From 2f123b7c54b285201ea24c0e7d7c44ee75323606 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Wed, 4 Sep 2024 04:56:38 +0000 Subject: [PATCH] Updated datasets 2024-09-04 UTC --- gee_catalog.json | 174 +++++++++-------- gee_catalog.tsv | 157 ++++++++-------- nasa_cmr_catalog.json | 420 +++++++++++++++++++++++++++++++++++++----- nasa_cmr_catalog.tsv | 42 ++++- 4 files changed, 588 insertions(+), 205 deletions(-) diff --git a/gee_catalog.json b/gee_catalog.json index b9dbbe0..1854068 100644 --- a/gee_catalog.json +++ b/gee_catalog.json @@ -708,7 +708,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S1_GRD')", "provider": "European Union/ESA/Copernicus", "state_date": "2014-10-03", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel", @@ -726,7 +726,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2')", "provider": "European Union/ESA/Copernicus", "state_date": "2015-06-27", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -56, 180, 83", "deprecated": true, "keywords": "copernicus, esa, eu, msi, radiance, sentinel", @@ -744,7 +744,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')", "provider": "European Union/ESA/Copernicus/SentinelHub", "state_date": "2015-06-27", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub", @@ -762,7 +762,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_HARMONIZED')", "provider": "European Union/ESA/Copernicus", "state_date": "2015-06-27", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "copernicus, esa, eu, msi, radiance, sentinel", @@ -780,7 +780,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_SR')", "provider": "European Union/ESA/Copernicus", "state_date": "2017-03-28", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -56, 180, 83", "deprecated": true, "keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr", @@ -798,7 +798,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')", "provider": "European Union/ESA/Copernicus", "state_date": "2017-03-28", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr", @@ -816,7 +816,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S3/OLCI')", "provider": "European Union/ESA/Copernicus", "state_date": "2016-10-18", - "end_date": "2024-09-01", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "copernicus, esa, eu, olci, radiance, sentinel, toa", @@ -834,7 +834,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -852,7 +852,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -870,7 +870,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-05", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi", @@ -888,7 +888,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-11-22", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi", @@ -906,7 +906,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-10-02", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi", @@ -924,7 +924,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi", @@ -942,7 +942,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -960,7 +960,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi", @@ -978,7 +978,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -996,7 +996,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -1014,7 +1014,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4')", "provider": "European Union/ESA/Copernicus", "state_date": "2019-02-08", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi", @@ -1032,7 +1032,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi", @@ -1050,7 +1050,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-06-28", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi", @@ -1068,7 +1068,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-12-05", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi", @@ -1086,7 +1086,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-06-28", - "end_date": "2024-08-24", + "end_date": "2024-08-25", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi", @@ -1104,7 +1104,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-09-08", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -1122,7 +1122,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-04-30", - "end_date": "2024-08-18", + "end_date": "2024-08-19", "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_SO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-12-05", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi", @@ -1554,7 +1554,7 @@ "snippet": "ee.ImageCollection('ECMWF/CAMS/NRT')", "provider": "European Centre for Medium-Range Weather Forecasts (ECMWF)", "state_date": "2016-06-22", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter", @@ -1608,7 +1608,7 @@ "snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR')", "provider": "Daily Aggregates: Google and Copernicus Climate Data Store", "state_date": "1950-01-02", - "end_date": "2024-08-26", + "end_date": "2024-08-27", "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", @@ -1626,7 +1626,7 @@ "snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY')", "provider": "Copernicus Climate Data Store", "state_date": "1950-01-01", - "end_date": "2024-08-26", + "end_date": "2024-08-27", "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", @@ -2382,7 +2382,7 @@ "snippet": "ee.ImageCollection('FIRMS')", "provider": "NASA / LANCE / EOSDIS", "state_date": "2000-11-01", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal", @@ -2670,7 +2670,7 @@ "snippet": "ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')", "provider": "Google Earth Engine", "state_date": "2015-06-27", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "google, cloud, sentinel2_derived", @@ -2688,7 +2688,7 @@ "snippet": "ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1')", "provider": "World Resources Institute", "state_date": "2015-06-27", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "global, google, landcover, landuse, nrt, sentinel2_derived", @@ -2904,7 +2904,7 @@ "snippet": "ee.ImageCollection('HYCOM/sea_surface_elevation')", "provider": "NOPP", "state_date": "1992-10-02", - "end_date": "2024-09-01", + "end_date": "2024-09-02", "bbox": "-180, -80.48, 180, 80.48", "deprecated": false, "keywords": "elevation, hycom, nopp, ocean, ssh, water", @@ -2922,7 +2922,7 @@ "snippet": "ee.ImageCollection('HYCOM/sea_temp_salinity')", "provider": "NOPP", "state_date": "1992-10-02", - "end_date": "2024-09-01", + "end_date": "2024-09-02", "bbox": "-180, -80.48, 180, 80.48", "deprecated": false, "keywords": "hycom, nopp, ocean, salinity, sst, water, water_temp", @@ -2940,7 +2940,7 @@ "snippet": "ee.ImageCollection('HYCOM/sea_water_velocity')", "provider": "NOPP", "state_date": "1992-10-02", - "end_date": "2024-09-01", + "end_date": "2024-09-02", "bbox": "-180, -80.48, 180, 