inventory of Methane Emissions from Fuel Exploitation V1 (GFEI_CH4) GES_DISC STAC Catalog 2016-01-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143386827-GES_DISC.umm_json This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries. The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning. proprietary GFOI_Boreno_Island GFOI_Borneo_Island CEOS_EXTRA STAC Catalog 1972-01-01 114, 1, 114, 1 https://cmr.earthdata.nasa.gov/search/concepts/C2231555055-CEOS_EXTRA.umm_json The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to:foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilising observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC). proprietary -GFSAD1KCD_001 Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001 LPCLOUD STAC Catalog 2007-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261621-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data. The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012. proprietary -GFSAD1KCM_001 Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001 LPCLOUD STAC Catalog 2007-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261632-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012. proprietary -GFSAD30AFCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-06-30 -37, -50.108476, 70.001218, 40.10861 https://cmr.earthdata.nasa.gov/search/concepts/C2763261633-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AFCE data product uses two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), and one object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary -GFSAD30AUNZCNMOCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 60, -62, 180, 60.095406 https://cmr.earthdata.nasa.gov/search/concepts/C2763261638-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary -GFSAD30EUCEARUMECE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261648-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary -GFSAD30NACE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2010 North America product 30 m V001 LPCLOUD STAC Catalog 2008-01-01 2015-12-31 -180, -0.001078, 1.928773, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261670-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over North America for nominal year 2010 at 30 meter resolution (GFSAD30NACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30NACE data product uses a combination of the pixel-based supervised classifier, Random Forest (RF), and the object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30NACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary -GFSAD30SAAFGIRCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, and Iran product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-06-30 39.998656, -10.038404, 100.001086, 40.10861 https://cmr.earthdata.nasa.gov/search/concepts/C2763261689-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary -GFSAD30SACE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 -110, -60, -20, 15 https://cmr.earthdata.nasa.gov/search/concepts/C2763261708-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary -GFSAD30SEACE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 70, -45, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763261715-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD1KCD_001 Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001 LPCLOUD STAC Catalog 2007-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261621-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security Support Analysis Data (GFSAD) Crop Dominance Global 1 kilometer (km) dataset was created using multiple input data including: Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation, and Moderate Resolution Imaging Spectrometer (MODIS) remote sensing data; crop type data, secondary elevation data; 50-year precipitation and 20-year temperature data; reference sub-meter to 5 meter resolution ground data; and country statistic data. The GFSAD1KCD data were produced for nominal 2010 by overlaying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011, Biradar et al., 2009) to ultimately create eight classes of crop dominance. The GFSAD1KCD nominal 2010 product is based on data ranging from years 2007 through 2012. proprietary +GFSAD1KCM_001 Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001 LPCLOUD STAC Catalog 2007-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261632-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012. proprietary +GFSAD30AFCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-06-30 -37, -50.108476, 70.001218, 40.10861 https://cmr.earthdata.nasa.gov/search/concepts/C2763261633-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AFCE data product uses two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), and one object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD30AUNZCNMOCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 60, -62, 180, 60.095406 https://cmr.earthdata.nasa.gov/search/concepts/C2763261638-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD30EUCEARUMECE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261648-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD30NACE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2010 North America product 30 m V001 LPCLOUD STAC Catalog 2008-01-01 2015-12-31 -180, -0.001078, 1.928773, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763261670-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over North America for nominal year 2010 at 30 meter resolution (GFSAD30NACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30NACE data product uses a combination of the pixel-based supervised classifier, Random Forest (RF), and the object-oriented classifier, Recursive Hierarchical Image Segmentation (RHSEG). The classifiers retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30NACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD30SAAFGIRCE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, and Iran product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-06-30 39.998656, -10.038404, 100.001086, 40.10861 https://cmr.earthdata.nasa.gov/search/concepts/C2763261689-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD30SACE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 -110, -60, -20, 15 https://cmr.earthdata.nasa.gov/search/concepts/C2763261708-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary +GFSAD30SEACE_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia product 30 m V001 LPCLOUD STAC Catalog 2013-01-01 2016-12-31 70, -45, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763261715-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. proprietary GFSAD30VAL_001 Global Food Security-support Analysis Data (GFSAD) Cropland Extent-Product 2015 Validation 30 m V001 LPDAAC_ECS STAC Catalog 2008-01-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1432078714-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30 Validation (GFSAD30VAL) data product provides a thorough and independent accuracy assessment and validation of the cropland extent products produced for each of the seven regions. The accuracy assessment and validation process utilizes a cluster of 3 by 3 pixels of 30 meter data to resample the product to 90 meter resolution. Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe. proprietary GGD200_1 Borehole temperatures in deep wells of Western Siberia, Russia, 1960-1995, Version 1 NSIDCV0 STAC Catalog 1960-01-01 1995-12-31 67, 60, 80, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1386206546-NSIDCV0.umm_json This data set is a database of the permafrost and geothermal conditions of the oil and gas deposits of Western Siberia. Data were taken from 736 plots, each having from one to ten wells. The data set includes soil and rock temperatures at 20, 50, 100, 200, 300, 400, 500, 1000, and 3000 meters; depth of the bedding of the top and bottom of permafrost layers; size of the thermal flows in the subpermafrost; and thickness of frozen layers and underlying thawed layers. Additional information includes the geographical coordinates of the sites, the air temperature, permafrost-geothermal geological sections, maps of thermal flows, and the distribution of the temperatures at each depth (down to 5000 meters). The data are presented on the CAPS Version 1.0 CD-ROM, June 1998. proprietary GGD222_1 Active layer and permafrost properties, including snow depth, soil temperature, and soil moisture, Barrow, Alaska, Version 1 NSIDCV0 STAC Catalog 1962-01-01 1993-12-31 -156.78872, 71.29058, -156.78872, 71.29058 https://cmr.earthdata.nasa.gov/search/concepts/C1386206550-NSIDCV0.umm_json This data set contains soil temperature, soil moisture, thaw depth, and snow depth data collected at test sites near Barrow, Alaska, during the following years. Soil temperature data - 1963-1966, 1993 Soil moisture data - 1963 Thaw depth - 1962-1968, 1991-1993 Snow depth - 1963-1964 This study focused on characterizing the active soil layer at Barrow, and determining the relationships between and among these physical properties at permafrost sites in the Arctic. This site is U1 of the IPA's Circumpolar Active Layer Monitoring (CALM) Program and later measurements are available at the CALM Web site. proprietary @@ -6174,32 +6168,32 @@ GHISACONUS_001 Global Hyperspectral Imaging Spectral-library of Agricultural cro GIMMS3g_NDVI_Trends_1275_1 Long-Term Arctic Growing Season NDVI Trends from GIMMS 3g, 1982-2012 ORNL_CLOUD STAC Catalog 1982-06-01 2012-08-31 -180, 20, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784897341-ORNL_CLOUD.umm_json This data set provides normalized difference vegetation index (NDVI) data for the arctic growing season derived primarily with data from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard several NOAA satellites over the years 1982 through 2012. The NDVI data, which show vegetation activity, were averaged annually for the arctic growing season (GS; June, July and August). The products include the annual GS-NDVI values and the results of a cumulative GS-NDVI time series trends analysis. The data are circumpolar in coverage at 8-km resolution and limited to greater than 20 degrees N.These normalized difference vegetation index (NDVI) trends were calculated using the third generation data set from the Global Inventory Modeling and Mapping Studies (GIMMS 3g). GIMMS 3g improves on its predecessor (GIMMS g) in three important ways. First, GIMMS 3g integrates data from NOAA-17 and 18 satellites to lengthen its record. Second, it addresses the spatial discontinuity north of 72 degrees N, by using SeaWiFS, in addition to SPOT VGT, to calibrate between the second and third versions of the AVHRR sensor (AVHRR/2 and AVHRR/3). Finally, the GIMMS 3g algorithm incorporates improved snowmelt detection and is calibrated based on data from the shorter, arctic growing season (May-September) rather than the entire year (January-December). proprietary GISS-CMIP5_1 GISS ModelE2 contributions to the CMIP5 archive NCCS STAC Catalog 0850-01-01 2100-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1542315069-NCCS.umm_json We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era (1980-2004) taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics. proprietary GIS_EastAngliaClimateMonthly_551_1 Global Monthly Climatology for the Twentieth Century (New et al.) ORNL_CLOUD STAC Catalog 1900-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780535151-ORNL_CLOUD.umm_json A 0.5 degree lat/lon data set of monthly surface climate over global land areas, excluding Antarctica. Primary variables are interpolated directly from station time-series: precipitation, mean temperature and diurnal temperature range. proprietary -GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary +GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary -GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary -GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary +GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary +GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549166-NSIDC_CPRD.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary -GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary -GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary +GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary -GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary +GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary +GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary -GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary +GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153551318-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary GLAH15_034 GLAS/ICESat L2 Ocean Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153552369-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary @@ -6631,6 +6625,7 @@ GloSSAC_2.1 Global Space-based Stratospheric Aerosol Climatology Version 2.1 LAR GloSSAC_2.2 Global Space-based Stratospheric Aerosol Climatology Version 2.2 LARC_ASDC STAC Catalog 1979-01-01 2021-12-31 180, -80, -180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2284299943-LARC_ASDC.umm_json The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 43-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an ‘as available’ basis. proprietary GloSSAC_2.21 Global Space-based Stratospheric Aerosol Climatology Version 2.21 LARC_ASDC STAC Catalog 1979-01-01 2022-12-31 180, -80, -180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2705848058-LARC_ASDC.umm_json The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 44-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an ‘as available’ basis. proprietary GloSSAC_2.22 Global Space-based Stratospheric Aerosol Climatology Version 2.22 LARC_ASDC STAC Catalog 1979-01-01 2023-12-31 180, -80, -180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2935447251-LARC_ASDC.umm_json The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, is a 44-year climatology of stratospheric aerosol properties focused on extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005 and later from mid-2017 and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. Data from other space instruments and from ground-based, air and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an ‘as available’ basis. proprietary +GlobFireCarbon_1 Global Fire carbon emissions from CMS-Flux inversions assimilating atmospheric carbon monoxide observations GES_DISC STAC Catalog 2010-01-01 2023-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3278574317-GES_DISC.umm_json This dataset provides the Carbon Flux for Fires. The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning. proprietary Global_Biomass_1950-2010_1296_1 Global 1-degree Maps of Forest Area, Carbon Stocks, and Biomass, 1950-2010 ORNL_CLOUD STAC Catalog 1950-01-01 2010-12-31 -180, -70, 180, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2763367695-ORNL_CLOUD.umm_json This data set provides global forest area, forest growing stock, and forest biomass data at 1-degree resolution for the period 1950-2010. The data set is based on a compilation of forest area and growing stock data reported in international assessments performed by FAO, MCPFE (now Forest Europe), and UNECE. Data of different assessments are to the extent possible harmonized to reflect both forest area and other wooded land, to be comparable between countries and assessments. proprietary Global_CDOM_0 Global colored dissolved organic matter (CDOM) measurements OB_DAAC STAC Catalog 2005-08-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360247-OB_DAAC.umm_json Measurements of CDOM (colored dissolved organic matter) in the central equatorial Pacific Ocean in 2005 and 2006. proprietary Global_Clumping_Index_1531_1 Global 500-m Foliage Clumping Index Data Derived from MODIS BRDF, 2006 ORNL_CLOUD STAC Catalog 2006-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2767477592-ORNL_CLOUD.umm_json This dataset provides global clumping index (CI) data for 2006 derived from the MODIS Bidirectional Reflectance Distribution Function (BRDF) data product. Clumping index is a key structural parameter of plant canopies which represents the degree of foliage grouping within distinct canopy structures relative to a random distribution. The data are provided at substantially higher resolution (500-m) than existing clumping index data products. proprietary @@ -6731,7 +6726,10 @@ HI545_hydrographic_survey_1 Hydrographic survey HI545 by the RAN Australian Hydr HI560_hydrographic_survey_1 Hydrographic survey HI560 by the RAN Australian Hydrographic Service at Casey, December 2014 to February 2015 AU_AADC STAC Catalog 2014-12-04 2015-02-04 110.3639, -66.3119, 110.5697, -66.2328 https://cmr.earthdata.nasa.gov/search/concepts/C1214313547-AU_AADC.umm_json The RAN Australian Hydrographic Service conducted hydrographic survey HI560 at Casey, December 2014 to February 2015. The survey was conducted jointly with Geoscience Australia. The survey area was offshore from Clark Peninsula south to Beall Island, but not including Newcomb Bay and O'Brien Bay which were surveyed in 2013/14 (see the metadata record with ID HI545_hydrographic_survey). A multibeam sonar system was used. The survey dataset, which includes the Report of Survey, was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The RAN Australian Hydrographic Service team was lead by LCDR G.A.Walker. The data are not suitable for navigation. proprietary HI607_hydrographic_survey_1 Mawson Station - Hydrographic Survey 2017-18 AU_AADC STAC Catalog 2018-01-09 2018-01-09 62.8601, -67.6079, 62.8839, -67.5957 https://cmr.earthdata.nasa.gov/search/concepts/C1968847747-AU_AADC.umm_json This terrestrial dataset was collected at Ursula Harris’s behest by Craig Hamilton and a Naval Survey team on 09 January 2018 when sea conditions prevented the team from taking bathymetric measurements. This survey was intended to fill gaps in the existing Mawson Station survey data and includes 29 previously unrecorded features comprised of bollards, HF towers, flagpoles, masts, antennae, ionosonde transmitter and receiver, the Mawson Signpost and the Douglas Mawson Bust. proprietary HI634_hydrographic_survey_1 Hydrographic survey HI634 by the RAN Australian Hydrographic Service at Davis, February 2020 AU_AADC STAC Catalog 2020-02-06 2020-02-14 77.941667, -68.586111, 77.983333, -68.563889 https://cmr.earthdata.nasa.gov/search/concepts/C1834759931-AU_AADC.umm_json The Australian Antarctic Division identified areas that required hydrographic surveying. (See map available in the download at \Plans and Instructions\HPS Supplied Data\davis_plan_2019_2020 version 5.1.pdf and a shapefile of the identified areas at FSD\ArcGIS\Pink V2\AOI_Unproject_wgs84.shp) A team from the Maritime Geospatial Warfare Unit, of the Australian Hydrographic Service, was at Davis in early February 2020. Single beam and side scanning survey data was collected on the water, beach profiles collected and rock data. Single beam and side scanning survey data Areas A, D, F, H, I, J and K were ice free. Area J was further broken down into four areas, J1, J2, J3 and J4. Areas A, D and F were thoroughly surveyed with 10m mainline spacing with 20m X-line spacing. Areas I, J3 and J4 were surveyed but due to time constraints were surveyed at approximately 40m line spacing to provide 200% sea floor coverage with the SSS to detect any features dangerous to navigation with one shoal detected in area I which is mentioned in Section I. Area H was too shallow to survey at any other time except high tide and it was decided to focus on other areas as the survey of this area would not value add to the required results of the survey. Area J1, J2 and K were not surveyed due to time constraints. RTK corrections or access to the CORS network couldn't be made to the CEESCOPE survey system. Instead positioning during the survey was recorded exclusively with the NovaTel GNSS 850 Antenna. No post processing was conducted. The team wasn't able to determine why the CEESCOPE was unable to connect to the CORS network or Base Station to gain RTK corrections, despite considerable effort spent problem solving and conducting a number of trials. Tide data collected was applied to the data and all tidal information is explained in section F of the report. A map showing the surveyed areas can be found in the report. Raw data in caris format is available from the Australian Hydrographic Office (AHO). Sounding data, stored as a shapefile, is available as a download file. Beach profiles Sites were also surveyed with 5m line spacing to maximise seafloor coverage, at 5 beach locations, 4 in area A and 1 in Area F. ArcGIS projects and PDF documents displaying the depth data and significant rocks are included in the download. Please note the ArcGIS projects do not include the AHO chart, due to distribution restrictions on digital charts. It is included in the PDF documents. These documents refer to images taken from the survey boat and spreadsheets displaying gradients data. Rock data A shapefile recording conspicuous rocks as well as photographs is available for downloading. Bench mark positions were reclaimed using Trimble R10 and post processed with AUSPOS. Abbreviations used in the download directories ROS = Report of Survey, FSD = Final Survey Data A detailed report can be found at /ROS/ Projection……..…...…...………….….……..Universal Transverse Mercator (UTM) Zone 43 South Horizontal Datum……………………………World Geodetic System 1984 (WGS84) Vertical Datum…………………………….....Approximated Lowest Astronomical Tide (LAT) Sounding Depths.……………………………Metres (m) Survey Date………………..