80.48", "deprecated": false, "keywords": "hycom, nopp, ocean, velocity, water", @@ -2958,7 +2958,7 @@ "snippet": "ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')", "provider": "University of California Merced", "state_date": "1979-01-01", - "end_date": "2024-08-30", + "end_date": "2024-08-31", "bbox": "-124.9, 24.9, -66.8, 49.6", "deprecated": false, "keywords": "climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind", @@ -3696,7 +3696,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index", @@ -3750,7 +3750,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst", @@ -3804,7 +3804,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-08-30", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color", @@ -3858,7 +3858,7 @@ "snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3')", "provider": "Global Change Observation Mission (GCOM)", "state_date": "2021-11-29", - "end_date": "2024-08-30", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst", @@ -3876,7 +3876,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "2014-03-01", - "end_date": "2024-09-01", + "end_date": "2024-09-03", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -3912,7 +3912,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "2014-03-01", - "end_date": "2024-09-01", + "end_date": "2024-09-03", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -3930,7 +3930,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "1998-01-01", - "end_date": "2024-09-01", + "end_date": "2024-09-03", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -5388,7 +5388,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT')", "provider": "USGS", "state_date": "2013-03-18", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs", @@ -5406,7 +5406,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')", "provider": "USGS/Google", "state_date": "2013-03-18", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l8, landsat, lc8, toa, usgs", @@ -5496,7 +5496,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs", @@ -5532,7 +5532,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA')", "provider": "USGS/Google", "state_date": "2021-10-31", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, landsat, toa, usgs", @@ -5550,7 +5550,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2')", "provider": "USGS", "state_date": "2021-11-02", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs", @@ -5586,7 +5586,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA')", "provider": "USGS/Google", "state_date": "2021-11-02", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, toa, usgs", @@ -9636,7 +9636,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf')", "provider": "NASA / GMAO", "state_date": "2022-10-01", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9654,7 +9654,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr')", "provider": "NASA / GMAO", "state_date": "2022-10-01", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9672,7 +9672,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf')", "provider": "NASA / GMAO", "state_date": "2018-01-01", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9690,7 +9690,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr')", "provider": "NASA / GMAO", "state_date": "2018-01-01", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -10158,7 +10158,7 @@ "snippet": "ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2')", "provider": "NASA / LANCE / NOAA20_VIIRS", "state_date": "2023-10-08", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs", @@ -10176,7 +10176,7 @@ "snippet": "ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2')", "provider": "NASA / LANCE / SNPP_VIIRS", "state_date": "2023-09-03", - "end_date": "2024-08-31", + "end_date": "2024-09-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs", @@ -10284,7 +10284,7 @@ "snippet": "ee.ImageCollection('NASA/NLDAS/FORA0125_H002')", "provider": "NASA GES DISC at NASA Goddard Space Flight Center", "state_date": "1979-01-01", - "end_date": "2024-08-28", + "end_date": "2024-08-30", "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", @@ -10428,7 +10428,7 @@ "snippet": "ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006')", "provider": "Google and NSIDC", "state_date": "2023-12-04", - "end_date": "2024-08-30", + "end_date": "2024-08-31", "bbox": "-180, -84, 180, 84", "deprecated": false, "keywords": "drought, nasa, smap, soil_moisture, surface, weather", @@ -10446,7 +10446,7 @@ "snippet": "ee.ImageCollection('NASA/SMAP/SPL4SMGP/007')", "provider": "Google and NSIDC", "state_date": "2015-03-31", - "end_date": "2024-08-29", + "end_date": "2024-09-01", "bbox": "-180, -84, 180, 84", "deprecated": false, "keywords": "drought, nasa, smap, soil_moisture, surface, weather", @@ -10644,7 +10644,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/sea_level_pressure')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-08-29", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis", @@ -10662,7 +10662,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/surface_temp')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-08-29", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature", @@ -10680,7 +10680,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/surface_wv')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-08-29", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor", @@ -10914,7 +10914,7 @@ "snippet": "ee.ImageCollection('NOAA/CDR/OISST/V2_1')", "provider": "NOAA", "state_date": "1981-09-01", - "end_date": "2024-08-30", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature", @@ -10986,7 +10986,7 @@ "snippet": "ee.ImageCollection('NOAA/CFSR')", "provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)", "state_date": "2018-12-13", - "end_date": "2024-09-01", + "end_date": "2024-09-02", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather", @@ -11058,7 +11058,7 @@ "snippet": "ee.ImageCollection('NOAA/GFS0P25')", "provider": "NOAA/NCEP/EMC", "state_date": "2015-07-01", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind", @@ -11076,7 +11076,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/FDCC')", "provider": "NOAA", "state_date": "2017-05-24", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "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", @@ -11094,7 +11094,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/FDCF')", "provider": "NOAA", "state_date": "2017-05-24", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire", @@ -11112,7 +11112,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPC')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "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", @@ -11130,7 +11130,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPF')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -11148,7 +11148,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPM')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -11256,7 +11256,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/FDCC')", "provider": "NOAA", "state_date": "2022-10-13", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "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", @@ -11274,7 +11274,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/FDCF')", "provider": "NOAA", "state_date": "2022-10-13", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire", @@ -11292,7 +11292,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPC')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, 14.57, 180, 53.51", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11310,7 +11310,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPF')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11328,7 +11328,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPM')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-09-02", + "end_date": "2024-09-03", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11436,7 +11436,7 @@ "snippet": "ee.ImageCollection('NOAA/NWS/RTMA')", "provider": "NOAA/NWS", "state_date": "2011-01-01", - "end_date": "2024-09-01", + "end_date": "2024-09-02", "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", @@ -11814,7 +11814,7 @@ "snippet": "ee.ImageCollection('OREGONSTATE/PRISM/AN81d')", "provider": "PRISM / OREGONSTATE", "state_date": "1981-01-01", - "end_date": "2024-08-29", + "end_date": "2024-08-31", "bbox": "-125, 24, -66, 50", "deprecated": false, "keywords": "climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather", @@ -12948,7 +12948,7 @@ "snippet": "ee.ImageCollection('TOMS/MERGED')", "provider": "NASA / GES DISC", "state_date": "1978-11-01", - "end_date": "2024-08-30", + "end_date": "2024-08-31", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms", @@ -13901,7 +13901,7 @@ "type": "image_collection", "snippet": "ee.ImageCollection('USGS/3DEP/1m')", "provider": "United States Geological Survey", - "state_date": "1998-08-16", + "state_date": "2015-01-01", "end_date": "2006-01-01", "bbox": "-171, -16.6, 164, 76.9", "deprecated": false, @@ -14586,7 +14586,7 @@ "snippet": "ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1')", "provider": "Institute of Industrial Science, The University of Tokyo, Japan", "state_date": "2007-01-01", - "end_date": "2024-08-31", + "end_date": "2024-09-02", "bbox": "60, -60, 180, 60", "deprecated": false, "keywords": "drought, kbdi, lst_derived, rainfall, utokyo, wtlab", @@ -15047,6 +15047,24 @@ "terms_of_use": "https://developers.google.com/earth-engine/datasets/catalog/WRI_GPPD_power_plants#terms-of-use", "license": "CC-BY-4.0" }, + { + "id": "WRI/SBTN/naturalLands/v1", + "title": "SBTN Natural Lands Map v1", + "type": "image_collection", + "snippet": "ee.ImageCollection('WRI/SBTN/naturalLands/v1')", + "provider": "WRI", + "state_date": "2020-01-01", + "end_date": "2020-01-01", + "bbox": "-180, -60, 180, 75", + "deprecated": false, + "keywords": "wri, landcover", + "catalog": "https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_SBTN_naturalLands_v1.json", + "url": "https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1", + "thumbnail": "https://developers.google.com/earth-engine/datasets/images/WRI/WRI_SBTN_naturalLands_v1_sample.png", + "script": "https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_SBTN_naturalLands_v1", + "terms_of_use": "https://spdx.org/licenses/CC-BY-NC-SA-4.0.html", + "license": "CC-BY-NC-SA-4.0" + }, { "id": "WWF/HydroATLAS/v1/Basins/level03", "title": "WWF HydroATLAS Basins Level 03", diff --git a/gee_catalog.tsv b/gee_catalog.tsv index 41b2370..6434901 100644 --- a/gee_catalog.tsv +++ b/gee_catalog.tsv @@ -38,31 +38,31 @@ 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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-01 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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_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 2024-09-02 -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 2024-09-02 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-08-24 -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 2024-08-31 -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 2024-08-18 -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 2024-08-31 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-02 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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_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 2024-09-03 -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 2024-09-03 -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 2024-09-01 -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 2024-09-01 -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 2024-09-01 -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 2024-09-01 -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 2024-09-01 -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 2024-09-01 -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 2024-08-25 -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 2024-09-01 -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 2024-08-19 -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 2024-09-01 -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 @@ -85,11 +85,11 @@ CSP/ERGo/1_0/US/topoDiversity US NED Topographic Diversity image ee.Image('CSP/E CSP/HM/GlobalHumanModification CSP gHM: Global Human Modification image_collection ee.ImageCollection('CSP/HM/GlobalHumanModification') Conservation Science Partners 2016-01-01 2016-12-31 -180, -90, 180, 90 False csp, fragmentation, human_modification, landcover, landscape_gradient, stressors, tnc https://storage.googleapis.com/earthengine-stac/catalog/CSP/CSP_HM_GlobalHumanModification.json https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification CC-BY-NC-SA-4.0 DLR/WSF/WSF2015/v1 World Settlement Footprint 2015 image ee.Image('DLR/WSF/WSF2015/v1') Deutsches Zentrum für Luft- und Raumfahrt (DLR) 2015-01-01 2016-01-01 -180, -90, 180, 90 False landcover, landsat_derived, sentinel1_derived, settlement, urban https://storage.googleapis.com/earthengine-stac/catalog/DLR/DLR_WSF_WSF2015_v1.json https://developers.google.com/earth-engine/datasets/catalog/DLR_WSF_WSF2015_v1 CC0-1.0 DOE/ORNL/LandScan_HD/Ukraine_202201 LandScan High Definition Data for Ukraine, January 2022 image ee.Image('DOE/ORNL/LandScan_HD/Ukraine_202201') Oak Ridge National Laboratory 2022-01-01 2022-02-01 22.125, 44.175, 40.225, 52.4 False landscan, population, ukraine https://storage.googleapis.com/earthengine-stac/catalog/DOE/DOE_ORNL_LandScan_HD_Ukraine_202201.json https://developers.google.com/earth-engine/datasets/catalog/DOE_ORNL_LandScan_HD_Ukraine_202201 CC-BY-4.0 -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 2024-09-02 -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/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 2024-09-03 -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 2024-08-26 -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 2024-08-26 -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 2024-08-27 -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 2024-08-27 -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 2024-07-01 -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 2024-07-01 -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 @@ -131,7 +131,7 @@ 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 2024-08-31 -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 2024-09-02 -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 59, 18, 69.4, 29.2 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 59, 18, 69.4, 29.2 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 @@ -147,8 +147,8 @@ 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 2024-09-02 -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 2024-09-02 -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 2024-09-03 -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 2024-09-03 -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/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 GOOGLE/Research/open-buildings/v2/polygons Open Buildings V2 Polygons [deprecated] table ee.FeatureCollection('GOOGLE/Research/open-buildings/v2/polygons') Google Research - Open Buildings 2022-08-30 2022-08-30 -180, -90, 180, 90 True africa, asia, building, built_up, open_buildings, south_asia, southeast_asia, structure https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings_v2_polygons.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v2_polygons CC-BY-4.0 @@ -160,10 +160,10 @@ HU_BERLIN/EPFD/V2/polygons European Primary Forest Dataset - Polygons table ee.F HYCOM/GLBu0_08/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation [deprecated] image_collection ee.ImageCollection('HYCOM/GLBu0_08/sea_surface_elevation') NOPP 1992-10-02 2018-12-09 -180, -80.48, 180, 80.48 True elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_surface_elevation proprietary HYCOM/GLBu0_08/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity [deprecated] image_collection ee.ImageCollection('HYCOM/GLBu0_08/sea_temp_salinity') NOPP 1992-10-02 2018-12-09 -180, -80.48, 180, 80.48 True hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_temp_salinity proprietary HYCOM/GLBu0_08/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity [deprecated] image_collection ee.ImageCollection('HYCOM/GLBu0_08/sea_water_velocity') NOPP 1992-10-02 2018-12-09 -180, -80.48, 180, 80.48 True hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_water_velocity proprietary -HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-09-01 -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 2024-09-01 -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 2024-09-01 -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 2024-08-30 -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 +HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-08-31 -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 @@ -204,20 +204,20 @@ 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 2024-08-31 -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 2024-09-02 -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 2024-08-31 -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 2024-09-02 -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 2024-08-30 -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 2024-08-31 -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) 2021-11-29 2024-08-30 -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 2024-09-01 -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) 2021-11-29 2024-08-31 -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 2024-09-03 -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 2024-09-01 -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 2024-09-01 -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 2024-09-03 -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 2024-09-03 -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/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 JRC/GFC2020/V1 EC JRC global map of forest cover 2020, V1 image_collection ee.ImageCollection('JRC/GFC2020/V1') Joint Research Centre, European Commission 2020-12-31 2020-12-31 -180, -90, 180, 90 False eudr, forest, jrc https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_V1 proprietary @@ -298,18 +298,18 @@ 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 2024-08-28 -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 2024-08-28 -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 2024-09-02 -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 2024-09-02 -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 2024-09-03 -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 2024-09-03 -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 2024-08-28 -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 2024-08-28 -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 2024-08-28 -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 2024-08-28 -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 2024-09-02 -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 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-09-03 -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 2024-08-31 -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 2024-09-02 -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 2024-09-02 -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/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 2024-09-03 -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 2024-09-03 -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 2024-08-31 -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 2024-09-02 -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/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 2024-09-03 -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 @@ -534,10 +534,10 @@ NASA/EMIT/L2B/CH4ENH Earth Surface Mineral Dust Source Investigation- Methane En 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 2024-07-28 -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 2024-07-01 -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, 