………………….6th - 18th Feb 2020 Bathymetric Accuracy Horizontal……………± 0.8m Bathymetric Accuracy Vertical………………±0.46m Sounding Density……………………………..2m Surface Chart Reference………………………………AUS 451, 602​ ITRF 2014 and GRS80 were utilised for static observations of bench marks and levelling to the tide pole for establishment of approximate LAT. Hypack v19.1.11.0 which was used to gather all bathymetric data does not have the option to use the ITRF datum and the WGS84 Datum was used. proprietary +HICO_L0_1 ISS HICO Level-0 Raw Science Data, version 1 OB_CLOUD STAC Catalog 2009-09-25 2014-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327154959-OB_CLOUD.umm_json The Hyperspectral Imager for the Coastal Ocean (HICO™) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world. proprietary +HICO_L1_2 ISS HICO Level-1B Data, version 2 OB_CLOUD STAC Catalog 2009-09-25 2014-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327154985-OB_CLOUD.umm_json The Hyperspectral Imager for the Coastal Ocean (HICO™) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world. proprietary HICO_L1_2 ISS Hyperspectral Imager for the Coastal Ocean (HICO) L1 Full-Resolution Calibrated Science Data OB_DAAC STAC Catalog 2009-09-25 2014-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1562007964-OB_DAAC.umm_json The Hyperspectral Imager for the Coastal Ocean (HICO™) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world. proprietary +HICO_L2_OC_2022.0 ISS HICO Level-2 Regional Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 2009-09-25 2014-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327155006-OB_CLOUD.umm_json The Hyperspectral Imager for the Coastal Ocean (HICO™) is an imaging spectrometer based on the PHILLS airborne imaging spectrometers. HICO is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 90 m with full spectral coverage (380 to 960 nm sampled at 5.7 nm) and a very high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO demonstrates coastal products including water clarity, bottom types, bathymetry and on-shore vegetation maps. Each year HICO collects approximately 2000 scenes from around the world. The current focus is on providing HICO data for scientific research on coastal zones and other regions around the world. proprietary HIC_NMOrthos_1 Heard Island Coastal Orthophotos derived from Non-Metric Photography AU_AADC STAC Catalog 1980-01-01 1987-12-31 73.23761, -53.2011, 73.85834, -52.96022 https://cmr.earthdata.nasa.gov/search/concepts/C1214313562-AU_AADC.umm_json The orthophoto is a rectified georeferenced image of the Heard Island Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric Hasselblad and Linhof cameras (focal length unknown). The photos are between 17 MB and 193 MB each, and are in tiff format with associated world files. proprietary HIC_PHOTO_NMOrtho_TopoMapping_1 Heard Island Topographic Mapping from Orthophotos derived from Non-Metric Photography AU_AADC STAC Catalog 1980-01-01 1987-12-31 73.24, -53.21, 73.9, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214313525-AU_AADC.umm_json The Heard Island Topographic Data was mapped from Ortho-rectified non-metric photography. The data consists of Coastline, Glacier, Lagoon, Offshore Rocks, Water Storage and Watercourse datasets digitised from the photography, all of which are available for download at the url given below. proprietary HIMI_Demersal_Fish_1 HIMI Demersal Fish Update and Environmental Covariates AU_AADC STAC Catalog 1990-05-23 1993-09-24 71, -53, 77, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1219925456-AU_AADC.umm_json "This dataset contains demersal fish data from 181 trawls from the HIMS 1989-90 (AADC-0075), FISHOG 1991-92 (AADC-00080) and THIRST 1993-94 (AADC-00073) surveys as well as a selection of matching environmental data, most obtained from the Polar_Environmental_Data AADC dataset. The dataset IDs above contain the full metadata for each survey and many environmental layers. The fish data has been modified from the data in the Historical Fish Data database in the following ways: 1) Fish abundances are those recorded in the trawl logs (i.e. not limited to the ~200 measured and weighed for abundant species) 2) Trawls noted as having issues in log books or without complete coverage of the selected environmental covariates were omitted 3) Species that were rare (present at less than 8 sites) were omitted, leaving 15 species. The dataset consists of a spreadsheets called fish.csv. It contains fish abundance records and associated environmental covariates for each haul Below are descriptions of field headers in the fish.csv file as well as source, units and resolution for environmental covariates Column Name Description Units Resolution Source 1 Champsocephalus_gunnari Species abundance Count NA AAD Historical Fish Database checked against trawl logs 2 Lepidonotothen_squamifrons Species abundance Count NA AAD Historical Fish Database checked against trawl logs 3 Channichthys_rhinoceratus Species abundance Count NA AAD Historical Fish Database checked against trawl logs 4 Dissostichus_eleginoides Species abundance Count NA AAD Historical Fish Database checked against trawl logs 5 Macrourus_holotrachys Species abundance Count NA AAD Historical Fish Database checked against trawl logs 6 Paradiplospinus_gracilis Species abundance Count NA AAD Historical Fish Database checked against trawl logs 7 Bathyraja_eatonii Species abundance Count NA AAD Historical Fish Database checked against trawl logs 8 Zanclorhynchus_spinifer Species abundance Count NA AAD Historical Fish Database checked against trawl logs 9 Lycodapu_antarcticus Species abundance Count NA AAD Historical Fish Database checked against trawl logs 10 Bathyraja_murrayi Species abundance Count NA AAD Historical Fish Database checked against trawl logs 11 Lepidonotothen_mizops Species abundance Count NA AAD Historical Fish Database checked against trawl logs 12 Muraenolepis_sp Species abundance Count NA AAD Historical Fish Database checked against trawl logs 13 Paraliparis_operculosus Species abundance Count NA AAD Historical Fish Database checked against trawl logs 14 Gobionotothen_acuta Species abundance Count NA AAD Historical Fish Database checked against trawl logs 15 Melanostigma_gelatinosum Species abundance Count NA AAD Historical Fish Database checked against trawl logs 16 Latitude Latitude of trawl midpoint Decimal degree NA Midpoint of trawl from trawl logs 17 Longitude Longitude of trawl midpoint Decimal degree NA Trawl logs 18 Geomorphology Geomorphology Bank, Plateau, Plateau_slope 0.1 degree Polar_Environmental_Data 19 Season Season/year Autumn_1990, Summer_1992, Spring_1993 NA Trawl logs 20 Seafloor_temperature Average temperature near seafloor from CAISOM model Degrees C 0.1 degree Polar_Environmental_Data 21 SST_spatial_gradient Spatial gradient of sst_summer_climatology (from MODIS 2002-2010) Degrees C/ km 0.1 degree Polar_Environmental_Data 22 SST_summer_climatology Average austral summer SST (from MODIS 2002-2010) Degrees C 0.1 degree Polar_Environmental_Data 23 Seafloor_salinity Average bottom salinity PSU 0.5 degree CSIRO Atlas of Regional Seas: www.cmar.csiro.au/cars 24 Seafloor_salinity_SD Standard deviation of bottom salinity PSU 0.5 degree CSIRO Atlas of Regional Seas: www.cmar.csiro.au/cars 25 Surface_temperature Average of daily surface temperature Degrees C 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)* 26 Surface_temperature_var Variance in daily surface temperature Degrees C 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)* 27 Surface_temperature_var_ds Variance after removing seasonal cycle (detrended) in daily surface temperature Degrees C 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)* 28 Surface_temperature_trend Slope of linear regression over timeseries Degrees C/ year 0.25 degree Calculated from NOAA OI SST v2 (1982- 2014)* 29 Chlorophyll_a_yearly_mean Standard deviation of annual chla mean mg/m3 0.2 degree mean of yearly mean chl-a (1997-2010) from corrected L3 SeaWiFs data ^ 30 Chlorophyll_a_yearly_SD Average of annual chla mean mg/m3 0.2 degree mean of yearly mean chl-a (1997-2010) from corrected L3 SeaWiFs data ^ 31 Haul_depth Average depth of haul m NA Trawl logs 32 Bathymetry_slope Seafloor slope Degrees 0.1 degree Polar_Environmental_Data 33 Seafloor_current_speed Average current speed near seafloor from CAISOM model m/s2 0.1 degree Polar_Environmental_Data 34 Chlorophyll_a_summer_climatology Average austral summer chla concentration mg/m3 0.1 degree Polar_Environmental_Data 35 Haul_Index Haul Identifier NA AAD Historical Fish Database ^ Daily SeaWiFs values corrected for the Southern Ocean using: Johnson, R., et al. (2013). ""Three improved satellite chlorophyll algorithms for the Southern Ocean."" Journal of Geophysical Research: Oceans 118(7): 3694-3703. *NOAA High Resolution SST data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/ * Reynolds, Richard W., Thomas M. Smith, Chunying Liu, Dudley B. Chelton, Kenneth S. Casey, Michael G. Schlax, 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496. Reynolds, Richard W., Thomas M. Smith, Chunying Liu, Dudley B. Chelton, Kenneth S. Casey, Michael G. Schlax, 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496." proprietary @@ -11656,8 +11654,8 @@ OMCLDO2Z_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) Zoomed 1-Or OMCLDO2_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 1-Orbit L2 Swath 13x24km V003 (OMCLDO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966787-GES_DISC.umm_json The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day. proprietary OMCLDO2_CPR_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 200-km swath subset along CloudSat track V003 (OMCLDO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350939-GES_DISC.umm_json This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR) proprietary OMCLDRRG_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMCLDRRG) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136100-GES_DISC.umm_json This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes. proprietary -OMCLDRR_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary OMCLDRR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/ proprietary +OMCLDRR_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary OMCLDRR_004 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159637081-GES_DISC.umm_json This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary OMCLDRR_CPR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 200-km swath subset along CloudSat track V003 (OMCLDRR_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350980-GES_DISC.umm_json This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR) proprietary OMDOAO3G_003 OMI/Aura Ozone (O3) DOAS Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMDOAO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136103-GES_DISC.umm_json This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size. proprietary @@ -11747,8 +11745,8 @@ OMTO3_003 OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT OM OMTO3_003 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary OMTO3_CPR_003 OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350982-GES_DISC.umm_json This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003) proprietary OMTO3d_003 OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction L3 1 day 1 degree x 1 degree V3 (OMTO3d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136070-GES_DISC.umm_json The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes. proprietary -OMTO3e_003 OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.umm_json The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) . proprietary OMTO3e_003 OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.umm_json The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. proprietary +OMTO3e_003 OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.umm_json The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) . proprietary OMUANC_004 Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUANC) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2556143653-GES_DISC.umm_json The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official. proprietary OMUFPITMET_003 GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km V3 (OMUFPITMET) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1561222825-GES_DISC.umm_json The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMUFPITMET by combining layers above the troposphere. The OMUFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official. proprietary OMUFPMET_004 GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUFPMET) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2556146042-GES_DISC.umm_json The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official. proprietary @@ -11824,137 +11822,34 @@ PACE_EPH_DEF_1 PACE Definitive Ephemeris Data Data, V1 OB_CLOUD STAC Catalog 202 PACE_HARP2_L0_D1_1 PACE HARP2 Level-0 Detector 1 (D1) Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798238-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary PACE_HARP2_L0_D2_1 PACE HARP2 Level-0 Detector 2 (D2) Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798239-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary PACE_HARP2_L0_D3_1 PACE HARP2 Level-0 Detector 3 (D3) Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798240-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L0_REAL_1 PACE HARP2 Level-0 Real-time Direct Transfer Mode Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798243-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L0_SCI_1 PACE HARP2 Level-0 Science Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798245-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1A_AST0_2 PACE HARP2 Level-1A Acquisition Scheme Type 0 - Full-Resolution Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026579577-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1A_AST1_2 PACE HARP2 Level-1A Acquisition Scheme Type 1 - Half-Resolution Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026579667-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1A_AST2_2 PACE HARP2 Level-1A Acquisition Scheme Type 2 -  Science Mode (no MTDI) Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026579735-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1A_AST3_2 PACE HARP2 Level-1A Acquisition Scheme Type 3 Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026579815-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1A_AST4_2 PACE HARP2 Level-1A Acquisition Scheme Type 4 - Science Mode Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026579888-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary PACE_HARP2_L1A_SCI_2 PACE HARP2 Level-1A Science Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026579942-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1B_AST4_2 PACE HARP2 Level-1B Acquisition Scheme Type 4 - Science Mode Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026580004-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary PACE_HARP2_L1B_SCI_2 PACE HARP2 Level-1B Science Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026580118-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary -PACE_HARP2_L1C_AST4_2 PACE HARP2 Level-1C Acquisition Scheme Type 4 - Science Mode Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026580193-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary PACE_HARP2_L1C_SCI_2 PACE HARP2 Level-1C Science Data, V2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026580280-OB_CLOUD.umm_json The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape. proprietary PACE_HKT_1 PACE Spacecraft Housekeeping, NetCDF format Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2832273136-OB_CLOUD.umm_json The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. proprietary PACE_HSK_1 PACE Spacecraft Housekeeping Data, V1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2869693107-OB_CLOUD.umm_json The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. proprietary -PACE_OCI_L0_DARK_1 PACE OCI Level-0 Dark Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798299-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L0_DIAG_1 PACE OCI Level-0 Diagnostic/Calibaration Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798302-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_LIN_1 PACE OCI Level-0 Linearity Calibration Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798304-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_LUN_1 PACE OCI Level-0 Lunar Calibration Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798306-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_RAW_1 PACE OCI Level-0 Raw Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798308-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L0_SCI_1 PACE OCI Level-0 Science Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798309-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_SNAPI_1 PACE OCI Level-0 Snapshot Internal Trigger Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798315-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_SNAPX_1 PACE OCI Level-0 Snapshot External Trigger Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798322-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_SOLD_1 PACE OCI Level-0 Daily Solar Calibration Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798329-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_SOLM_1 PACE OCI Level-0 Monthly Solar Calibration Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798338-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_SPEC_1 PACE OCI Level-0 Spectral Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798347-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L0_STAT_1 PACE OCI Level-0 Static Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2804798354-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L1A_SCI_2 PACE OCI Level-1A Science Data, version 2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026581050-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L1B_SCI_2 PACE OCI Level-1B Science Data, version 2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026581092-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L1C_SCI_2 PACE OCI Level-1C Science Data, version 2 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3026581150-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L2_AER_DB_2.0 PACE OCI Level-2 Regional Aerosol Optical Properties, Deep Blue Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920056-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L2_AER_DB_NRT_2.0 PACE OCI Level-2 Regional Aerosol Optical Properties, Deep Blue - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920010-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_AER_DT_2.0 PACE OCI Level-2 Regional Aerosol Optical Properties, Dark Target Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920135-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L2_AER_DT_NRT_2.0 PACE OCI Level-2 Regional Aerosol Optical Properties, Dark Target - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920095-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_AOP_2.0 PACE OCI Level-2 Regional Apparent Optical Properties Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920242-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L2_AOP_NRT_2.0 PACE OCI Level-2 Regional Apparent Optical Properties - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920190-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_BGC_2.0 PACE OCI Level-2 Regional Biogeochemical Properties Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920337-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L2_BGC_NRT_2.0 PACE OCI Level-2 Regional Biogeochemical Properties, Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920290-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_CLD_2.0 PACE OCI Level-2 Regional Cloud Optical Properties Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920454-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L2_CLD_MASK_3.0 PACE OCI Level-2 Regional Cloud Mask Data, version 3.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288168499-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L2_CLD_MASK_NRT_3.0 PACE OCI Level-2 Regional Cloud Mask - Near Real-time (NRT) Data, version 3.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288168490-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_CLD_NRT_2.0 PACE OCI Level-2 Regional Cloud Optical Properties - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920417-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_IOP_2.0 PACE OCI Level-2 Regional Inherent Optical Properties (IOP) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920547-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L2_IOP_NRT_2.0 PACE OCI Level-2 Regional Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920493-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_LAND_2.0 PACE OCI Level-2 Regional Normalized Difference Vegetation Index Land Reflectance Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920658-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L2_LAND_NRT_2.0 PACE OCI Level-2 Regional Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920606-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_PAR_2.0 PACE OCI Level-2 Regional Photosynthetically Active Radiation (PAR) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920783-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L2_PAR_NRT_2.0 PACE OCI Level-2 Regional Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920715-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L2_SFREFL_2.0 PACE OCI Level-2 Regional Surface Reflectance Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920963-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L2_SFREFL_NRT_2.0 PACE OCI Level-2 Regional Surface Reflectance - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020920858-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AER_DBL_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Land Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921155-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AER_DBL_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Land - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921049-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AER_DBO_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Ocean Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921424-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AER_DBO_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Ocean - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921356-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AER_DB_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921490-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AER_DB_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921257-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AER_DTL_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Land Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921591-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AER_DTL_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Land - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921529-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AER_DTO_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Ocean Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921819-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AER_DTO_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Ocean - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921758-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AER_DT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921884-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AER_DT_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921665-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AOT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Thickness Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921996-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_AOT_NRT_2.0 PACE OCI Level-3 Global Binned Aerosol Optical Thickness (AOT), Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020921937-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_AVW_2.0 PACE OCI Level-3 Global Binned Apparent Visible Wavelength (AVW) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922135-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_CARBON_2.0 PACE OCI Level-3 Global Binned Carbon Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922231-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_CARBON_NRT_2.0 PACE OCI Level-3 Global Binned Carbon, Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922208-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_CHL_2.0 PACE OCI Level-3 Global Binned Chlorophyll (CHL) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922315-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_CHL_NRT_2.0 PACE OCI Level-3 Global Binned Chlorophyll (CHL) - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922264-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_CLD_MASK_3.0 PACE OCI Level-3 Global Binned Cloud Mask Data, version 3.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288168508-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_CLD_MASK_NRT_3.0 PACE OCI Level-3 Global Binned Cloud Mask - Near Real-time (NRT) Data, version 3.