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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-08-18 -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 2024-04-30 -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 @@ -563,14 +563,14 @@ NASA/GSFC/MERRA/rad/2 MERRA-2 M2T1NXRAD: Radiation Diagnostics V5.12.4 image_col 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 2024-08-01 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-09-01 -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 2024-09-01 -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, 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, 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, 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 2024-08-28 -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 2024-08-30 -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 @@ -578,8 +578,8 @@ NASA/ORNL/DAYMET_V3 Daymet V3: Daily Surface Weather and Climatological Summarie NASA/ORNL/DAYMET_V4 Daymet V4: Daily Surface Weather and Climatological Summaries image_collection ee.ImageCollection('NASA/ORNL/DAYMET_V4') NASA ORNL DAAC at Oak Ridge National Laboratory 1980-01-01 2023-12-31 -150.8, 1.6, -1.1, 84 False climate, daily, daylight, daymet, flux, geophysical, nasa, ornl, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_DAYMET_V4.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V4 proprietary 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/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 2024-08-30 -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 2024-08-29 -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/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 2024-08-31 -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 2024-09-01 -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 2024-08-23 -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 2024-08-04 -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 2024-08-04 -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 @@ -590,9 +590,9 @@ NASA/VIIRS/002/VNP21A1N VNP21A1N.002: Night Land Surface Temperature and Emissiv 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 2024-08-29 -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 2024-08-29 -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 2024-08-29 -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 2024-08-31 -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 2024-08-31 -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 2024-08-31 -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 2024-06-30 -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 @@ -605,36 +605,36 @@ 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 2024-08-30 -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 2024-08-31 -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 2024-09-01 -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/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-09-02 -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 2024-09-01 -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/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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-02 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-09-03 -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 2024-07-31 -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 2024-09-01 -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 2024-09-02 -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 @@ -655,7 +655,7 @@ 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 2024-04-01 -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 50.75, 3.2, 53.7, 7.22 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 2024-08-29 -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 2024-08-31 -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 2024-07-01 -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 @@ -718,7 +718,7 @@ 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 2024-08-30 -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 2024-08-31 -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 @@ -771,7 +771,7 @@ USFS/GTAC/MTBS/burned_area_boundaries/v1 MTBS Burned Area Boundaries table ee.Fe USFS/GTAC/TreeMap/v2016 USFS TreeMap v2016 (Conterminous United States) image_collection ee.ImageCollection('USFS/GTAC/TreeMap/v2016') USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) 2016-01-01 2017-01-01 -128.97722, 22.76862, -65.25445, 51.64968 False biomass, carbon, climate_change, conus, forest, forest_type, gtac, landcover, landfire, usfs, treemap, redcastle_resources, tree_cover, vegetation, forest_inventory_and_analysis https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_TreeMap_v2016.json https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2016 proprietary USGS/3DEP/10m USGS 3DEP 10m National Map Seamless (1/3 Arc-Second) image ee.Image('USGS/3DEP/10m') United States Geological Survey 1998-08-16 2020-05-06 -171, -16.6, 164, 76.9 False 3dep, dem, elevation, geophysical, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m.json https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m proprietary USGS/3DEP/10m_metadata USGS 3DEP National Map Spatial Metadata 1/3 Arc-Second (10m) table ee.FeatureCollection('USGS/3DEP/10m_metadata') United States Geological Survey 1998-08-16 2020-05-06 -171, -16.6, 164, 76.9 False 3dep, usgs https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m_metadata.json https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m_metadata proprietary -USGS/3DEP/1m USGS 3DEP 1m National Map image_collection ee.ImageCollection('USGS/3DEP/1m') United States Geological Survey 1998-08-16 2006-01-01 -171, -16.6, 164, 76.9 False 3dep, dem, elevation, geophysical, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_1m.json https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_1m proprietary +USGS/3DEP/1m USGS 3DEP 1m National Map image_collection ee.ImageCollection('USGS/3DEP/1m') United States Geological Survey 2015-01-01 2006-01-01 -171, -16.6, 164, 76.9 False 3dep, dem, elevation, geophysical, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_1m.json https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_1m proprietary USGS/GAP/AK/2001 USGS GAP Alaska 2001 image ee.Image('USGS/GAP/AK/2001') USGS 2001-01-01 2002-01-01 -180, 49.09, 180, 71.84 False gap, landcover, landfire, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GAP_AK_2001.json https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_AK_2001 proprietary USGS/GAP/CONUS/2011 USGS GAP CONUS 2011 image ee.Image('USGS/GAP/CONUS/2011') USGS 2011-01-01 2012-01-01 -127.98, 22.79, -65.27, 52.57 False