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288168501-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_CLD_TOP_2.0 PACE OCI Level-3 Global Binned Cloud Optical Properties, Cloud Top Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922412-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_CLD_TOP_NRT_2.0 PACE OCI Level-3 Global Binned Cloud Optical Properties, Cloud Top - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922362-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_FLH_2.0 PACE OCI Level-3 Global Binned Fluorescence Line Height (FLH) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922494-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_FLH_NRT_2.0 PACE OCI Level-3 Global Binned Fluorescence Line Height (FLH) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922458-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_IOP_2.0 PACE OCI Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922583-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_IOP_NRT_2.0 PACE OCI Level-3 Global Binned Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922543-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_KD_2.0 PACE OCI Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922664-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_KD_NRT_2.0 PACE OCI Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922624-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_LAND_2.0 PACE OCI Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922842-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_LAND_NRT_2.0 PACE OCI Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922797-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_PAR_2.0 PACE OCI Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922915-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_PAR_NRT_2.0 PACE OCI Level-3 Global Binned Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922875-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_PIC_2.0 PACE OCI Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922977-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3B_PIC_NRT_2.0 PACE OCI Level-3 Global Binned Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020922940-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_POC_2.0 PACE OCI Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923051-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_POC_NRT_2.0 PACE OCI Level-3 Global Binned Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923019-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_RRS_2.0 PACE OCI Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923122-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_RRS_NRT_2.0 PACE OCI Level-3 Global Binned Remote-Sensing Reflectance (RRS) - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923086-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3B_SFREFL_2.0 PACE OCI Level-3 Global Binned Surface Reflectance Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923200-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3B_SFREFL_NRT_2.0 PACE OCI Level-3 Global Binned Surface Reflectance - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923163-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AER_DBL_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Land Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923267-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AER_DBL_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Land - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923237-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AER_DBO_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Ocean Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923375-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AER_DBO_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Ocean - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923343-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AER_DB_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923412-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AER_DB_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923313-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AER_DTL_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Land Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923480-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AER_DTL_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Land - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923448-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AER_DTO_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Ocean Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923609-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AER_DTO_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Ocean - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923569-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AER_DT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923646-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AER_DT_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923531-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AOT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Thickness Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923739-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_AOT_NRT_2.0 PACE OCI Level-3 Global Mapped Aerosol Optical Thickness (AOT), Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923694-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_AVW_2.0 PACE OCI Level-3 Global Mapped Apparent Visible Wavelength (AVW) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923814-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_AVW_NRT_2.0 PACE OCI Level-3 Global Mapped Apparent Visible Wavelength (AVW) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923779-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_CARBON_2.0 PACE OCI Level-3 Global Mapped Carbon Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923871-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_CARBON_NRT_2.0 PACE OCI Level-3 Global Mapped Carbon, Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923838-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_CHL_2.0 PACE OCI Level-3 Global Mapped Chlorophyll (CHL) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923954-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_CHL_NRT_2.0 PACE OCI Level-3 Global Mapped Chlorophyll (CHL) - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923919-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_CLD_MASK_3.0 PACE OCI Level-3 Global Mapped Cloud Mask Data, version 3.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288168556-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_CLD_MASK_NRT_3.0 PACE OCI Level-3 Global Mapped Cloud Mask - Near Real-time (NRT) Data, version 3.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288168526-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_CLD_TOP_2.0 PACE OCI Level-3 Global Mapped Cloud Optical Properties, Cloud Top Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924017-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_CLD_TOP_NRT_2.0 PACE OCI Level-3 Global Mapped Cloud Optical Properties, Cloud Top - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020923989-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_FLH_2.0 PACE OCI Level-3 Global Mapped Fluorescence Line Height (FLH) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924092-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_FLH_NRT_2.0 PACE OCI Level-3 Global Mapped Fluorescence Line Height (FLH) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924054-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_IOP_2.0 PACE OCI Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924182-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_IOP_NRT_2.0 PACE OCI Level-3 Global Mapped Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924144-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_KD_2.0 PACE OCI Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924251-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_KD_NRT_2.0 PACE OCI Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924216-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_LAND_2.0 PACE OCI Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924359-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_LAND_NRT_2.0 PACE OCI Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924301-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_PAR_2.0 PACE OCI Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924438-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_PAR_NRT_2.0 PACE OCI Level-3 Global Mapped Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924399-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_PIC_2.0 PACE OCI Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924510-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_PIC_NRT_2.0 PACE OCI Level-3 Global Mapped Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924478-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_POC_2.0 PACE OCI Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924599-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary -PACE_OCI_L3M_POC_NRT_2.0 PACE OCI Level-3 Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924558-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_RRS_2.0 PACE OCI Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924694-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_RRS_NRT_2.0 PACE OCI Level-3 Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924646-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary -PACE_OCI_L3M_SFREFL_2.0 PACE OCI Level-3 Global Mapped Surface Reflectance Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924782-OB_CLOUD.umm_json The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. proprietary PACE_OCI_L3M_SFREFL_NRT_2.0 PACE OCI Level-3 Global Mapped Surface Reflectance - Near Real-time (NRT) Data, version 2.0 OB_CLOUD STAC Catalog 2024-02-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3020924734-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary PACE_SPEXONE_L0_1 PACE SPEXone Level-0 Data, version 1 OB_CLOUD STAC Catalog 2024-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2816780240-OB_CLOUD.umm_json The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere. proprietary PACE_SPEXONE_L1A_SCI_3 PACE SPEXone Level-1A Science Data, version 3 OB_CLOUD STAC Catalog 2024-02-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3294162788-OB_CLOUD.umm_json The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere. proprietary @@ -12978,18 +12873,18 @@ SPL1C_S0_HiRes_METADATA_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_METADATA_V003 ASF S SPL1C_S0_HiRes_QA_001_1 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214474435-ASF.umm_json SMAP Level 1C Sigma Naught High Res Data Quality Info proprietary SPL1C_S0_HiRes_QA_002_2 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243255360-ASF.umm_json SMAP Level 1C Sigma Naught High Res Data Quality Info Version 2 proprietary SPL1C_S0_HiRes_QA_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243140611-ASF.umm_json SMAP Level 1C Sigma Naught High Res Data Quality Info Version 3 proprietary -SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary +SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary +SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMP_008 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V008 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2136471610-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary SPL2SMP_E_005 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V005 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2136471686-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary -SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary +SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2024-11-21 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary @@ -13001,11 +12896,11 @@ SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMP_008 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V008 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2136471705-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMP_E_005 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V005 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2136471727-NSIDC_ECS.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary @@ -13013,8 +12908,8 @@ SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary -SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary +SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPOT-6.and.7.ESA.archive_9.0 SPOT-6 and 7 ESA archive ESA STAC Catalog 2012-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336951-ESA.umm_json The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products. SPOT 6 and 7 Primary and Ortho products can be available in the following modes: Panchromatic image at 1.5m resolution Pansharpened colour image at 1.5m resolution Multispectral image in 4 spectral bands at 6m resolution Bundle (1.5m panchromatic image + 6m multispectral image) Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/SPOT6-7 available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided. proprietary @@ -13076,14 +12971,14 @@ SRTMGL1_NC_003 NASA Shuttle Radar Topography Mission Global 1 arc second NetCDF SRTMGL1_NUMNC_003 NASA Shuttle Radar Topography Mission Global 1 arc second Number NetCDF V003 LPDAAC_ECS STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C3261871055-LPDAAC_ECS.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) version SRTM, which includes the global 1 arc second (~30 meter) product. SRTMGL1_NUMNC is used along with the SRTMGL1_NC data product and offers the number count in NetCDF. NASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and flew for 11 days. SRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60° North (N) and 56° South (S) latitude. This accounts for about 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary SRTMGL30_002 NASA Shuttle Radar Topography Mission Global 30 arc second V002 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763266346-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSURES) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 30 arc second (~1,000 meter) product. The NASA SRTM product with sample spacing of 3 arc second (~90 meter) generated by a 3 X 3 averaging of the 1 arc second data are then 10 X 10 averaged to produce thirty 30 arc second (~1,000 meter) data to correspond with Global 30 Arc Second Elevation (GTOPO30). (See the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.1.4.) The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. proprietary SRTMGL3N_003 NASA Shuttle Radar Topography Mission Global 3 arc second number V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268440-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) number product. Ancillary one-byte (0 to 255) “NUM” (number) files were produced for NASA SRTM Version 3. These files have names corresponding to the elevation files, except with the extension “.NUM” (such as N37W105.NUM). The elevation files use the extension “.HGT”, meaning height (such as N37W105.HGT). The separate NUM file indicates the source of each DEM pixel; the number of ASTER scenes used (up to 100), if ASTER; and the number of SRTM data takes (up to 24), if SRTM. The NUM file for both 3 arc second products (whether sampled or averaged) references the 3 x 3 center pixel. Note that NUMs less than 6 are water and those greater than 10 are land. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf). The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary -SRTMGL3S_003 NASA Shuttle Radar Topography Mission Global 3 arc second sub-sampled V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268442-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) sub-sampled product. The 3 arc second data was derived from the 1 arc second using sampling methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTMGL3 data were sub-sampled from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension “.HGT”, meaning height (such as N37W105.SRTMGL3S.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (http://dx.doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM). The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary +SRTMGL3S_003 NASA Shuttle Radar Topography Mission Global 3 arc second sub-sampled V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268442-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) sub-sampled product. The 3 arc second data was derived from the 1 arc second using sampling methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTMGL3 data were sub-sampled from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension “.HGT”, meaning height (such as N37W105.SRTMGL3S.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (http://dx.doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM). The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary SRTMGL3_003 NASA Shuttle Radar Topography Mission Global 3 arc second V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763266377-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using averaging methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf). The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTMGL3 data were generated from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension “.HGT”, meaning height (such as N37W105.SRTMGL3.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM). The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. proprietary SRTMGL3_NC_003 NASA Shuttle Radar Topography Mission Global 3 arc second NetCDF V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763266381-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) SRTM, which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. SRTMGL3_NC offers the data product in NetCDF. NASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and flew for 11 days. SRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60° North (N) and 56° South (S) latitude. This accounts for about 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary SRTMGL3_NUMNC_003 NASA Shuttle Radar Topography Mission Global 3 arc second Number NetCDF V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763266390-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) SRTM, which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. SRTMGL3_NUMNC is used along with the SRTMGL3_NC data product and offers the number count in NetCDF. NASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and flew for 11 days. SRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60° North (N) and 56° South (S) latitude. This accounts for about 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary SRTMGLOBAL1N Shuttle Radar Topography Mission 1-arc second Global USGS_LTA STAC Catalog 2000-02-11 2000-02-22 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1220567890-USGS_LTA.umm_json The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour February 11-22, 2000. The National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) participated in an international project to acquire radar data which were used to create the first near-global set of land elevations. The radars used during the SRTM mission were actually developed and flown on two Endeavour missions in 1994. The C-band Spaceborne Imaging Radar and the X-Band Synthetic Aperture Radar (X-SAR) hardware were used on board the space shuttle in April and October 1994 to gather data about Earth's environment. The technology was modified for the SRTM mission to collect interferometric radar, which compared two radar images or signals taken at slightly different angles. This mission used single-pass interferometry, which acquired two signals at the same time by using two different radar antennas. An antenna located on board the space shuttle collected one data set and the other data set was collected by an antenna located at the end of a 60-meter mast that extended from the shuttle. Differences between the two signals allowed for the calculation of surface elevation. Endeavour orbited Earth 16 times each day during the 11-day mission, completing 176 orbits. SRTM successfully collected radar data over 80% of the Earth's land surface between 60° north and 56° south latitude with data points posted every 1 arc-second (approximately 30 meters). Two resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive: 1 arc-second (approximately 30-meter) high resolution elevation data offer worldwide coverage of void filled data at a resolution of 1 arc-second (30 meters) and provide open distribution of this high-resolution global data set. Some tiles may still contain voids. The SRTM 1 Arc-Second Global (30 meters) data set will be released in phases starting September 24, 2014. Users should check the coverage map in EarthExplorer to verify if their area of interest is available. 3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60° north and 56° south latitude. [Summary provided by the USGS.] proprietary -SRTMIMGM_003 NASA Shuttle Radar Topography Mission Combined Image Data Set V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268443-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) combined (merged) image data product. (See User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.2.) The combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary -SRTMIMGR_003 NASA Shuttle Radar Topography Mission Swath Image Data V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268444-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects)) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) swath (raw) image data product. (See [User Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.1) The SRTM swath image data set consists of radar image files containing brightness values, as well as quality assurance (incidence angle) files for each of four overlapping sub-swaths that passes through a 1 degree by 1 degree tile. Data from each sub-swath is included as a separate file. Some files may contain only partial data; however, every image pixel acquired by SRTM is included in this data set. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary -SRTMSWBD_003 NASA Shuttle Radar Topography Mission Water Body Data Shapefiles & Raster Files V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268445-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. proprietary +SRTMIMGM_003 NASA Shuttle Radar Topography Mission Combined Image Data Set V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268443-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) combined (merged) image data product. (See User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.2.) The combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary +SRTMIMGR_003 NASA Shuttle Radar Topography Mission Swath Image Data V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268444-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) swath (raw) image data product. (See [User Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.1) The SRTM swath image data set consists of radar image files containing brightness values, as well as quality assurance (incidence angle) files for each of four overlapping sub-swaths that passes through a 1 degree by 1 degree tile. Data from each sub-swath is included as a separate file. Some files may contain only partial data; however, every image pixel acquired by SRTM is included in this data set. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Improvements/Changes from Previous Versions * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). proprietary +SRTMSWBD_003 NASA Shuttle Radar Topography Mission Water Body Data Shapefiles & Raster Files V003 LPCLOUD STAC Catalog 2000-02-11 2000-02-21 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2763268445-LPCLOUD.umm_json The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/about/competitive-programs/measures) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. proprietary SSBUVIRR_008 Shuttle SBUV (SSBUV) Solar Spectral Irradiance V008 (SSBUVIRR) at GES DISC GES_DISC STAC Catalog 1989-10-19 1996-01-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1273652226-GES_DISC.umm_json The Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites. Data are available in an ASCII text format. UV irradiance data are available for the following days from the eight missions: Flight #1: 1989 October 19, 20, 21 Flight #2: 1990 October 7, 8, 9 Flight #3: 1991 August 3, 4, 5, 6 Flight #4: 1992 March 29, 30 Flight #5: 1993 April 9, 11, 13, 15, 16 Flight #6: 1994 March 14, 15, 17 Flight #7: 1994 November 5, 7, 10, 13 Flight #8: 1996 January 12, 16, 18 The Shuttle SBUV (SSBUV) instrument measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. These data demonstrate the evolution of short-term solar UV activity during solar cycle 22. proprietary SSBUVO3_008 Shuttle SBUV (SSBUV) Level 2 Ozone Profile and Total Column, Aerosol Index, and UV-Reflectivity V008 (SSBUVO3) at GES DISC GES_DISC STAC Catalog 1989-10-19 1996-01-18 -180, -57, 180, 58 https://cmr.earthdata.nasa.gov/search/concepts/C1273652228-GES_DISC.umm_json The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods: Flight #1: 1989 October 19, 20, 21. Flight #2: 1990 October 7, 8, 9. Flight #3: 1991 August 3, 4, 5, 6. Flight #4: 1992 March 29, 31. Flight #5: 1993 April 9, 11, 13, 15, 16. Flight #6: 1994 March 14, 15, 17. Flight #7: 1994 November 5, 7, 10, 13. Flight #8: 1996 January 12, 16, 18. SSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb. proprietary SSDP_HAZARD_EARTHQUAKE Earthquakes and Planning for Ground Rupture Hazards CEOS_EXTRA STAC Catalog 1970-01-01 -116, 33, -115.5, 33.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553786-CEOS_EXTRA.umm_json Detailed maps bring a greater resolution to the number and locations of active faults. Preparing maps at a higher resolution requires extensive field study, and with a GIS, information, such as tract and parcel data, utility corridors, and flood hazard zones, can be incorporated to help decision makers in locating remediation facilities. After the Sylmar earthquake in 1972, building codes were strengthened, and the Alquist-Priolo Special Studies Zone Act was passed. Its purpose is to mitigate the hazard of fault rupture by prohibiting the location of most human occupancy structures across the traces of active faults. Earthquake fault zones are regulatory zones that encompass surface traces of active faults with a potential for future surface fault rupture. The zones are generally established about 500 feet on either side of the surface trace of active faults. Active faults and strips of state-mandated zoning along faults (Alquist-Priolo zones) riddle the Salton Sea Basin. The primary fault, the San Andreas, steps from the northeast side of the Salton Sea across the southern end, along a series of poorly understood faults, to the Brawley and Imperial fault systems. This stepover region has not had a historic ground-rupturing earthquake. Alquist-Priolo zones could not be defined because the faults are not well-located. Faults parallel to, and splaying from, the San Andreas are also capable of major earthquakes. Initial plans for remediation facilities take into account the generalized information (at 1:750,000 scale) on active faults, and the fault maps do not provide information on strong ground shaking. The shaking can damage facilities that lie far from an earthquake epicenter and far from active faults. Information on near-surface materials is required to estimate the ground-shaking hazards. proprietary @@ -15122,12 +15017,12 @@ VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1 GHRSST Level 3C North Atlantic Regional (NAR VIIRS_Val_FLKeys_0 VIIRS validation measurements made in the Florida Keys OB_DAAC STAC Catalog 2016-03-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360693-OB_DAAC.umm_json Measurements made in the Florida Keys as part of efforts to Validate the VIIRS instrument. proprietary VIIRS_Validation_0 VIIRS Validation measurements OB_DAAC STAC Catalog 2014-05-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360694-OB_DAAC.umm_json VIIRS Validation measurements. proprietary VIMS_2005_0 Virginia Institute of Marine Science 2005 optical measurements OB_DAAC STAC Catalog 2005-07-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360695-OB_DAAC.umm_json Measurements made along the York River by the Virginia Institute of Marine Science. proprietary -VIP01_004 Vegetation Index and Phenology (VIP) Vegetation Indices Daily Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268446-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. proprietary -VIP07_004 Vegetation Index and Phenology (VIP) Vegetation Indices 7Days Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268449-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP07 VI data product is a composite of seven daily images with 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP07 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. proprietary -VIP15_004 Vegetation Index and Phenology (VIP) Vegetation Indices 15Days Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268450-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP15 VI data product is provided twice monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP15 VI product contains 12 Science Datasets (SDS) which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. The VIP15 dataset includes two composites per month. The first composite is generated from day 1 to 15, and the second composite includes the remaining days of the month. This dataset consists of 24 files per year. proprietary -VIP30_004 Vegetation Index and Phenology (VIP) Vegetation Indices Monthly Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268451-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year. proprietary -VIPPHEN_EVI2_004 Vegetation Index and Phenology (VIP) Phenology EVI-2 Yearly Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268453-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year. proprietary -VIPPHEN_NDVI_004 Vegetation Index and Phenology (VIP) Phenology NDVI Yearly Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268456-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data. proprietary +VIP01_004 Vegetation Index and Phenology (VIP) Vegetation Indices Daily Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268446-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. proprietary +VIP07_004 Vegetation Index and Phenology (VIP) Vegetation Indices 7Days Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268449-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP07 VI data product is a composite of seven daily images with 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP07 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. proprietary +VIP15_004 Vegetation Index and Phenology (VIP) Vegetation Indices 15Days Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268450-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP15 VI data product is provided twice monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP15 VI product contains 12 Science Datasets (SDS) which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available. The VIP15 dataset includes two composites per month. The first composite is generated from day 1 to 15, and the second composite includes the remaining days of the month. This dataset consists of 24 files per year. proprietary +VIP30_004 Vegetation Index and Phenology (VIP) Vegetation Indices Monthly Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268451-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year. proprietary +VIPPHEN_EVI2_004 Vegetation Index and Phenology (VIP) Phenology EVI-2 Yearly Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268453-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year. proprietary +VIPPHEN_NDVI_004 Vegetation Index and Phenology (VIP) Phenology NDVI Yearly Global 0.05Deg CMG V004 LPCLOUD STAC Catalog 1981-01-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268456-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/about/competitive-programs/measures) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data. proprietary VIRGAS_MetNav_AircraftInSitu_Data_1 VIRGAS WB-57 Aircraft In-Situ Meteorology and Navigation Data LARC_ASDC STAC Catalog 2015-10-24 2015-11-02 -110, 10, -75, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2179058095-LARC_ASDC.umm_json VIRGAS_MetNav_AircraftInSitu_Data are the meteorology and navigational data collected during the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) sub-orbital campaign. Data from the Meteorological Measurement System (MMS) are featured in this data product and data collection is complete. Conducted in October 2015, the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) field campaign had a primary objective to test instrument capability and readiness for deployment in the investigation of major volcanic eruptions. VIRGAS aimed to enable researchers to assess the impact of these volcanic eruptions on stratospheric aerosols and the ozone layer. As sulfur dioxide is a characteristic component of volcanic emissions, the LIF SO2 instrument was of critical importance to VIRGAS. VIRGAS was conducted in one deployment consisting of six science flights based from Houston, TX. The current available data products are from the NOAA LASER-Induced Fluorescence (LIF SO2) instrument, the NOAA Unmanned Aircraft System O3 Photometer (UASO3), and NASA’s Meteorological Measurement System (MMS). The ASDC houses data including 1 Hz SO2 data from seven flights, 1 Hz O3 data from ten flights, and 1 Hz and 20 Hz data for temperature, pressure, and 3-D winds from 5 flights. VIRGAS was led by Dr. Karen Rosenlof and Dr. Ru-Shan Gao of the NOAA Chemical Sciences Laboratory (NOAA CSL), as well as by Dr. Paul Newman of NASA Godard Space Flight Center’s Earth Sciences Division. Other participants include researchers from NASA Ames Research Center, the Bay Area Environmental Research Institute (BAERI), and the University of Miami. proprietary VIRGAS_TraceGas_AircraftInSitu_Data_1 VIRGAS WB-57 Aircraft In-Situ Trace Gas Data LARC_ASDC STAC Catalog 2015-10-17 2015-11-01 -110, 10, -75, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2179058097-LARC_ASDC.umm_json VIRGAS_TraceGas_AircraftInSitu_Data are the in-situ trace gas data collected during the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) sub-orbital campaign. Data from the whole air sampler, NOAA UASO3 and Laser Induced Fluorescence - SO2 (LIF-SO2) are featured in this data product. Data collection is complete. Conducted in October 2015, the Volcano-plume Investigation Readiness and Gas-phase and Aerosol Sulfur (VIRGAS) field campaign had a primary objective to test instrument capability and readiness for deployment in the investigation of major volcanic eruptions. VIRGAS aimed to enable researchers to assess the impact of these volcanic eruptions on stratospheric aerosols and the ozone layer. As sulfur dioxide is a characteristic component of volcanic emissions, the LIF SO2 instrument was of critical importance to VIRGAS. VIRGAS was conducted in one deployment consisting of six science flights based from Houston, TX. The current available data products are from the NOAA LASER-Induced Fluorescence (LIF SO2) instrument, the NOAA Unmanned Aircraft System O3 Photometer (UASO3), and NASA’s Meteorological Measurement System (MMS). The ASDC houses data including 1 Hz SO2 data from seven flights, 1 Hz O3 data from ten flights, and 1 Hz and 20 Hz data for temperature, pressure, and 3-D winds from 5 flights. VIRGAS was led by Dr. Karen Rosenlof and Dr. Ru-Shan Gao of the NOAA Chemical Sciences Laboratory (NOAA CSL), as well as by Dr. Paul Newman of NASA Godard Space Flight Center’s Earth Sciences Division. Other participants include researchers from NASA Ames Research Center, the Bay Area Environmental Research Institute (BAERI), and the University of Miami. proprietary VISSRGOES1IMIR_001 VISSR/GOES-1 Infrared Imagery on 70mm Film V001 (VISSRGOES1IMIR) at GES DISC GES_DISC STAC Catalog 1976-01-27 1976-10-28 155, -90, -25, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2386986323-GES_DISC.umm_json VISSRGOES1IMIR is the Visible Infrared Spin-Scan Radiometer (VISSR) Infrared Imagery on 70mm Film data product from the first Geostationary Operational Environmental Satellite (GOES-1). This set of IR imagery (10.5 to 12.5 micrometer) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title on the top boundary and a 33-level gray scale on the right boundary that represents brightness temperatures. It may have a combination of the following options: 1) contrast enhancement, 2) image sectorization, and 3) 1/16-size imagery. The maximum effective size covers 500 sq km, represented by 4000 by 3904 pixels. Each element has a maximum resolution of 3.7 km. The title contains the satellite identification, picture number, picture type, coordinate numbers of the top left pixel relative to the visible sensor, start time of sectorized image, and pixel scaling and sector size identification. The GOES-1 satellite was parked over the equator at longitude 115W on Dec 18, 1975 viewing the hemisphere below the satellite. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00247 (old ID 75-100A-01B). proprietary @@ -15582,17 +15477,18 @@ Zobler_Soil_649_1 SAFARI 2000 Soil Types, 0.5-Deg Grid (Modified Zobler) ORNL_CL ZonalFlux_0 Measurements from the western equatorial Pacific Ocean OB_DAAC STAC Catalog 1996-04-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360710-OB_DAAC.umm_json Measurements taken in the western equatorial Pacific Ocean in 1996. proprietary a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0 An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing ENVIDAT STAC Catalog 2023-01-01 2023-01-01 8.2071304, 47.5210264, 8.2382011, 47.543743 https://cmr.earthdata.nasa.gov/search/concepts/C3226081770-ENVIDAT.umm_json Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice. proprietary a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0 A numerical solver for heat and mass transport in snow based on FEniCS ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.8472494, 46.812044, 9.8472494, 46.812044 https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.umm_json This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics proprietary +a0782135bcd04d77a1dae4aa71fba47c_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360338-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary a0d9764a3068439b997c42928ef739d2_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2 FEDEO STAC Catalog 1992-01-27 2010-06-13 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142504-FEDEO.umm_json This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010. It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage. It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity. The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH). proprietary +a13994c5-8d10-4627-90b8-60077ab5de40_NA EnMAP HSI - Level 0 / Quicklook Images - Global FEDEO STAC Catalog 2022-04-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3326864967-FEDEO.umm_json The EnMAP HSI L0 Quicklooks collection contains the VNIR and SWIR quicklook images as well as the quality masks for haze, cloud, or snow; based on the latest atmospheric correction methodology of the land processor. It allows users to get an overview which L0 data has been acquired and archived since the operational start of the EnMAP mission and which data is potentially available for on-demand processing into higher level products with specific processing parameters via the EOWEB-GeoPortal. The database is constantly updated with newly acquired L0 data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth’s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making. For more information, please see the mission website: https://www.enmap.org/mission/ proprietary a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA Resourcesat-2 - Multispectral Images (LISS-IV) - Europe, Multispectral Mode FEDEO STAC Catalog 2004-01-18 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458025-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary -a386504aa8ae492f9f2af04c109346e9_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): A database of coastal sea level anomalies and associated trends from Jason satellite altimetry from 2002 to 2018 FEDEO STAC Catalog 2002-01-15 2018-05-30 -30, -45, 160, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2548142874-FEDEO.umm_json This dataset contains 17-year-long (June 2002 to May 2018 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of six regions: Mediterranean Sea, Northeast Atlantic, West Africa, North Indian Ocean, Southeast Asia and Australia. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from the ESA SL_cci+ v1.1 dataset of coastal sea level anomalies (also available in the catalogue, DOI:10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005), which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series. This large amount of coastal sea level estimates has been further analysed to produce the present dataset: it consists in a selection of 429 portions of satellite tracks crossing land for which valid sea level time series are provided at monthly interval together with the associated sea level trends over the 17-year time span at each along-track 20-Hz point, from 20 km offshore to the coast.The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset has a DOI: https://doi.org/10.17882/74354 proprietary a6efcb0868664248b9cb212aba44313d_NA ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO STAC Catalog 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142742-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation. proprietary a78f0eb5-a146-4129-9066-519378e22fd8_1 IUCN PROTECTED AREAS OF AFRICA CEOS_EXTRA STAC Catalog 1986-01-01 1986-01-01 -17.3, -34.6, 51.1, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2232848904-CEOS_EXTRA.umm_json "The Protected Areas of Africa were provided by the International Union for the Conservation of Nature and Natural Resources (IUCN) - World Conservation Monitoring Center (WCMC) of Gland, Switzerland and Cambridge, UK, to UNEP/GRID-Geneva for digitization into computer form in 1986. The map was digitized in ARC/INFO and subsequently rasterized to a two-minute cell size in the ELAS software format. Today, the same data set resides at GRID on an IBM mainframe computer, but it has not been updated since the initial work was carried out.* The Protected Areas of Africa data set shows a series of 11 different types of parks, reserves and other unique areas which had some degree of protected status. The various types of Protected Areas are all shown as squares of varying size on a background map of Africa, with four square sizes which are proportional to the actual size of each area, and with center points approximately equal to the actual central location of each Protected Area. Thus, this data set is perhaps most useful for showing the general distribution of African Protected Areas by type, circa 1986. There are two versions of the Protected Areas of Africa data set (two data files) at UNEP/GRID-Geneva. Because there is significant overlap between the Protected Area squares, the first shows the various squares superimposed with the size of Protected Areas used as the criterion for which take precedence over others. The second version shows Protected Areas ranked by importance; that is, squares take precedence according to the order in which they appear in the legend, with the more highly ranked Protected Areas overlaying others of lower rank. Following is the legend which applies to the Protected Areas of Africa (categories were formulated by IUCN-WCMC): Values Category of Protected Area ------ -------------------------- 1 Scientific Reserve 2 National Park 3 National Monument 4 Wildlife Sanctuary 5 Protected Landscape 6 Resource Reserve 7 Anthropological Reserve 8 Multiple Use Management Area 9 Biosphere Reserve 10 World Heritage Site 11 Unclassified The Protected Areas data set from IUCN-WCMC covers the entire African continent at a spatial resolution of two minutes (120 seconds) of latitude/longitude, or approximately 3.7 kilometers. The data file consists of 2191 rows (lines/records) by 2161 columns (elements/pixels/ samples). Its upper-left or northwest corner origin is 38 degrees, 0 minutes and 45 seconds North latitude (38d 00' 45"" N), and -20 degrees, 1 minute and 15 seconds West longitude (-20d 01' 15"" W); and it extends to -35 degrees, 1 minute and 15 seconds South latitude (-35d 01' 15"" S), and 52 degrees, 0 minutes and 45 seconds East longitude (52d 00' 45"" E) at its terminal point in the lower-right or southeast corner. The data file comprises 4.74 Megabytes. The source of the Protected Area data is, as mentioned above, the International Union for the Conservation of Nature and Natural Resources (IUCN's) World Conservation Monitoring Center (WCMC) in Cambridge, UK. There is no published reference for this data set. * - Another more recent version of Protected Areas for Africa, with actual protected area boundaries, exists at UNEP/GRID-Nairobi. " proprietary a7b87a912c494c03b4d2fa5ab8479d1c_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change 1992-2014, v1.2 FEDEO STAC Catalog 1992-01-01 2014-12-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142730-FEDEO.umm_json This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from satellite (ERS‐1, ERS‐2, Envisat and Cryosat) radar altimetry. The ice mask is based on the GEUS/GST land/ice/ocean mask provided as part of national mapping projects, and based on 1980’s aerial photography. The data from ERS and Envisat are based on a 5‐year running average, using combined algorithms of repeat‐track (RT), along‐track (AT) or cross‐over (XO) algorithms, and include propagated error estimates. It is important to note that different processing algorithms were applied to the ERS‐1, ERS‐2, Envisat and CryoSat data; for details see the Product User Guide (PUG), available on the CCI website and in the documentation section here. For ERS‐1, the radar data were processed using a cross‐over algorithm (XO) only. For ERS‐2 data and Envisat data in repeat mode, a combination of RT and XO algorithms was applied, followed by filtering. For across‐mission (i.e. ERS‐2‐Envisat) combinations, and for Envisat operating in a drifting orbit, an AT and XO combination was applied (the difference between RT and AT algorithms is that AT use reference tracks and searches for data in the vicinity of this track). For CryoSat data a binning/gridding and plane fit method has been applied, following by weak filtering (0.05 degree resolution). proprietary a7e11745933a4f37b5aa1d4b23d71a83_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ADV algorithm), Version 2.31 FEDEO STAC Catalog 1995-06-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142909-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. Data are available for the period 1995-2002.For further details about these data products please see the linked documentation. proprietary a86bc574-3f22-44f8-a1f6-8d5bcc1c8a72_NA IRS-1C - Panchromatic Images (PAN) - Europe FEDEO STAC Catalog 1996-01-28 2004-01-31 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458068-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS PAN data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary a8b8191d62504acdb218d4767b446280_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (SU algorithm), Version 4.3 FEDEO STAC Catalog 1995-06-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143186-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the Swansea University (SU) algorithm, version 4.3. Data are available for the period 1995-2003.For further details about these data products please see the documentation. proprietary -aa09603e91b44f3cb1573c9dd415e8a8_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0 FEDEO STAC Catalog 2002-09-30 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142551-FEDEO.umm_json The CH4_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT, as part of the ESA's Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 4.0, and forms part of the Climate Research Data Package 4.The Weighting Function Modified DOAS (WFMD) algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. The data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. For further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary -aab98144131244f58ce1b56e7342ff3e_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142832-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 4.2 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary +aa09603e91b44f3cb1573c9dd415e8a8_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0 FEDEO STAC Catalog 2002-10-01 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142551-FEDEO.umm_json The CH4_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT, as part of the ESA's Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 4.0, and forms part of the Climate Research Data Package 4.The Weighting Function Modified DOAS (WFMD) algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. The data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. For further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary +aa8268e2ca0e48d98aee372795722253_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00 FEDEO STAC Catalog 2016-05-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359664-FEDEO.umm_json This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary aad_ais_gz_modis_slope_break_1 Amery Ice Shelf Grounding Zone defined as interpreted slope break in MODIS images AU_AADC STAC Catalog 2003-11-02 2003-11-02 66.3, -73.3, 74, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214311485-AU_AADC.umm_json Grounding Zone of the Amery Ice Shelf, East Antarctica defined by break of surface slope as determined through interpretation of MODIS images. It defines the landward edge of the grounding zone and therefore the maximum extent of the ice shelf. The MODIS data from the 250 m Channel 2 were processed to a reflectance product and remapped to a Polar Stereographic Projection. The image contrast was stretched so that subtle variations in reflectance could be perceived. The variation in reflectance was used as an indicator of variation in slope. The break of slope of the snow surface was picked interactively on an image display at a frequency sufficient to define the shape of the grounding zone margin. The series of points are provided as a Point shapefile file as well as a set of arcs connecting the points. The point positions are given in geographic coordinates. This work was completed as part of ASAC projects 2224 and 3067 (ASAC_2224, ASAC_3067). proprietary aad_ctd_database_1 Database of CTD data collected in the Southern Ocean by the AAD, ACE CRC and part of the Southern Ocean Atlas data set. AU_AADC STAC Catalog 1900-01-01 2003-03-09 -180, -80, 180, -15.