gap, landcover, landfire, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GAP_CONUS_2011.json https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_CONUS_2011 proprietary USGS/GAP/HI/2001 USGS GAP Hawaii 2001 image ee.Image('USGS/GAP/HI/2001') USGS 2001-01-01 2002-01-01 -160.2665484663998, 18.851697692492277, -154.669588436752, 22.295218441899475 False gap, landcover, landfire, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GAP_HI_2001.json https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_HI_2001 proprietary @@ -809,7 +809,7 @@ 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 2024-08-31 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 2024-09-02 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 @@ -835,6 +835,7 @@ WRI/GFW/FORMA/raw_output_ndvi FORMA Raw Output NDVI image_collection ee.ImageCol WRI/GFW/FORMA/thresholds FORMA Alert Thresholds image ee.Image('WRI/GFW/FORMA/thresholds') World Resources Institute / Global Forest Watch 2012-01-01 2016-01-01 -120, -50, 180, 40 False daily, deforestation, forest, forma, gfw, modis, monitoring, wri https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_GFW_FORMA_thresholds.json https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_thresholds proprietary WRI/GFW/FORMA/vegetation_tstats FORMA Vegetation T-Statistics image_collection ee.ImageCollection('WRI/GFW/FORMA/vegetation_tstats') World Resources Institute / Global Forest Watch 2012-01-01 2019-04-23 -120, -50, 180, 40 False daily, deforestation, forest, forma, gfw, modis, monitoring, wri https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_GFW_FORMA_vegetation_tstats.json https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_vegetation_tstats proprietary WRI/GPPD/power_plants Global Power Plant Database table ee.FeatureCollection('WRI/GPPD/power_plants') World Resources Institute 2018-06-11 2018-06-11 -180, -90, 180, 90 False climate, energy, infrastructure, power, power_plants, wri https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_GPPD_power_plants.json https://developers.google.com/earth-engine/datasets/catalog/WRI_GPPD_power_plants CC-BY-4.0 +WRI/SBTN/naturalLands/v1 SBTN Natural Lands Map v1 image_collection ee.ImageCollection('WRI/SBTN/naturalLands/v1') WRI 2020-01-01 2020-01-01 -180, -60, 180, 75 False wri, landcover https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_SBTN_naturalLands_v1.json https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1 CC-BY-NC-SA-4.0 WWF/HydroATLAS/v1/Basins/level03 WWF HydroATLAS Basins Level 03 table ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level03') WWF 2000-02-22 2000-02-22 -180, -90, 180, 90 False geophysical, hydroatlas, hydrography, hydrology, hydrosheds, srtm, water, watershed, wwf https://storage.googleapis.com/earthengine-stac/catalog/WWF/WWF_HydroATLAS_v1_Basins_level03.json https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroATLAS_v1_Basins_level03 CC-BY-4.0 WWF/HydroATLAS/v1/Basins/level04 WWF HydroATLAS Basins Level 04 table ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level04') WWF 2000-02-22 2000-02-22 -180, -90, 180, 90 False geophysical, hydroatlas, hydrography, hydrology, hydrosheds, srtm, water, watershed, wwf https://storage.googleapis.com/earthengine-stac/catalog/WWF/WWF_HydroATLAS_v1_Basins_level04.json https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroATLAS_v1_Basins_level04 CC-BY-4.0 WWF/HydroATLAS/v1/Basins/level05 WWF HydroATLAS Basins Level 05 table ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level05') WWF 2000-02-22 2000-02-22 -180, -90, 180, 90 False geophysical, hydroatlas, hydrography, hydrology, hydrosheds, srtm, water, watershed, wwf https://storage.googleapis.com/earthengine-stac/catalog/WWF/WWF_HydroATLAS_v1_Basins_level05.json https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroATLAS_v1_Basins_level05 CC-BY-4.0 diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json index eda99dd..8dadd10 100644 --- a/nasa_cmr_catalog.json +++ b/nasa_cmr_catalog.json @@ -25,6 +25,58 @@ "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 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.", "license": "not-provided" }, + { + "id": "12-hourly_interpolated_surface_position_from_buoys", + "title": "12-Hourly Interpolated Surface Position from Buoys", + "catalog": "SCIOPS", + "state_date": "1979-01-01", + "end_date": "2009-12-01", + "bbox": "-180, 60, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/12-hourly_interpolated_surface_position_from_buoys", + "description": "This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z.", + "license": "not-provided" + }, + { + "id": "12-hourly_interpolated_surface_velocity_from_buoys", + "title": "12-Hourly Interpolated Surface Velocity from Buoys", + "catalog": "SCIOPS", + "state_date": "1979-01-01", + "end_date": "2009-12-02", + "bbox": "-180, 74, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/12-hourly_interpolated_surface_velocity_from_buoys", + "description": "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.", + "license": "not-provided" + }, + { + "id": "12_hourly_interpolated_surface_air_pressure_from_buoys", + "title": "12 Hourly Interpolated Surface Air Pressure from Buoys", + "catalog": "SCIOPS", + "state_date": "1979-01-01", + "end_date": "2007-11-30", + "bbox": "-180, 70, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214600618-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214600618-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/12_hourly_interpolated_surface_air_pressure_from_buoys", + "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": "not-provided" + }, + { + "id": "14c_of_soil_co2_from_ipy_itex_cross_site_comparison", + "title": "14C of soil CO2 from IPY ITEX Cross Site Comparison", + "catalog": "SCIOPS", + "state_date": "2008-01-16", + "end_date": "2008-01-21", + "bbox": "-157.4, -36.9, 147.29, 71.3", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214602443-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214602443-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/14c_of_soil_co2_from_ipy_itex_cross_site_comparison", + "description": "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.", + "license": "not-provided" + }, { "id": "200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES.v1", "title": "2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG", @@ -207,6 +259,19 @@ "description": "INSAT-3D Imager Level-2B Outgoing Longwave Radation (OLR) in HDF-5 Format", "license": "not-provided" }, + { + "id": "3d_snow_models.v4.0", + "title": "3D_Snow_Models", + "catalog": "ENVIDAT", + "state_date": "2022-01-01", + "end_date": "2022-01-01", + "bbox": "9.8471832, 46.8146287, 9.8471832, 