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214311486-AU_AADC.umm_json Microsoft Access database containing a compilation of CTD data collected in the Southern Ocean from Australian Antarctic Division (AAD), Antarctic Climate and Ecosystems Co-operative Research Centre (ACE CRC) and Hydrographic Atlas of the Southern Ocean (SOA) data sources. This SOA data contains discrete CTD (Conductivity, Temperature and Depth) station data along with a 1 x 1 degree gridded CTD data set interpolated in space and time. Parameters include pressure, temperature, salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, and silicate). Ocean Tools software developed by AAD is available in conjunction with this database to manipulate, extract and visualise data (including station map, transect selection, xy plots, vertical cross sections, geostrophic velocity/transport calculations). The download file contains an access database of the compiled CTD data, a word document containing further information about the structure of the database and the data (AAD CTD Data.doc), and a folder of the original source data, including readmes providing reference details, and specific information. proprietary aae157df-5b91-4a49-b00b-d81729a566d7_NA TerraSAR-X - High Resolution Spotlight Images (TerraSAR-X High Resolution Spotlight) FEDEO STAC Catalog 2007-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457996-FEDEO.umm_json "This collection contains radar image products of the German national TerraSAR-X mission acquired in High Resolution Spotlight mode. High Resolution Spotlight imaging allows for a spatial resolution of up to 1 m at a scene size of 10 km (across swath) x 5 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html" proprietary @@ -15631,7 +15527,6 @@ adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT regio adu_birp Animal Demography Unit - The Birds in Reserves Project (BIRP) CEOS_EXTRA STAC Catalog 1906-02-05 2007-05-20 16.46, -34.77, 32.86, -22.61 https://cmr.earthdata.nasa.gov/search/concepts/C2232477691-CEOS_EXTRA.umm_json BIRP is a joint project of BirdLife South Africa (BLSA), and the Animal Demography Unit (ADU), based at the University of Cape Town (UCT). The basic purpose of BIRP is to compile a comprehensive catalogue of the species of birds which occur and breed in South Africa’s many protected areas. A database of this kind will help to identify the species which are as yet not adequately protected and will also provide the managers of protected areas with information useful in setting management policies. proprietary adu_cwac Animal Demography Unit - Coordinated Waterbird Counts (CWAC) CEOS_EXTRA STAC Catalog 1983-07-15 2006-09-30 16.46, -34.72, 32.88, -22.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232477679-CEOS_EXTRA.umm_json The Coordinated Waterbird Counts (CWAC) project was launched in 1992. The objective of CWAC is to monitor South Africa's waterbird populations and the conditions of the wetlands which are important for waterbirds. This is being done by means of a programme of regular mid-summer and mid-winter censuses at a large number of South African wetlands. Regular six-monthly counts are conducted; however, we do encourage counters to survey their wetlands on a more regular basis as this provides better data. CWAC currently monitors over 400 wetlands around the country on a regular basis, and furthermore curates waterbird data for close to 600 wetlands. proprietary adu_safring Animal Demography Unit - South African Bird Ringing Unit (SAFRING) CEOS_EXTRA STAC Catalog 1899-12-30 2004-12-31 -76.33, -71.9, 73.5, 72.25 https://cmr.earthdata.nasa.gov/search/concepts/C2232477669-CEOS_EXTRA.umm_json The South African Bird Ringing Unit (SAFRING) administers bird ringing in southern Africa, supplying rings, ringing equipment and services to volunteer and professional ringers in South Africa and neighbouring countries. All ringing records are curated by SAFRING, which is an essential arm of the Animal Demography Unit. Contact is maintained by the SAFRING Project Coordinator with all ringers (banders in North American or Australian terminology). The Bird Ringing Scheme in South Africa was initiated in 1948, so 1998 saw the 50th anniversary of the scheme. During this period over 1.7 million birds of 852 species were ringed. There have been a total of 16 800 ring recoveries since the inception of the scheme. This gives an overall recovery rate for rings in southern Africa of marginally less than 1%, averaged across all species. This probability varies enormously across species. proprietary -aeae1a19608347f7b802691db6984343_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142920-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 4.2 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 AU_AADC STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 AU_AADC STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division AU_AADC STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary @@ -15648,6 +15543,7 @@ aerosol_properties_725_1 SAFARI 2000 Physical and Chemical Properties of Aerosol aes5davg_236_1 BOREAS AES Five-day Averaged Surface Meteorological and Upper Air Data ORNL_CLOUD STAC Catalog 1976-01-01 1997-01-01 -107.87, 52.17, -97.83, 57.35 https://cmr.earthdata.nasa.gov/search/concepts/C2807614663-ORNL_CLOUD.umm_json Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba. proprietary aes_upl1_238_1 BOREAS AFM-05 Level-1 Upper Air Network Data, R1 ORNL_CLOUD STAC Catalog 1993-08-16 1996-10-22 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2812433046-ORNL_CLOUD.umm_json Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region. proprietary aes_upl2_239_1 BOREAS AFM-05 Level-2 Upper Air Network Standard Pressure Level Data ORNL_CLOUD STAC Catalog 1993-08-16 1996-10-22 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2807616315-ORNL_CLOUD.umm_json Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region. proprietary +af60720c1e404a9e9d2c145d2b2ead4e_NA ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4 FEDEO STAC Catalog 2010-01-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359400-FEDEO.umm_json This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format. proprietary afforestation-stillberg_1.0 Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.86716, 46.773573, 9.86716, 46.773573 https://cmr.earthdata.nasa.gov/search/concepts/C3226081740-ENVIDAT.umm_json # Background information The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Long-term monitoring of the large-scale high-elevation afforestation has generated data about tree growth, survival, and vitality. In addition, detailed characteristics of the microsite conditions of the research were conducted. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone. # Experiment description The Stillberg afforestation experiment was established in 1975 by planting 92,000 seedlings of *Larix decidua*, *Pinus cembra* and *Pinus mugo* ssp. *uncinata* in the alpine treeline ecotone. The afforestation site is located on a northeast-facing slope with steep, topographically highly structured terrain and covers elevations from 2075 to 2230 m a.s.l. The afforestation site was divided into 4052 square plots of 3.5 × 3.5 m, arranged in a regular species-alternating pattern over the whole area. Each plot contained 25 trees of one species (1350 plots per species), and the seedlings were systematically planted 70 cm apart. The trees have been monitored since 1975. Specifically, tree mortality was assessed annually from 1975 until 1995 and has been documented every ten years since then, with surveys in 2005 and 2015 (the next survey is due in 2025). Height of the surviving trees was measured in 1975, 1979, 1982, 1985, 1990, 1995, 2005, and 2015. In 1995, 2005, and 2015, drivers of tree vitality were assessed for a subset of trees per plot. Additionally, an extensive set of environmental parameters characterizing microsite conditions of the afforestation area were recorded before and after the planting of the trees. # Data description The five datasets from the afforestation experiment comprise ecological and environmental data from the main afforestation experiment in five datasets with accompanying metadata (Stillberg_afforestation_all_metadata.xlsx). All data and metadata files are bundled in a ZIP-file (Stillberg_afforestation_v1.zip). In particular, a first dataset contains environmental data characterising microsite conditions of the 4000 plots with regard to soil, topography, vegetation and microclimatic conditions (Stillberg_afforestation_plot_data_v1.csv; Stillberg_afforestation_plot_metadata_v1.csv. In each plot, the natural tree regeneration was assessed by counting seedings of several tree species in 2005 and 2015 (Stillberg_afforestation_regeneration_data_v1.csv; Stillberg_afforestation_regeneration_metadata_v1.csv). Furthermore, specific information about each of the 92’000 planted trees of the tree species is available (Stillberg_afforestation_tree_parameter_data_v1.csv; Stillberg_afforestation_tree_parameter_metadata_v1.csv). Survival data for each of the 92’000 individual trees can be found in a separate dataset (Stillberg_afforestation_tree_survival_data_v1.csv; Stillberg_afforestation_tree_survival_metadata_v1.csv). Tree growth and vitality parameters are available for all trees from 1995, and for subsets of trees for 2005 and 2015 (Stillberg_afforestation_tree_measurements_data_v1.csv; Stillberg_afforestation_tree_measurements_metadata_v1.csv). proprietary afm06ihd_240_1 BOREAS AFM-06 Boundary Layer Height Data ORNL_CLOUD STAC Catalog 1994-05-21 1994-09-20 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2807618371-ORNL_CLOUD.umm_json Contains AFM-06 hourly inversion height measurements. proprietary afm06ptd_241_1 BOREAS AFM-06 Mean Temperature Profile Data ORNL_CLOUD STAC Catalog 1994-05-21 1994-09-21 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2807620192-ORNL_CLOUD.umm_json Contains the AFM-06 temperature profiler data near the Old Jack Pine site in the Southern Study Area. proprietary @@ -15786,13 +15682,14 @@ avhrrlc1_434_1 BOREAS AFM-12 1-km AVHRR Seasonal Land Cover Classification ORNL_ aws_gis_1 Australian Antarctic automatic weather station gis dataset - 2003 location information AU_AADC STAC Catalog 2003-08-01 2003-08-30 54, -77, 144, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214313166-AU_AADC.umm_json This layer is a point dataset in the Geographical Information System (GIS). Point data represents Australian Antarctic Automatic Weather Stations. The operating dates for all stations is attached in the attribute table. The dataset was compiled in August 2003 from the The Australian Antarctic automatic weather station dataset http://aws.acecrc.org.au/datapage.html proprietary b017235a8e544d6fbad21387ebfbf0d8_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by TU Dresden, v1.2 FEDEO STAC Catalog 2002-03-31 2016-08-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143452-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.2) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016)The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065 proprietary b03b3887ad2f4d5481e7a39344239ab2_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (SU Algorithm), Version 4.3 FEDEO STAC Catalog 2002-05-20 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142709-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the Swansea University (SU) algorithm, version 4.3. It covers the period from 2002 - 2012.For further details about these data products please see the linked documentation. proprietary +b0ec72a28b6a4829a33ed9adc215d5bc_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360215-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary b1bd715112ca43ab948226d11d72b85e_NA ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1 FEDEO STAC Catalog 1982-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143110-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide. proprietary b1f1ac03077b4aa784c5a413a2210bf5_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0 FEDEO STAC Catalog 2002-06-10 2012-03-31 -180, -90, 180, -16 https://cmr.earthdata.nasa.gov/search/concepts/C2548142992-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information. proprietary b25d4a6174de4ac78000d034f500a268_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0 FEDEO STAC Catalog 1997-01-01 2019-12-31 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143089-FEDEO.umm_json This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year. proprietary b382ebe6679d44b8b0e68ea4ef4b701c_NA ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7 FEDEO STAC Catalog 1992-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143154-FEDEO.umm_json As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS). proprietary b431fbecf73c4442ad5d7bcf80929b03_NA ESA Ozone Climate Change Initiative (Ozone CCI): GOMOS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2002-01-01 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143149-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc” contains monthly zonal mean data for GOMOS in 2008. proprietary b480d7c8-3694-4772-8294-941f3d3ede9f_1 European remote sensing forest/non-forest digital map CEOS_EXTRA STAC Catalog 1992-01-01 1993-09-28 -12, 38, 44, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2232847861-CEOS_EXTRA.umm_json "The European Remote Sensing Forest/Non-forest Digital Map was originally prepared for the European Space Agency (ESA) as a contribution to the World Forest Watch project of the International Space Year (ISY), 1992. The actual production of the map was carried out by a consortium of four companies, GAF mbH (Munich FRG), the Swedish Space Corporation (Kiruna), SCOT Conseil (France) and the National Land Survey of Finland (Helsinki). It is based entirely on the digital classification of NOAA/AVHRR-HRPT* one-kilometer resolution multispectral data, approximately 70 scenes from the summer periods only of 1990 to 1992. As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source. Because the methodology used to produce the digital map is documented and was ""economically"" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992). The following steps are a summary of those actually used by the consortium in the production of the digital map: - Satellite data selection (minimal cloud cover)/acquisition; - Data pre-processing for a) geometric correction and b) cloud masking; - Data subset stratification into homogeneous spectral zones; - Data subset classification (Bayesian maximum likelihood); - Accuracy assessment (using classified Landsat MSS); - Mosaicking of classified data subsets; - Merging of final results and overlays; - Cartographic preparation. The producers of the digital map used only data from AVHRR channels 1, 2 and 3 with ""maximal geometric and radiometric resolution""; that is, the central 1200 to 1600 pixels of any given scan line, to map European forest areas greater than one square kilometer. Because the AVHRR sensor is not capable of distinguishing among different European forest types, many broad classes (Boreal, Central European and Mediterranean) are grouped together as ""forest"" in the digital map. * - the National Oceanic and Atmospheric Administration (NOAA) / satellite's Advanced Very High Resolution Radiometer (AVHRR) sensor - and High Resolution Picture Transmission (HRPT) data. For the 32 Landsat scenes compared with the NOAA/AVHRR forest/non-forest classification, the overall accuracy (percentage of pixels ""correctly"" classified) was calculated as 82.5%, and the surface area accuracy (degree of agreement in areal extent between the NOAA/AVHRR results and the Landsat MSS used as ""ground truth"") was found to be 93.8%. Format of the Original ESA/ESTEC-Provided Data Set The European Forest/Non-Forest Digital Map was provided to GRID on a single 150-Mb data cartridge, as a total of seven ARC/INFO-format data files for separate parts of the continent as follows: Northwest; North; Central; Southwest and Southeast Europe; the Commonwealth of Independent States (CIS, up to the Ural Mountains only); and North Africa. A total of 53 countries are included altogether. Within this original digital map, data are coded by country and category (i.e. forest, non-forest or water), but ""overall"" selections of one category or another are rendered difficult because the codes are in combination (i.e. country + category). Also, the large size of the seven individual ARC/INFO coverages all but prohibits working with the digital data for the entire pan-European area. Explanation of the Data Processing done by GRID GRID's objective in data processing of the European Forest/Non-forest Digital Map was to create a single seamless product covering most of the continent, for forestry and GIS studies at a pan-European level. The assemblage of the seven original individual coverages prepared for ESA/ESTEC into a single entity proved impractical due to both hardware and software limitations; thus, the seventh and largest portion for the Commonwealth of Independent States (CIS) was left out of the overall assemblage. Even so, it was still necessary to generalize the data somewhat, given the total number of polygons (>100000) and arcs (>170000) in the remaining six original coverages. Thus, the following methodology was followed to reduce the amount of data and assemble the six coverages into a single product (all data processing was done using commands in the ARC/INFO software): - Polygon elimination based on area - After several experiments, polygons with an area smaller than four square kilometers (sq. km.) were eliminated. This minimum area proved to be a good compromise between original forest patterns and number of polygons eliminated (total of 70%). The equivalent of four sq. km. at a central latitude within each of the six original coverages was calculated, and this value was used in the 'ELIMINATE' command. It would have been more accurate to perform the 'ELIMINATEs' with the data in an equal-area projection, but for practical reasons (space and time) they were not. - Assembling six coverages into one - The six coverages were put together using the 'MAPJOIN' command. The software limitation of a maximum 10000 arcs per polygon was circumvented by splitting the outer polygon of Europe into three separate parts. - Editing errors produced by step (2) - The 'MAPJOIN' command puts adjacent coverages together and recreates topology using an assigned distance known as the ""fuzzy tolerance"" factor. Any reasonable factor forces some lines to converge, creating dangling arcs and new polygons without IDs. As a result, interactive editing of the new coverage was necessary to delete dangling arcs, and to assign proper polygon IDs. - Update of the topology - After the modifications made in step (3), it was necessary to re-create the polygon topology using 'CLEAN'. - Addition of INFO item 'classes' - A new numeric item (format 3 3 I) was added in the polygon attribute table (.PAT) to contain the following values: 1) Forest; 2) Non-forest; and 3) Water. This item allows a user to select e.g. all of the European forested area polygons, as opposed to just those within a single country, in one simple INFO command. The European Forest/Non-forest data set is available from GRID as one ARC/INFO 'EXPORT'-format data file in the Geographic Projection, which covers an area from 20 to 80 degrees North latitude, and -30 degrees West to 60 degrees East longitude. The single data file ""EURO_FOR.E00"" comprises 77.25 Mb., but after being 'IMPORTed' to the equivalent ARC/INFO coverage, is reduced to 19.7 Mb in size. There is also the separate, original (non-generalized) data file which covers the CIS area alone; this additional 'EXPORT'-format data file ""CIS.E00"" comprises 68.262 Mb. Users who would prefer to have other original portions of the European Forest/Non-forest Digital Map listed above, as opposed to the GRID version documented herein, are requested to contact ESA/ESTEC at the address listed below. Reference and Source The source of the data set is the ESA/ESTEC ISY Office*, as modified by UNEP/GRID-Geneva. The proper reference to the data set is ""ESA, 1992, Remote sensing forest map of Europe (brochure), ESA/ESTEC, 18 pages."" ESA/ESTEC also provides a paper entitled ""Digital data set of the remote sensing forest map of Europe; guidelines for data handling (as prepared by GAF-Munich in April 1993)"", which contains much useful information about their original digital data product and the seven individual data files they distribute as one entity. In addition, ESA/ESTEC distributes a paper map of the original product having the same name as above, at a