46.8146287", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/3d_snow_models.v4.0", + "description": "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).", + "license": "not-provided" + }, { "id": "3fe263d2-99ed-4751-b937-d26a31ab0606", "title": "AVHRR - Vegetation Index (NDVI) - Europe", @@ -584,6 +649,32 @@ "description": "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The \"ACOS\" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the \"ACOS\" Level 2 production process.", "license": "not-provided" }, + { + "id": "ACR3L2DM.v1", + "title": "ACRIM III Level 2 Daily Mean Data V001", + "catalog": "LARC", + "state_date": "2000-04-05", + "end_date": "2013-11-09", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179031504-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179031504-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/ACR3L2DM.v1", + "description": "ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day.", + "license": "not-provided" + }, + { + "id": "ACR3L2SC.v1", + "title": "ACRIM III Level 2 Shutter Cycle Data V001", + "catalog": "LARC", + "state_date": "2000-04-05", + "end_date": "2013-11-09", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C61787524-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C61787524-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/ACR3L2SC.v1", + "description": "ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite.", + "license": "not-provided" + }, { "id": "ADAM.Surface.Reflectance.Database", "title": "ADAM Surface Reflectance Database v4.0", @@ -1663,6 +1754,19 @@ "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": "not-provided" }, + { + "id": "ATSMIGEO.v002", + "title": "MISR Geometric Parameters subset for the ARCTAS region V002", + "catalog": "LARC", + "state_date": "2008-04-02", + "end_date": "2008-07-24", + "bbox": "-157, 54, -110, 71", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000541-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000541-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/ATSMIGEO.v002", + "description": "This file contains the Geometric Parameters subset for the ARCTAS region which measures the sun and view angles at the reference ellipsoid", + "license": "not-provided" + }, { "id": "AU_DySno_NRT_R02.v2", "title": "NRT AMSR2 Unified L3 Global Daily 25 km EASE-Grid Snow Water Equivalent V2", @@ -2170,6 +2274,19 @@ "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes: Background: Reference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities. Requirement: Initiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner. Pseudo-Invariant Calibration Sites (PICS): Algeria 3 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.", "license": "not-provided" }, + { + "id": "CH-OG-1-GPS-10S.v0.0", + "title": "10 sec GPS ground tracking data", + "catalog": "SCIOPS", + "state_date": "2001-05-28", + "end_date": "", + "bbox": "-63.51, -45.69, 170.42, 78.87", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214586614-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214586614-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/CH-OG-1-GPS-10S.v0.0", + "description": "This data set comprises GPS ground data of a sample rate of 10 sec, generated by decoding and sampling GPS high rate ground data. This raw data passed no quality control. The data are given in the Rinex 2.1 format.", + "license": "not-provided" + }, { "id": "CIESIN_SEDAC_EPI_2008.v2008.00", "title": "2008 Environmental Performance Index (EPI)", @@ -2846,6 +2963,19 @@ "description": "Studies of change and variations on decadal timescales are essential for planning satellite missions that seek to improve our understanding of linkages among various components of the Earth System. Decadal predictions using a version of the GEOS-5 AOGCM were contributed to the CMIP5 project. The dataset include a three-member ensemble initialized on December 1 of each year from 1960 to 2010. These data are available, with the designation NASA GMAO, from the CMIP5 Archive at NASA NCCS.", "license": "not-provided" }, + { + "id": "GOMIGEO.v002", + "title": "MISR Geometric Parameters subset for the GoMACCS region V002", + "catalog": "LARC", + "state_date": "2006-07-30", + "end_date": "2006-10-17", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1625796320-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1625796320-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/GOMIGEO.v002", + "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Geometric Parameters subset for the GoMACCS region V002 contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid.", + "license": "not-provided" + }, { "id": "Global_Litter_Carbon_Nutrients_1244.v1", "title": "A Global Database of Litterfall Mass and Litter Pool Carbon and Nutrients", @@ -2950,6 +3080,19 @@ "description": "The data received from IMS1, HySI which operates in 64 spectral bands in VNIR bands(400-900nm) with 500 meter spatial resolution and swath of 128 kms.", "license": "not-provided" }, + { + "id": "ISERV.v1", + "title": "International Space Station SERVIR Environmental Research and Visualization System V1", + "catalog": "USGS_EROS", + "state_date": "2013-03-27", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1379906336-USGS_EROS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1379906336-USGS_EROS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/USGS_EROS/collections/ISERV.v1", + "description": "Abstract: The ISS SERVIR Environmental Research and Visualization System (ISERV) acquired images of the Earth's surface from the International Space Station (ISS). The goal was to improve automatic image capturing and data transfer. ISERV's main component was the optical assembly which consisted of a 9.25 inch Schmidt-Cassegrain telescope, a focal reducer (field of view enlarger), a digital single lens reflex camera, and a high precision focusing mechanism. A motorized 2-axis pointing mount allowed pointing at targets approximately 23 degrees from nadir in both along- and across-track directions.", + "license": "not-provided" + }, { "id": "KOPRI-KPDC-00000008.v1", "title": "1998 Seismic Data, Antarctica", @@ -3223,6 +3366,84 @@ "description": "Near Real-Time (NRT) MODIS Thermal Anomalies / Fire locations processed by FIRMS (Fire Information for Resource Management System) - Land Atmosphere Near real time Capability for EOS (LANCE), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified.MCD14DL are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.", "license": "not-provided" }, + { + "id": "MIANACP.v1", + "title": "MISR Aerosol Climatology Product V001", + "catalog": "LARC", + "state_date": "1999-11-22", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIANACP.v1", + "description": "MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based; 