scale of 1:6 000 000 (the paper map uses the Lambert Azimuthal Equal-Area projection). * - the European Space Agency/European Space Research and Technology Centre - the International Space Year; P. O. Box 299; 2200 AG Noordwijk; The Netherlands (Mr. K. Pseiner; fax = 01719-17400). " proprietary -b64b1a0ad7874fb39791e99c57b944bc_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142812-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary +b54d5f1c08594879a05929ce09951c56_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00 FEDEO STAC Catalog 2018-12-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327358997-FEDEO.umm_json This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage runs from December 2018 to December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary b673f41b-d934-49e4-af6b-44bbdf164367_NA AVHRR - Land Surface Temperature (LST) - Europe, Daytime FEDEO STAC Catalog 1998-02-23 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458008-FEDEO.umm_json "The ""Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)"" is a fixed grid map (in stereographic projection ) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between –39.5°C and +87°C with a radiometric resolution of 0.5°C. A value of –40°C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/" proprietary bark-and-wood-boring-insects-in-pines_1.0 Infestation of Scots pines with different vitalities by bark and wood boring insects ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.5627136, 46.2249145, 7.8984833, 46.3184179 https://cmr.earthdata.nasa.gov/search/concepts/C2789814729-ENVIDAT.umm_json After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack. proprietary baro-levelling-to-domec_1 Barometric Leveling Results, Pioneerskaya to Dome C AU_AADC STAC Catalog 1955-01-01 1985-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313156-AU_AADC.umm_json Record of barometric leveling measurements taken during the traverse from Pioneerskaya to Dome C (year currently unknown). These documents have been archived in the records store at the Australian Antarctic Division. proprietary @@ -15814,6 +15711,7 @@ beryllium_10be_isotopes_lawdome_1 High resolution studies of cosmogenic berylliu beryllium_7be_isotopes_lawdome_1 High resolution studies of cosmogenic beryllium isotopes (7Be) at Law Dome AU_AADC STAC Catalog 2013-03-01 2013-03-31 112.80535, -66.7059, 112.80534, -66.7058 https://cmr.earthdata.nasa.gov/search/concepts/C1214571593-AU_AADC.umm_json Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output. A ~1.4 x 1 x 1 m pit was dug on Law Dome. The wall was flattened using a ~60 cm level, handsaw and paint scrappers. A significant sastrugi could be seen in the top right of the wall. Sampling was started on the left of the wall to avoid this where possible. Wearing plastic gloves to avoid contaminating the samples, the top surface was levelled to the lowest point, and some of the snow collected as sample P1-1. It was around 4 cm at its highest point. A 10 cm x 10 cm grid was drawn into the wall, covering 80 cm x 80 cm. The top 10 cm layer was sawn out of the wall using a hand saw, cutting into the wall by at least 20 cm along the horizontal 10 cm below the top surface, then the back 20 cm from the front surface, and finally chopping the large block into smaller blocks. The extra six blocks were discarded, and the two samples were put into zip lock bags as P2-1 and P2-2. The back of the sampling area was cleared back to allow easier access for the next layer. This was repeated for seven more layers, finishing with P9. One block from each level was used for density measurements. The samples from each level were combined into a melting jar and carrier added. For some samples, not all the blocks fitted at once, so a portion of the blocks were melted (with the carrier) in the oven at 60 degrees C. The samples were allowed melt completely overnight. ~10mL of the samples were retained for water isotopes . The samples were filtered though 41 microns and the 0.45 microns and pumped onto cation columns. proprietary bet_1.0 Bryophytes of Europe Traits (BET) dataset ENVIDAT STAC Catalog 2023-01-01 2023-01-01 -31.1718714, 26.214591, 70.1953197, 82.3206462 https://cmr.earthdata.nasa.gov/search/concepts/C3226081833-ENVIDAT.umm_json The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023). proprietary bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA MERIS - Water Parameters - Baltic Sea, Monthly FEDEO STAC Catalog 2006-01-01 2012-04-08 6.98888, 52.1246, 34.1429, 66.7187 https://cmr.earthdata.nasa.gov/search/concepts/C2207458063-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps. proprietary +bf535053562141c6bb7ad831f5998d77_NA ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01 FEDEO STAC Catalog 2010-01-01 2021-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359101-FEDEO.umm_json This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.This version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude. proprietary bf5eae2a052848aab2abf93d96e7e9aa_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ensemble product), Version 2.6 FEDEO STAC Catalog 1995-08-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143216-FEDEO.umm_json In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite.The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. In 2002, it also contains data from the AATSR instrument on the ENVISAT satellite. A separate AATSR product covering the period 2002-2012 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation. proprietary bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA METOP GOME-2 - Nitrogen Dioxide (NO2) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458015-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary bhc1_bhc2_1982_1 BHC1 and BHC2 Orientation and Temperature, Law Dome 1982-83 AU_AADC STAC Catalog 1982-01-01 1983-12-31 110, -67, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306659-AU_AADC.umm_json Results for the temperature and orientation recorded from the BHC1 and BHC2 ice core boreholes in 1982-83. A hard copy of this document has been archived in the Australian Antarctic Division records section. proprietary @@ -15916,7 +15814,7 @@ c4p3rad_1 CAMEX-4 NOAA WP-3D RADAR V1 GHRC_DAAC STAC Catalog 2001-09-03 2001-09- c4p3vid_1 CAMEX-4 NOAA WP-3D VIDEO V1 GHRC_DAAC STAC Catalog 2001-09-03 2001-09-19 -100, 10, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1995554052-GHRC_DAAC.umm_json The CAMEX-4 NOAA WP-3D Video dataset was collected during the fourth field campaign in the CAMEX series (CAMEX-4), which ran from 16 August to 25 September, 2001 and was based out of Jacksonville Naval Air Station, Florida. An important addition to CAMEX-4 was the participation of the NOAA weather reconnaissance WP-3D that collected radar, video and microphysical data.The NOAA WP-3D Videos were created giving a forward, left, right and downward views relative to the aircraft. Each view is a separate tape. All are recoreded in SVHS format in compressed time mode. That means that the video shows time passing at a rate approximately 12.5 times that of normal speed (e.g. 1 minute real time takes 5 seconds on the video). For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov proprietary c4sg8_1 CAMEX-4 GOES-8 PRODUCTS V1 GHRC_DAAC STAC Catalog 2001-08-03 2001-09-21 -130, 15, -10, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1979102757-GHRC_DAAC.umm_json The CAMEX-4 GOES-8 Products dataset was collected during the CAMEX-4 field campaign, which ocused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. In support of the fourth Convection and Moisture Experiment (CAMEX-4), imagery from the Geostationary Operational Environmental Satellite 8 (GOES-8) was collected and archived. Three channels were archived: channel 1-- visible (0.65 microns), channel 2-- infrared (11 microns) and channel 3-- known as the water vapor channel (6.75 microns). Data files are available in McIDAS format, and browse imagery is also available. proprietary c5064da0-ce61-47fc-b17f-c837bd2847be Flood events CEOS_EXTRA STAC Catalog 1970-01-01 -101.242195, -34.5123, 127.74636, 52.267735 https://cmr.earthdata.nasa.gov/search/concepts/C2232848956-CEOS_EXTRA.umm_json This dataset includes an estimate of flood events. It is based on two sources: 1) A GIS modeling using a statistical estimation of peak-flow magnitude and a hydrological model using HydroSHEDS dataset and the Manning equation to estimate river stage for the calculated discharge value. 2) Observed flood from 1999 to 2007, obtained from the Dartmouth Flood Observatory (DFO). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe, with key support from USGS EROS Data Center, Dartmouth Flood Observatory 2008. proprietary -c65ce27928f34ebd92224c451c2a8bed_NA ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1 FEDEO STAC Catalog 1991-08-31 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143126-FEDEO.umm_json The ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI) dataset accurately maps the surface temperature of the global oceans over the period 1991 to 2010, using observations from many satellites. The data provides an independently quantified SST to a quality suitable for climate research.The ESA SST CCI Analysis Long Term Product consists of daily, spatially complete fields of sea surface temperature (SST), obtained by combining the orbit data from the AVHRR and ATSR ESA SST CCI Long Term Products, using optimal interpolation to provide SSTs where there were no measurements. These data cover the period between 09/1991 and 12/2010.The Version 1.1 data is an update of the Version 1.0 dataset.Version 1.0 of this dataset is cited in: Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20 proprietary +c65ce27928f34ebd92224c451c2a8bed_NA ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1 FEDEO STAC Catalog 1991-09-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143126-FEDEO.umm_json The ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI) dataset accurately maps the surface temperature of the global oceans over the period 1991 to 2010, using observations from many satellites. The data provides an independently quantified SST to a quality suitable for climate research.The ESA SST CCI Analysis Long Term Product consists of daily, spatially complete fields of sea surface temperature (SST), obtained by combining the orbit data from the AVHRR and ATSR ESA SST CCI Long Term Products, using optimal interpolation to provide SSTs where there were no measurements. These data cover the period between 09/1991 and 12/2010.The Version 1.1 data is an update of the Version 1.0 dataset.Version 1.0 of this dataset is cited in: Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20 proprietary c88_data_1 Fish biological and stomach contents data - Casey 1988 AU_AADC STAC Catalog 1988-01-01 1988-12-31 110, -67, 112, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313382-AU_AADC.umm_json Data from fish captured by Erwin, Casey 1988. Includes fish size, weight, sex, reproductive stage data as well as quantitative stomach contents data and qualitative position data. Approximate locations where fish were caught are provided in the database. Additionally an approximate image map is also provided as a visual reference. These data are stored in an Access Database. Additionally, another Microsoft Access database containing data from this cruise, plus several others is available for download from the URL given below. The Entry ID's of the other metadata records also related to this data are: AADC-00038 AADC-00068 AADC-00073 AADC-00075 AADC-00080 AADC-00082 c88_data The fields in this dataset are: Cruises Date Location Latitude Longitude Species Gear Length Weight Sex Gonad Eye Otolith Stomach Lifestage Family proprietary calibgas_500_1 BOREAS Calibration Gas Standards ORNL_CLOUD STAC Catalog 1994-05-01 1996-11-30 -111, 49, -93, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2761665325-ORNL_CLOUD.umm_json In order to improve the comparability of trace gas measurements made by various science teams, the BOReal Ecosystem-Atmosphere Study (BOREAS) obtained several cylinders of carbon dioxide (CO2) and methane (CH4) that were used as calibration standards. proprietary canopychem_422_1 Seedling Canopy Chemistry, 1992-1993 (ACCP) ORNL_CLOUD STAC Catalog 1992-11-06 1993-03-15 -122.05, 37.4, -122.05, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776831590-ORNL_CLOUD.umm_json The nitrogen and chlorophyll concentrations of constructed Douglas-fir and bigleaf maple seedling canopies were determined. Canopy reflectance spectra were measured before foliage samples were collected. proprietary @@ -16005,10 +15903,9 @@ csgcpex01_1 GPM GROUND VALIDATION GCPEX SNOW MICROPHYSICS CASE STUDY V1 GHRC_DAA csiro_australianinsect Australian Insect Common Names Database CEOS_EXTRA STAC Catalog 2001-01-01 111.22, -45.73, 155.72, -8.88 https://cmr.earthdata.nasa.gov/search/concepts/C2226653610-CEOS_EXTRA.umm_json "The Australian Insect Common Names Database includes insects in the phylum of Arthropods, classes - Arachnida, Chilopoda, Collembola, Diplopoda, Insecta, Malacostraca, and Symphyla. This website database provides ready access to the correct scientific name of every insect or related creature for which there is a common (or vernacular) name in use in Australia. The site also enables the user to discover the common name or names used in Australia for a species for which the user knows only the scientific name. Species are also listed in family groupings. An index of commonly used abbreviations of authors' names has also been included. This index is intended to assist in the interpretation of abbreviations which may be encountered in entomological literature. It is recommended, however, that in present-day usage authors' names be quoted in full to avoid ambiguity. While scientific nomenclature is governed by strict rules, vernacular nomenclature is not. Inevitably there will be differences of opinion over what constitutes an appropriate common name or over whether a particular common name is or is not in wide use. In preparing the lists which follow we have endeavoured to include common names which are used in conversation and in the literature. We have also taken the opportunity to weed out a few contrived or clumsy names which have appeared in earlier editions of the Handbook but which seem not to be in use. Few Aboriginal names have been included but we believe that such names would enhance future versions of this website. We have included the common names of Australian butterflies listed by M. Braby in The Butterflies of Australia (2001), although with some rationalisation. The following conventions are used: 1. Where changes of scientific or common names have occurred since the previous edition of the Handbook, the earlier names are listed and cross referenced to entry's new name. 2. We have avoided hyphenation whenever possible, preferring such fusions as 'stemborer', leafminer', 'stumptailed', 'blackheaded', etc. Where a common name is taxonomically incorrect, e.g. 'whitefly' (Hemiptera, not Diptera) and 'whitemoth' (Trichoptera, not Lepidoptera) the two words comprising the name are fused. When the common name is taxonomically correct, the words are used separately, e.g. 'bed bug' and 'hawk moth'. Exceptions are made when usage over many years has fused two words that would be separated if this convention was strictly applied, e.g. blowfly, mealybug. 3. Where a common name is applied to more than one species, this is indicated by bracketing the name, e.g. '(canegrub)'. 4. For each species there is an indication whether the organism is an native, exotic or introduced as a biological control agent and that it has been successfully established. 5. In the distribution maps, presence of the species in a State is indicated by a shading of the entire State or Territory. This does not imply that the species necessarily occurs throughout the State or Territory in question. A large question mark superimposed on the map of Australia indicates that the distribution of the species within Australia is unknown to us. Information was obtained from ""http://www.ento.csiro.au/aicn/""." proprietary d12fc40e4f254ce38303157fa460f01c_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (SU algorithm), Version 4.3 FEDEO STAC Catalog 2002-05-20 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143145-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly aerosol products from the AATSR instrument on the ENVISAT satellite, using the Swansea University (SU) algorithm, version 4.3. Data is available for the period 2002 - 2012.For further details about these data products please see the documentation. proprietary d2ed0c005761475d92ca444666156c4a_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01 FEDEO STAC Catalog 1995-06-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142674-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. The data covers the period from 1995 - 2003.For further details about these data products please see the linked documentation. proprietary +d34330ce3f604e368c06d76de1987ce5_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0 FEDEO STAC Catalog 1997-01-01 2021-12-31 -180, 25, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C3327359586-FEDEO.umm_json This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. proprietary d51ffc79-5c9f-4252-be8e-2932eab8fff0_NA IRS-1D - Multispectral Images (LISS-III) - Europe FEDEO STAC Catalog 1999-01-01 2005-01-27 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458013-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. \\n\\nIRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products. proprietary d545d232-ac86-49c3-a42d-67b0b9608b29_NA METOP GOME-2 - Cloud Top Pressure (CTP) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458022-FEDEO.umm_json The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/ proprietary -d6d0d7b4cf3540448b4ddcaed2f54b81_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143153-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary -d9df331e346f4a50b18bcf41a64b98c7_NA ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2019), version 1.0 FEDEO STAC Catalog 1982-01-01 2019-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142699-FEDEO.umm_json This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1982-2019. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years. proprietary da2b8512312a4f14a928766f7f632d36_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ORAC algorithm), Version 4.01 FEDEO STAC Catalog 2002-05-20 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142735-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on ENVISAT, derived using the ORAC algorithm, version 4.01. Both daily and monthly gridded products are availableFor further details about these data products please see the linked documentation. proprietary daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0 daily 500m gridded net radiation and soil moisture for Switzerland, 2004 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814737-ENVIDAT.umm_json R data set containing R raster objects with 500m gridded daily modeled soil moisture and net radiation covering Switzerland for the year 2004. proprietary daily-solute-and-isotope-of-stream-water-and-precipitation_1.0 Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland ENVIDAT STAC Catalog 2019-01-01 2019-01-01 8.7092058, 47.04259, 8.75655, 47.1507977 https://cmr.earthdata.nasa.gov/search/concepts/C2789814801-ENVIDAT.umm_json This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations. An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242 proprietary @@ -16047,7 +15944,6 @@ davis_baro_leveling_1970_1 Davis Inland Barometric Levelling 1970 AU_AADC STAC C davis_lidar_2009_1 Lidar data captured in 2009/10 in the Davis and Heidemann Valley area of the Vestfold Hills, Antarctica AU_AADC STAC Catalog 2009-11-17 2009-11-23 77.9358, -68.6094, 78.0914, -68.5597 https://cmr.earthdata.nasa.gov/search/concepts/C1214313412-AU_AADC.umm_json This dataset consists of: 1 Lidar data captured by the Australian Antarctic Division in November 2009 in the Davis and Heidemann Valley area of the Vestfold Hills, Antarctica. The files are in las format. 2 A report about the processing of the lidar data that resulted in the las files. The raw data from which the las files were derived are described by the metadata record 'High resolution digital aerial photography and LIDAR scanning of portions of the Vestfold Hills and Rauer Group' which has an Entry ID of: Davis_2009_Aerial_Photography. The lidar data were captured for the purpose of creating a Digital Elevation Model of the area. proprietary davis_strain_1971_1 Davis Strain Grid Measurement 1971 AU_AADC STAC Catalog 1971-12-08 1971-12-16 77, -68, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214308522-AU_AADC.umm_json "Strain grid measurements near Davis during the traverse program for the 1971 season. Chaining out from the center pole to one of four corner stakes required two moves with an assumed 600ft ""invar"" tape. Measurement in both directions was achieved on all grids. Snow to bottom of tag measurements were recorded in centimeters plus the central two inch steel black pole. For the theodolite readings, both left and right face azimuth angles were taken at snow level and elevation read to the bottom of the tag. Physical records archived at the Australian Antarctic Division." proprietary davisbathy_gis_1 Bathymetry of Approaches to Davis Station AU_AADC STAC Catalog 1989-02-06 1992-02-12 78, -69, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214308526-AU_AADC.umm_json Bathymetric Contours and height range polygons of approaches to Davis Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings. proprietary -db32212d86f9431dae67076dd122565e_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 4.2 FEDEO STAC Catalog 1997-09-03 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142706-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary dc8ammr_1 CAMEX-3 DC-8 Airborne Multichannel Microwave Radiometer (AMMR) V1 GHRC_DAAC STAC Catalog 1998-08-20 1998-09-17 -86.165, 26.153, -78.982, 30.477 https://cmr.earthdata.nasa.gov/search/concepts/C1979104228-GHRC_DAAC.umm_json The CAMEX-3 DC-8 Airborne Multichannel Microwave Radiometer (AMMR) dataset is a browse-only dataset that consists of plotted digital count measurements collected by the Airborne Multichannel Microwave Radiometer (AMMR) during the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying the various aspects of tropical cyclones in the region. The AMMR was mounted onboard the NASA DC-8 aircraft. Daily browse files in GIF format are available for August 20, September 2, and September 17, 1998. proprietary dc8avaps_1 CAMEX-3 AIRBORNE VERTICAL ATMOSPHERE PROFILING SYSTEM (AVAPS) V1 GHRC_DAAC STAC Catalog 1998-08-15 1998-09-22 -85.7233, 14.0367, -63.549, 34.06 https://cmr.earthdata.nasa.gov/search/concepts/C1979104659-GHRC_DAAC.umm_json The CAMEX-3 DC-8 Airborne Vertical Atmospheric Profiling System (AVAPS) dataset consists of measurements from AVAPS, which uses dropsonde and Global Positioning System (GPS) receivers to measure the atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde's descent once each half second. These measurements were collected in support of the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying various aspects of tropical cyclones in the region. AVAPS provided vertical profiles of temperature, humidity, pressure, and winds. The dataset files are available in netCDF-3 and ASCII format with browse imagery available in GIF image format. proprietary dc8capac_1 CAMEX-3 CLOUD AND AEROSOL PARTICLE CHARACTERIZATION (CAPAC) V1 GHRC_DAAC STAC Catalog 1998-08-15 1998-09-23 -86.165, 14.045, -62.96, 39.0333 https://cmr.earthdata.nasa.gov/search/concepts/C1979110321-GHRC_DAAC.umm_json CAPAC is a series of three instruments: the Forward Scattering Spectrometer Probe model 300 (FSSP-300), the Two Dimensional Optical Array Probes [Cloud and Precipitation Probes (2D-P)] and the CAPAC video. These instruments flew during CAMEX-3 upon the NASA DC-8 mounted on the left wing. Cloud and aerosol particles were exposed to laser light to measure particle size from 0.3 micrometer to 6.4 millimeter, and both size and shape between 40 micrometer and 6.4 millimeter particle diameter as function of particle size. The size distributions thus determined were integrated to yield particle surface area, and ice and liquid water contents in clouds and precipitation. The purpose of the CAMEX-3 mission was to study hurricanes over land and ocean in the U.S. Gulf of Mexico, Caribbean, and Western Atlantic Ocean in coordination with multiple aircraft and research-quality radar, lightning, radiosonde and rain gauge sites. proprietary @@ -16060,7 +15956,6 @@ dc8macaws_1 CAMEX-3 MACAWS V1 GHRC_DAAC STAC Catalog 1998-08-21 1998-09-22 -105, dc8mms_1 CAMEX-3 DC-8 METEOROLOGICAL MEASUREMENT SYSTEM (MMS) V1 GHRC_DAAC STAC Catalog 1998-08-03 1998-09-23 -105, 10, -50, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1979111218-GHRC_DAAC.umm_json The CAMEX-3 Meteorological Measurement System (MMS) dataset consists of atmospheric parameters measured by the MMS instruments aboard NASA DC-8 aircraft. The MMS consists of three major systems: an air-motion sensing system to measure air velocity with respect to the aircraft, an aircraft-motion sensing system to measure the aircraft velocity with respect to the Earth, and a data acquisition system to sample, process, and record the measured quantities. The MMS dataset consits of atmospheric pressure, temperature, and wind measurements collected during the CAMEX-3 mission to study hurricanes over the land and ocean in the U.S Gulf of Mexico, Caribbean, and Western Atlantic Ocean. proprietary dc8psr_1 CAMEX-3 POLARIMETRIC SCANNING RADIOMETER (PSR) V1 GHRC_DAAC STAC Catalog 1998-08-06 1998-09-23 -105, 10, -50, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1979111637-GHRC_DAAC.umm_json The Polarimetric Scanning Radiometer (PSR) is a versatile airborne microwave imaging radiometer developed by the Georgia Institute of Technology and the NOAA Environmental Technology Laboratory for the purpose of obtaining polarimetric microwave emission imagery of the Earth's oceans, land, ice, clouds, and precipitation. proprietary dd3da2570363429791b51120bdd29c02_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2 FEDEO STAC Catalog 1991-08-05 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142649-FEDEO.umm_json The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 proprietary -de75072edfca44bfaaec0ed171d86bde_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142555-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary de883c15-85f6-435a-b5aa-3f6468ba919a_1 Annual methane emission from livestock (KG./SQ.KM) CEOS_EXTRA STAC Catalog 1988-06-01 1988-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847409-CEOS_EXTRA.umm_json "This one-degree latitude/longitude spatial resolution data set of Methane Emission from Animals data set was compiled at the NASA/Goddard Institute of Space Studies (GISS) from nine animal population densities.* The statistics on animal populations came from the Food and Agricultural Organization (FAO) and other sources. The animals were distributed across a one-degree latitude/longitude grid of national political boundaries, and sub-national boundaries for Australia, Brazil, Canada, China, India, USA and the former USSR. Published estimates of methane production from each type of animal were applied to the populations to yield a global distribution of annual methane emission by animals, expressed in kilograms per square kilometer of CH4 produced annually. A large spatial variability in the distribution of methane production (and the source animal populations) can clearly be seen in the global digital map. The total annual global estimate of methane emission is 75.8 teragrams (10 to the 12th power), about 55% of which is found between 25 degrees North and 55 degrees North latitude, a significant contribution to the observed north-south gradient of atmospheric methane concentration. The proper reference to this data set is ""J. Lerner, E. Matthews and I. Fung, June 1988. Methane Emission from Animals: a Global High-Resolution Data Base, Global Biogeochemical Cycles, vol. 2, no. 2, pp. 139-156."" The original magnetic tape containing these data came from the National Center for Atmospheric Research (NCAR-Scientific Computing Division/Data Support Section); 1850 Table Mesa Drive; Boulder, Colorado; 80307 USA. This tape contains the methane emission data file and ten animal population density data files (the nine listed below plus bovines, a combination of 'cattle' and 'dairy cows'). In addition it has three listing or program files; all of the data and non-data files are in ASCII format. While all of the 14 files have been read from tape to disk at GRID-Geneva, only the annual methane emission (kg./sq. km.) data file has been converted to a binary image format. This data set is available as five different file types: - ASCII file of complex real (floating-point, 32-bit) numbers, both original file; and the IBM-compatible file; - 16-bit, signed integer file; - eight-bit unsigned integer file; - demonstration file (also eight-bit), useful only for visualization. Type number (3) is recommended for most analytical purposes, as it contains all of the numerical information of the original file (1), but is easier to work on. Type number (4) is only recommended for those systems which cannot handle 16-bit data, and type (5) in cases where an annotated image or photoproduct only is desired. The Methane data file is held in the Plate Carree (Simple Cylindrical) projection, has a spatial resolution of one degree latitude/longitude and consists of 180 rows (lines) by 360 columns (elements/pixels/ samples) of data. Its origin point is at 90 degrees North latitude and 180 degrees West longitude, and it extends to 90 degrees South latitude and 180 degrees East longitude. The two-byte or 16-bit per element data file comprises 130 Kb, and the one-byte file 65 kb. - Cattle, Dairy cows, Water buffalo, Sheep, Goats, Camels, Pigs, Horses and Caribou " proprietary deadwood-generator_1.0 Deadwood Generator ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.9440727, 47.0239773, 9.011364, 47.0448028 https://cmr.earthdata.nasa.gov/search/concepts/C3226081551-ENVIDAT.umm_json The here presented code generates discrete three-dimensional, RAMMS::ROCKFALL readable deadwood log files (.pts-format) of windtrown forests, including the pilling effect due to slightly different throw directions. proprietary debris-flow-prediction-based-on-rainfall_1.0 Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.5740433, 46.2496507, 7.6626205, 46.3345789 https://cmr.earthdata.nasa.gov/search/concepts/C2789814606-ENVIDAT.umm_json This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021). ## How to install and run the example Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following: `$ conda env create -f environment.yml` `$ conda activate thresholds` or `$ source activate thresholds` To run an example: `$ python run_example()` It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found. ## License GNU General Public License v3.0 proprietary @@ -16098,19 +15993,15 @@ drought-and-beech-1000-beech-project_1.0 Data on multi-year drought impacts on E dtms0bil_247_1 BOREAS Daedalus TMS Level-0 Imagery: Digital Counts in BIL Format ORNL_CLOUD STAC Catalog 1994-09-16 1994-09-17 -106.32, 53.42, -97.23, 56.25 https://cmr.earthdata.nasa.gov/search/concepts/C2846959845-ORNL_CLOUD.umm_json The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. proprietary dynamics-of-insect-natural-enemies-of-bark-beetles_1.0 Dynamics of insect natural enemies of bark beetles ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.1146183, 46.9811854, 9.1285229, 46.9903195 https://cmr.earthdata.nasa.gov/search/concepts/C2789814602-ENVIDAT.umm_json In 1994 a large area of mountain spruce forest was infested by the European spruce bark beetle (Ips typographus) in the Gandberg forest near Schwanden, canton Glarus, Switzerland (46.99145 N, 9.10768 E, 1300 m a.s.l.). In a perimeter of approx. 13 ha, 50 infested dead spruce trees were selected and labelled in 1994. The trees were randomly distributed across the whole perimeter and attributed to 5 groups of 10 trees of approx. 25-40 cm diameter each. In each of the following 5 years (1995-1999), the trees of one of these groups were cut in early spring and transported by helicopter to a vehicle-accessible road. Of each log, two bolts of 1.5 m length were cut, one from the base and one from the beginning of the crown. The bolts were transported by truck to the institute WSL and exposed in emergence eclectors (metal cabinets of approx. 2.0x0.5x0.5 m) in a greenhouse to let the insects emerge. Each tree was left 2 years in the eclectors to allow insects with more than 1 year development time to emerge. During 2 months in the winter between the two exposure years the bolts were removed from the eclectors and exposed to ambient winter temperatures for chilling. They were then moved back to the eclectors in the greenhouse. Additionally, 18 living unattacked trees were provided with a pheromone lure in early spring 1995 to induce new bark beetle attack. 10 infested trees were then cut and processed as described above. The water-filled emergence traps of the eclectors were emptied monthly-bimonthly and the insects were separated to taxonomic groups and eventually identified by specialists. Before disposing the logs, tree age was recorded by tree-ring-counting. proprietary e1c0c34e0cc942898b3626efd1dcc095_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2014-10-10 2017-03-17 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143580-FEDEO.umm_json This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary -e2c223cdcb4844f9a1ffe9759b61eaf4_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143024-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary -e2f9d8f61a02431997361a8827eaf558_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142848-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) proprietary e3dbdc32f7b6476e949d52d8d3990205_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-01-26 2017-03-22 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142698-FEDEO.umm_json This dataset contains a time series of ice velocities for the Zachariae glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary e43aead9947549078c2d108b2c3632b2_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.3 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143265-FEDEO.umm_json The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.3 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 proprietary -e493802d83c846c8b76f817866fb74cc_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0 FEDEO STAC Catalog 2002-09-30 2012-04-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142824-FEDEO.umm_json The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section. proprietary +e493802d83c846c8b76f817866fb74cc_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0 FEDEO STAC Catalog 2002-10-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142824-FEDEO.umm_json The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section. proprietary e4f39152bc50466f8887bd2a343cac93_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Northern Drainage basin from ERS-1 for winter 1991-1992, v1.1 (June 2016 release) FEDEO STAC Catalog 1991-12-29 1992-03-22 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143266-FEDEO.umm_json This dataset contains ice velocities for the Greenland Northern Drainage Basin for winter 1991-1992, which have been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data has been derived from intensity-tracking of ERS-1 Ice phase (3 days repeat) data aquired between 29th December 1991 and 22nd March 1992.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevationmodel, is also provided. (Please note that in earlier versions of this product the horizontal velocities were provided as true East and North velocities). Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use this later v1.1 product. proprietary -e61704b00267405082fbd41bb710dd74_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8 FEDEO STAC Catalog 2009-03-31 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143069-FEDEO.umm_json The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4).The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document. proprietary +e61704b00267405082fbd41bb710dd74_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8 FEDEO STAC Catalog 2009-04-01 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143069-FEDEO.umm_json The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4).The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document. proprietary e670dada-89ec-45ee-a24f-06026dd9794b Cyclones Winds - Hazard, Wind Speed 100RP CEOS_EXTRA STAC Catalog 1970-01-01 -180, -55, 179.7721, 59.768925 https://cmr.earthdata.nasa.gov/search/concepts/C2232848611-CEOS_EXTRA.umm_json "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 100 years return period. It was generated by integration of the intensity values contained in the files ""Wind_Atlantic.AME"", ""Wind_EastPacific.AME"", ""Wind_NorthIndian.AME"", ""Wind_SudIndian.AME"", ""Wind_SudPacific.AME"" and ""Wind_WestPacific.AME""." proprietary e7d61dd1-570b-4e77-9d3c-78a92399c6fc_NA MERIS - Water Parameters - Lake Constance, Daily FEDEO STAC Catalog 2006-01-01 2010-03-18 8.76585, 47.4928, 9.81505, 47.874 https://cmr.earthdata.nasa.gov/search/concepts/C2207458010-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps. proprietary e7fa45e785a64481960c3b140038c948_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Hagen Glacier between 2017-06-30 and 2017-08-14, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-06-29 2017-08-14 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143147-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Hagen Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-30 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway. proprietary e80f28ccb0504c32b403eee654a8a5b3_NA ESA Land Cover Climate Change Initiative (Land_Cover_cci): MERIS Surface Reflectance FEDEO STAC Catalog 1999-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142923-FEDEO.umm_json This dataset consists of time series of surface reflectance from the MERIS instrument on the ENVISAT satellite, produced as part of the ESA Land Cover Climate Change Initiative (CCI) project. The time series are a temporal syntheses obtained over a 7-day compositing period, and encompass 13 of the 15 MERIS spectral channels (not including bands 11 and 15). The spatial resolution is 300m for the Full Resolution (FR) data and 1000m for the Reduced Resolution (RR) data.Given the amount and size of the MERIS surface reflectance archive (10 To), the Land Cover CCI team make the data available on request, through your own disks. Please contact contact@esa-landcover-cci.org proprietary -e94f2810c0794175b834153a71ac3182_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142989-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary -e9f82908fd9c48138b31e5cfaa6d692b_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142759-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites) covering the period 1997 - 2020. Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary eMASL1B_1 Enhanced MODIS Airborne Simulator (eMAS) Calibrated, Geolocated Radiances L1B 50m Data LAADS STAC Catalog 2013-08-01 2019-08-22 -180, -35, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2801308027-LAADS.umm_json The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. Prior to 1995, the MAS was deployed on the NASA's ER-2 and C-130 aircraft platforms using a 12-channel, 8-bit data system that somewhat constrained the full benefit of having a 50-channel scanning spectrometer. Beginning in January 1995, a 50-channel, 16-bit digitizer was used on the ER-2 platform, which greatly enhanced the capability of MAS to simulate MODIS data over a wide range of environmental conditions. Recently, it has undergone extensive upgrades to the optics and other components. New detectors have been installed and the spectral bands have been streamlined. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/ proprietary eMASL2AER_1 Enhanced MODIS Airborne Simulator (eMAS) L2 Aerosol Data LAADS STAC Catalog 2019-08-02 2019-08-23 -121.5, 30, -80, 47.9 https://cmr.earthdata.nasa.gov/search/concepts/C3229361786-LAADS.umm_json The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. The Enhanced MODIS Airborne Simulator (eMAS) L2 Aerosol Data product (eMASL2AER) consists of in-situ measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, follow plumes downwind to understand chemical transformation and air quality impacts, and assess the efficacy of satellite detections for estimating the emissions from sampled fires. These measurements were collected onboard the DC-8 aircraft during FIREX-AQ, during summer 2019. The DC-8 aircraft had a comprehensive instrument payload capable of measuring over 200 trace gases as well as aerosol microphysical, optical, and chemical properties. The eMASL2AER product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/ proprietary eMASL2CLD_1 Enhanced MODIS Airborne Simulator (eMAS) L2 Cloud Data LAADS STAC Catalog 2013-08-01 2016-09-28 -180, -35, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2801723593-LAADS.umm_json The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. The Enhanced MODIS Airborne Simulator (eMAS) L2 Cloud Data product (eMASL2CLD) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared and near infrared solar reflected radiances. Multispectral images of the reflectance and brightness temperature at 10 wavelengths between 0.66 and 13.98nm were used to derive the probability of clear sky (or cloud), cloud thermodynamic phase, and the optical thickness and effective radius of liquid water and ice clouds. The eMASL2CLD product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/ proprietary @@ -16127,7 +16018,6 @@ ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0 Ecosystem cou ecosystem_roots_1deg_929_1 ISLSCP II Ecosystem Rooting Depths ORNL_CLOUD STAC Catalog 1995-02-01 1995-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784847849-ORNL_CLOUD.umm_json The goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna. Roots transport nutrients and water upwards, but they are also pathways for carbon and nutrient transport into deeper soil layers and for deep water infiltration. Roots also affect the weathering rates of soil minerals. For calculating such processes on a global scale, data on vertical root distributions are needed as inputs to global biogeochemistry and vegetation models. In the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS), rooting depth and vertical soil characteristics were the most important factors explaining scatter for simulated transpiration among 14 land-surface models. Recently, the Terrestrial Observation Panel for Climate of the Global Climate Observation System (GCOS) identified the 95% rooting depth as a key variable needed to quantify the interactions between the climate, soil, and plants, stating that the main challenge was to find the correlation between rooting depth and soil and climate features (GCOS/GTOS Terrestrial Observation Panel for Climate 1997). In response to this challenge, a data set of vertical rooting depths was collected from the literature in order to construct maps of global ecosystem rooting depths.The parameters included in these data sets are estimates for the soil depths containing 50% and 95% of all roots, termed 50% and 95% rooting depths (D50 and D95, respectively). Together, these variables can be used to calculate estimates for vertical root distributions, using a logistic equation provided in this documentation. The data represent mean ecosystem rooting depths for 1 by 1 degree grid cells. Related data sets: The ORNL DAAC offers related data sets by Jackson et al. (2003), Gordon and Jackson (2003), Schenk and Jackson (2003), and Gill and Jackson (2003).This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets. proprietary ecousm1 A comparative study on floral ecology between Malaysia and Antarctica SCIOPS STAC Catalog 1970-01-01 110.32, -66.28, 110.32, -66.28 https://cmr.earthdata.nasa.gov/search/concepts/C1214621680-SCIOPS.umm_json The major objectives of this project are as follows: 1. To determine the composition and distribution of algal flora from a wide range of habitats, which provide a conductive niche for algal population in Antarctica. 2. To compare the Antarctic and tropical algal flora, in order to determine the degree of species endemism based on evolutionary process. 3. To study the important role of habitat specificity in determining the composition of diatom assemblages. 4. To test the utility and suitability of diatom community structure as indicators of environmental stress. This is done by: 1. Conducting an ecological survey of microalgal distribution at Australian Antarctic station sites by looking into several types of habitat. 2. Identifying the microalgae samples collected based on morphology using light microscopy and SEM. 3. Comparing the algae community, structure and distribution from the tropics. The principal milestones of the project are as follows: 1. Information of microalgal distribution at several sites in Antarctica. 2. Collection of microalgae cultures. 3. Completion of identification of Antarctic microalgae. In collaboration with the Australian Antarctic Division (AAD) we have gone on an expeditions to Australian Antarctic Station of Casey and Davis. Collection of samples was made from various sources such as water, snow and soil and we have established a list of microalgae species in our collection. Comparative studies on the species diversity and distribution with tropical microalgae communities are being conducted. Physiological studies are currently in progress. proprietary ect-and-rb-data-switzerland_1.0 ECT and RB data Switzerland ENVIDAT STAC Catalog 2020-01-01 2020-01-01 6.6500243, 45.8050626, 10.5831297, 47.4867706 https://cmr.earthdata.nasa.gov/search/concepts/C2789814654-ENVIDAT.umm_json "The data set contains the data used in the publication ""On snow stability interpretation of Extended Column Test results"" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences." proprietary -edaa7e7324e849f683d3726088a0c7bd_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 3.1 FEDEO STAC Catalog 1997-09-03 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142506-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, this the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary edc_landcover_xdeg_930_1 ISLSCP II IGBP DISCover and SiB Land Cover, 1992-1993 ORNL_CLOUD STAC Catalog 1986-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784854847-ORNL_CLOUD.umm_json This data set describes the geographic distributions of 17 classes of land cover based on the International Geosphere-Biosphere DISCover land cover legend (Loveland and Belward 1997) and the 15 classes of the SiB model processed at the USGS EROS Data Center (EDC). Specifically, the resampled DISCover datasets were derived from the 1km DISCover data set compiled by the USGS. The 1km data sets for each classification scheme were aggregated to 1, 0.5 and 0.25 degree spatial resolutions for this ISLSCP II data collection. Each layer of the aggregated products corresponds to a single DISCover land cover category and the values represent the percentage of the coarse resolution cell (1 degree, etc...)occupied by that land cover category. The dominant class data show the land cover category that occupies the majority of the cell and is derived from the percentage files for each cover type. The objective of this study was to create a land cover map derived from 1 kilometer AVHRR data using a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. During this re-processing, the original EDC land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by global modelers and others. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets. proprietary edgar_atmos_emissions_1deg_1022_1 ISLSCP II EDGAR 3 Gridded Greenhouse and Ozone Precursor Gas Emissions ORNL_CLOUD STAC Catalog 1970-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785350291-ORNL_CLOUD.umm_json The EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases CO2, CH4, N2O are provided on a 1.0 degree by 1.0 degree grid for the years 1970, 1980, 1990, and 1995 and for the tropospheric ozone precursor gases CO, NOx, NMVOC (Non-Methane Volatile Organic Compounds) and SO2 for the years 1990 and 1995. There are 2 *.zip data files with this data set. proprietary edna-fjord-svalbard-fish-plankton_1.0 Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord ENVIDAT STAC Catalog 2023-01-01 2023-01-01 10.645752, 78.9769189, 12.689209, 79.4215522 https://cmr.earthdata.nasa.gov/search/concepts/C3226081772-ENVIDAT.umm_json This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023). # Methods **Sampling** We sampled the Lilliehöök fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lilliehöök fjord merged with the mouth of Möller fjord. The fjord’s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-µm pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves. **Molecular** To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC). # Data content: + Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study. + Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder. # Additional informations For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9. proprietary @@ -16136,7 +16026,6 @@ eemma_1.0 eemma.R, an R script for Ensemble End-Member Mixing Analysis ENVIDAT S ef1627f523764eae8bbb6b81bf1f7a0a_NA ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.1 FEDEO STAC Catalog 1992-09-15 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142828-FEDEO.umm_json "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:• Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.• Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.• Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." proprietary ef5c6596cae548c6aea9dea181c7624c_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2014-10-09 2017-03-17 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143017-FEDEO.umm_json This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary ef6a9266-a210-4431-a4af-06cec4274726_NA Cartosat-1 (IRS-P5) - Panchromatic Images (PAN) - Europe, Monographic FEDEO STAC Catalog 2015-02-10 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207457985-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data. proprietary -ef8eb5ff84994f2ca416dbb2df7f72c7_NA ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2019), version 1.0 FEDEO STAC Catalog 2000-02-24 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143047-FEDEO.umm_json This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 – 2019. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps. proprietary effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0 Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.8225713, 46.796135, 9.8225713, 46.796135 https://cmr.earthdata.nasa.gov/search/concepts/C3226081817-ENVIDAT.umm_json The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this. The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions. We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy. This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example. Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization. proprietary effects-of-canopy-disturbance-on-swiss-forests_1.0 Effects of canopy disturbance on Swiss forests ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814791-ENVIDAT.umm_json "The files refer to the data used in Scherrer et al. (2021) ""Canopy disturbances catalyse tree species shifts in Swiss forests"" in _Ecosystems_. The two data files contain information about site factors (e.g. disturbance events, dominant tree species, elevation) and species-specific biomass of 5521 plots of the Swiss National Forest Inventory visited during the second (NFI2 1993-1995) and fourth (NFI4 2009-2017) inventory. In addition, we provide all the R-scripts necessary to reproduce the Figures and data tables of the related publication. For more detailed information about the data files please check the ReadMe.docx file." proprietary elev_arc_250_1 BOREAS Elevation Contours over the NSA and SSA ARC/Info Generate Format ORNL_CLOUD STAC Catalog 1970-01-01 1989-12-31 -105.23, 53.69, -98.09, 56.06 https://cmr.earthdata.nasa.gov/search/concepts/C2846961083-ORNL_CLOUD.umm_json Elevation contours over the NSA and SSA in ARC/Info Generate Format. Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000. proprietary @@ -16222,19 +16111,19 @@ exrad3dimpacts_1 ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS GHRC_DAAC STAC Catal exradepoch_1 ER-2 X-Band Doppler Radar (EXRAD) EPOCH GHRC_DAAC STAC Catalog 2017-08-09 2017-08-31 -124.717, 16.603, -83.6115, 34.9083 https://cmr.earthdata.nasa.gov/search/concepts/C2132312390-GHRC_DAAC.umm_json The ER-2 X-Band Doppler Radar (EXRAD) EPOCH dataset consists of radar reflectivity and Doppler velocity estimates collected by the EXRAD onboard the AV-6 Global Hawk Unmanned Aerial Vehicle research aircraft, though traditionally this instrument is flown on the NASA ER-2 aircraft. These data were gathered during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The EXRAD EPOCH dataset files are available from August 9, 2017 through August 31, 2017 in HDF-5 format. proprietary exradimpacts_1 ER-2 X-Band Doppler Radar (EXRAD) IMPACTS GHRC_DAAC STAC Catalog 2020-01-25 2023-03-02 -117.635, 27.106, -67.286, 48.658 https://cmr.earthdata.nasa.gov/search/concepts/C1997744595-GHRC_DAAC.umm_json The ER-2 X-band Radar (EXRAD) IMPACTS dataset consists of radar reflectivity and Doppler velocity estimates collected by the EXRAD onboard the NASA ER-2 high-altitude research aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The EXRAD IMPACTS dataset files are available from January 25, 2020, through March 2, 2023, in HDF-5 format. proprietary f0580e34da524770b0a5d43c033b33dc_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.2 FEDEO STAC Catalog 1978-11-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142979-FEDEO.umm_json The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 proprietary +f1445bde2f1249c99bb5a59b71e9a9d7_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00 FEDEO STAC Catalog 1995-08-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327358896-FEDEO.umm_json This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary f17f146a31b14dfd960cde0874236ee5_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1 FEDEO STAC Catalog 2002-05-31 2017-05-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142738-FEDEO.umm_json The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing. This version of the product is v2.1, which is an extension of the v2.0 Sea_Ice_cci data and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 50 km grid spacing is also available. proprietary f1ab07b5292f4813bd3090b51d270aa8_NA ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0 FEDEO STAC Catalog 2000-02-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143056-FEDEO.umm_json The Cloud_cci MODIS-Terra dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Terra) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Terra dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures. proprietary f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d 3 year daily average solar exposure map Mali 3Km GRAS September 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603974-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for September. proprietary -f30495d4425f46c489765a2f84dd6862_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 5.0 FEDEO STAC Catalog 1997-09-04 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142951-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary f31e8e988c4144bebe13892b53d08e42_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the 79Fjord Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-06-24 2017-08-10 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142547-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the 79Fjord Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The data have been produced by S[&]T Norway proprietary f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA IRS-P6 Resourcesat-1 - Multispectral Images (LISS-IV) - Europe, Multispectral Mode FEDEO STAC Catalog 2004-01-29 2009-01-31 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458036-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary f428fffb26cf4cd5b97dfb6381cb16bb_NA ESA Ozone Climate Change Initiative (Ozone CCI): OSIRIS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2001-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143180-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for OSIRIS in 2008. proprietary +f4654030223445b0bac63a23aaa60620_NA ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0 FEDEO STAC Catalog 1982-01-01 2019-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359735-FEDEO.umm_json This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The CryoClim FSC time series provides daily products for the period 1982 – 2019. The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.For the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.The algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008). proprietary f4c34f4f0f1d4d0da06d771f6972f180_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0 FEDEO STAC Catalog 2002-09-30 2012-03-31 -180, 16, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142502-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April. proprietary f5ffbd016e6b44858a33ae38ed2a149e_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.1 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143230-FEDEO.umm_json The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.The v06.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 proprietary -f9154243fd8744bdaf2a59c39033e659_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0 FEDEO STAC Catalog 2009-04-17 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143252-FEDEO.umm_json This CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.) The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the data is v7.0 and forms part of the Climate Research Data Package 4.This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.The product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary +f9154243fd8744bdaf2a59c39033e659_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0 FEDEO STAC Catalog 2009-04-18 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143252-FEDEO.umm_json This CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.) The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the data is v7.0 and forms part of the Climate Research Data Package 4.This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.The product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary f920473c-15a2-490c-8b24-b48f9b8a0226_NA Firebird MSC - Level 0 Multispectral Images FEDEO STAC Catalog 2014-03-22 -180, -83, 180, 83 https://cmr.earthdata.nasa.gov/search/concepts/C2207458028-FEDEO.umm_json The FireBIRD mission consists of two small satellites, TET-1 and BIROS. Together, the two satellites are on an Earth observation mission that aims to detect forest fires, or high-temperature events, from space. The new infrared system provides high-quality data that is capable of measuring the spread of the fire and the amount of heat generated with great accuracy very early on - almost in real time - meaning that FireBIRD can serve as an early warning system. The data acquired from this Earth observation mission can also be used as a basis for scientific climate research. In addition to the main payload of the cameras, further experiments have been planned for developing the technology on board the small satellites. Further information can be found on the following website: http://www.dlr.de/firebird/en/ and in the FireBIRD brochure available at: http://www.dlr.de/firebird/en/Portaldata/79/Resources/dokumente/FireBIRD_Broschuere_HighRes_v3_english.pdf proprietary f97068fa-c098-4521-87ec-357c6e3b6960_NA MERIS - Water Parameters - Lake Constance, 10-Day FEDEO STAC Catalog 2006-01-01 2010-03-10 8.76585, 47.4928, 9.81505, 47.874 https://cmr.earthdata.nasa.gov/search/concepts/C2207458043-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-days maps. proprietary -fa20aaa2060e40cabf5fedce7a9716d0_NA ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2018), version 1.0 FEDEO STAC Catalog 1979-01-06 2018-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142587-FEDEO.umm_json Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces; in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979 to 2018. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.The dataset was aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2018, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland. proprietary fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA TerraSAR-X - Spotlight Images (TerraSAR-X Spotlight) FEDEO STAC Catalog 2007-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458031-FEDEO.umm_json "This collection contains radar image products of the German national TerraSAR-X mission acquired in Spotlight mode. Spotlight imaging allows for a spatial resolution of up to 2 m at a scene size of 10 km (across swath) x 10 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html" proprietary faamwdat_237_1 BOREAS AFM-02 King Air 1994 Aircraft Flux and Moving Window Data ORNL_CLOUD STAC Catalog 1994-05-25 1994-09-17 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2807614767-ORNL_CLOUD.umm_json Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994. proprietary face-stillberg_1.0 FACE: Stillberg CO2 enrichment and soil warming study ENVIDAT STAC Catalog 2016-01-01 2016-01-01 9.867544, 46.7716544, 9.867544, 46.7716544 https://cmr.earthdata.nasa.gov/search/concepts/C2789815224-ENVIDAT.umm_json # Background information High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas compared to most other regions of the world. This project involved a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 until 2009, and a soil warming treatment (+4 °C) was applied from 2007 until 2012. The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research was carried out as part of this environmental change project. # Experimental design The experiment consisted of 40 hexagonal 1.1 m² plots, 20 with a *Pinus mugo* ssp. *uncinata* (mountain pine, evergreen) individual in the centre and 20 with a *Larix decidua* (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounded the tree in each plot, including the dominant dwarf shrub species *Vaccinium myrtillus* (bilberry), *Vaccinium gaultherioides* (group *V. uliginosum agg.*, northern bilberry) and *Empetrum nigrum* ssp. *hermaphroditum* (crowberry) plus several herbaceous and non-vascular species. At the beginning of the experimental period, the 40 plots were assigned to ten groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment, while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species. # Data description Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009). The data comprise of air temperature, soil temperature, soil moisture, sapflow, tree diameter and CO2 measurements. proprietary @@ -16248,12 +16137,11 @@ fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0 Fatal avalanche accid fatal-avalanche-accidents-switzerland-1995_1.0 Fatal avalanche accidents in Switzerland since 1995-1996 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815287-ENVIDAT.umm_json Attention: this data is not updated after 2022 anymore. This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added. The following information is provided: * avalanche identifier * date of the accident * accuracy of the date in range of days before and after * canton * name of the locality * start zone of the avalanche * coordinates (Swiss coordinate system, approximately in middle of start zone) * accuracy of the coordinates in meters * elevation (in meteres above sea level, app. in middle of start zone) * slope aspect (main orientation of start zone) * slope inclination (in degree, steepest point within start zone) * number of dead persons * number of caught persons * number of fully buried persons * forecasted avalanche danger level * activity/location of the accident party at the time of the incident proprietary fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA MERIS - Water Parameters - North Sea, Monthly FEDEO STAC Catalog 2006-01-01 2010-02-28 -6.10393, 49.9616, 11.4301, 61.9523 https://cmr.earthdata.nasa.gov/search/concepts/C2207458017-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps. proprietary fb3750f5b2544403873f8788b3ed7817_NA ESA Cloud Climate Change Initiative (Cloud CCI): AVHRR-AM monthly gridded cloud properties, version 3.0 FEDEO STAC Catalog 1991-09-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142505-FEDEO.umm_json The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003. proprietary -fb4b4be0-a4a3-4dcd-b381-bacde381d3eb_NA MERIS - Gap Free Leaf Area Index (LAI) - Global FEDEO STAC Catalog 2003-01-01 2011-01-31 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458057-FEDEO.umm_json This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data. proprietary fbfae06e787b4fefb4b03cba2fd04bc3_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0 FEDEO STAC Catalog 2010-11-01 2017-04-30 -180, -88, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C2548142550-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information. proprietary fdp-grapevine-trunks-impact-xylem-phloem_1.0 Impact of the “Flavescence dorée” phytoplasma on xylem growth and phloem anomalies in trunks of ‘Chardonnay’ grapevines (Vitis vinifera) ENVIDAT STAC Catalog 2022-01-01 2022-01-01 8.9400816, 46.042408, 8.9438152, 46.04494 https://cmr.earthdata.nasa.gov/search/concepts/C2789815299-ENVIDAT.umm_json "Dataset collected from dendroecological study on trunks of grapevines ('Chardonnay' cv.) infected by the ""Flavescence dorée"" phytoplasma (FDp) in Origlio (southern Switzerland) in 2019-2020. Ring widths were measured with cellSens (Olympus Corporation). Calculations and analysis were conducted within R. The Flavescence dorée phytoplasma (FDp) causes a severe grapevine (Vitis vinifera) disease. Anatomical modification due to FDp infections are known to occur but research so far focused on stems and leaf tissues and, in particular, on their phloem structure. In this paper, we applied dendrochronological techniques on wood rings and analysed the anatomical structures of the trunk of the susceptible grapevine cultivar ‘Chardonnay’ in order to verify their response to FDp infections. In this study, we tested the impact of FDp and drought stress on xylem ring width and also described phloem anomalies inside the trunk of grapevines. We concluded that drought and FDp infection both have a significant effect on ring width reductions and that FDp supersedes the effect of drought conditions (calculated after the SPEI index) in infected specimens." proprietary fe651dbef5d44248bef70906f4b3d12b_NA ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2001-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143134-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SMR_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for ODIN/SMR in 2008. proprietary feral_cat_macca_1 Biology of the Feral Cat, Felis catus (L.), on Macquarie Island AU_AADC STAC Catalog 1975-12-01 1981-02-28 158.86386, -54.692, 158.94331, -54.4977 https://cmr.earthdata.nasa.gov/search/concepts/C1214308566-AU_AADC.umm_json From the referenced paper: Between December 1976 and February 1981, 246 cats were collected. Overall sex ratio was in favour of males 1:0.8, and coat colour was tabby (74%), orange (26%) and black (2%). The breeding season extended from October to March with the peak in November-December. Mean number of embryos was 4.7 per female and evidence of females producing two litters was found. Mortality in kittens increased as they grew older, with litters of kittens greater than 1.8 kg containing two or fewer animals. Most cats lived in herbfield or tussock grassland, with very few if any in feldmark. The total population was estimated at between 169 and 252 adult cats. Observations of an adult male showed that its home range covered 41 ha, but this appeared not to be maintained during winter. It's daytime activity varied greatly, much time being spent foraging for food. Domestic cats Felis catus (L.) were feral on Macquarie Island by 1820, only 10 years after the island was discovered by sealers. Their presence was soon noted by early naturalists. Depredations by cats greatly reduced the numbers of burrow-nesting petrels and, together with the weka Gallirallus australis, cats were probably responsible for the extinction of the endemic parakeet Cyanoramphus novaezelandiae erythrotis and banded rail Rallus phillippensis before 1900. Feral cats are common on several other subantarctic islands and have been intensively studied; the only previous study on Macquarie Island was on diet. This study reports on other aspects of the biology of the feral cat on Macquarie Island. proprietary -ff4bfe39b7fe42fc993341d3cebdabb5_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5 FEDEO STAC Catalog 2002-03-31 2016-06-30 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143051-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013. proprietary +ff4bfe39b7fe42fc993341d3cebdabb5_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5 FEDEO STAC Catalog 2002-04-01 2016-06-30 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143051-FEDEO.umm_json This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013. proprietary ff79d140824f42dd92b204b4f1e9e7c2_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0 FEDEO STAC Catalog 2010-11-01 2017-04-30 -180, 16, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142887-FEDEO.umm_json This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April. proprietary ffo_Betts_1987-1989_afd_93_1 Site Averaged Flux Data: 1987-1989 (Betts) ORNL_CLOUD STAC Catalog 1987-05-27 1989-08-16 -96.61, 38.98, -96.45, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2810660344-ORNL_CLOUD.umm_json Site averaged product of the flux data collected by many PIs during the 1987-1989 FIFE experiment. Data are in 30-minute intervals and include the entire period 1987-1989. proprietary ffo_Betts_1987-1989_ams_89_1 Site Averaged AMS Data: 1987-1989 (Betts) ORNL_CLOUD STAC Catalog 1987-05-01 1989-11-19 -96.61, 38.98, -96.45, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2810659841-ORNL_CLOUD.umm_json Site averaged product of the Portable Automatic Meteorological Station (AMS) data acquired during the 1987-1989 FIFE experiment. Data are in 30-minute time intervals. proprietary