2) mixtures of pure aerosol to be compared with MISR observations; and 3) likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components, in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature, and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium and large; dirty and clean; spherical and nonspherical [Kahn et al. , 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", + "license": "not-provided" + }, + { + "id": "MIANCAGP.v1", + "title": "MISR Ancillary Geographic Product V001", + "catalog": "LARC", + "state_date": "1999-11-07", + "end_date": "2005-06-30", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIANCAGP.v1", + "description": "MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms which generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", + "license": "not-provided" + }, + { + "id": "MIANCARP.v2", + "title": "MISR Ancillary Radiometric Product V002", + "catalog": "LARC", + "state_date": "1999-12-28", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179031521-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179031521-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIANCARP.v2", + "description": "MIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.", + "license": "not-provided" + }, + { + "id": "MIRCCMF.v001", + "title": "MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V001", + "catalog": "LARC", + "state_date": "2000-12-13", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C135857530-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C135857530-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIRCCMF.v001", + "description": "Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V001 contains the FIRSTLOOK Radiometric camera-by-camera Cloud Mask (RCCM) dataset produced using ancillary inputs Radiometric Camera-by-camera Cloud mask Threshold (RCCT) from the previous time period. It is used to determine whether a scene is clear, cloudy or dusty (over ocean).", + "license": "not-provided" + }, + { + "id": "MIRCCMF.v002", + "title": "MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V002", + "catalog": "LARC", + "state_date": "2000-02-24", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2788936281-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2788936281-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIRCCMF.v002", + "description": "This is the FIRSTLOOK Radiometric Camera-by-camera Cloud Mask (RCCM) product. It contains initial estimated classifications of pixels/regions as clear or cloudy. It also has masks for the presence of glitter or dust. The FIRSTLOOK RCCM product is superceded by the final RCCM product following seasonal calibration.", + "license": "not-provided" + }, + { + "id": "MISBR.v005", + "title": "MISR Browse data V005", + "catalog": "LARC", + "state_date": "1999-12-18", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C43677744-LARC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C43677744-LARC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MISBR.v005", + "description": "This is the browse data associated with a particular granule.", + "license": "not-provided" + }, { "id": "MURI_Camouflage.v0", "title": "A Multi University Research Initiative (MURI) Camouflage Project", @@ -3353,6 +3574,32 @@ "description": "MODIS/Aqua Near Real Time (NRT) 5-minute GBAD data in L0 format.", "license": "not-provided" }, + { + "id": "NBII_SAIN2", + "title": "1986-1988 Plot-Transect Installation - Roan Mountain Massif Content Management", + "catalog": "SCIOPS", + "state_date": "1987-01-01", + "end_date": "1988-01-01", + "bbox": "-82.13472, 36.08544, -82.01191, 36.15365", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214586476-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214586476-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/NBII_SAIN2", + "description": "This data set contains information on a set of transects and plots that were originally installed in 1987 and 1988 on the grassy balds of the Roan Mountain Massif (Round Bald, Engine Gap, Jane Bald, Grassy Ridge, Big Yellow Mountain (also known as Yellow Mountain), Little Hump Mountain, Bradley Gap, and Hump Mountain (also known as Big Hump Mountain). Data collected from the transects and plots were to characterize baseline conditions against which the effects of future vegetation management actions could be evaluated. This legacy dataset contains information on the baseline (pre-management) conditions of the grassy balds based on the field collections and analysis of the data collected at transects and plots installed in 1987 and 1988. More specifically, this legacy dataset contains information on the first vegetation composition analysis and first comprehensive plant inventory conducted on the Roan Mountain grassy bald complex. Information that describes this dataset primarily comes from the following sources: various field reports, memos, letters, grant proposals, hardcopies of the 1987 and 1988 data sheets, photos of the original transects and plots, and interviews with the originators of the transect and plot data. This metadata record documents legacy data to the extent practical, as required by Executive Order 12906, \"Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure\", dated April 11, 1994. Details may be missing, but given the resources available, the information provided herein is as concise as possible at this point in time.", + "license": "not-provided" + }, + { + "id": "NBII_SAIN5", + "title": "1987- 1992 Plot-Transect - Community and Mowing Analysis - Roan Mountain Massif Data", + "catalog": "SCIOPS", + "state_date": "1987-01-01", + "end_date": "1992-01-01", + "bbox": "-82.13472, 36.08544, -82.01191, 36.15365", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214586477-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214586477-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/NBII_SAIN5", + "description": "The transects and plots were originally installed in 1987 and 1988 on the grassy balds of the Roan Mountain Massif [Round Bald, Engine Gap, Jane Bald, Grassy Ridge, Big Yellow Mountain (also known as Yellow Mountain), Little Hump Mountain, Bradley Gap, and Hump Mountain (also known as Big Hump Mountain)]. Data collected from the transects were to characterize baseline conditions against which the effects of future vegetation management actions could be evaluated. This legacy data set represents (1) an analysis of data collected from transects and plots that were originally installed in 1987 and 1988 and revisited in 1992, and (2) information on the entry of the 1987, 1988, 1992, 1993, and 1994 data electronically in 1994. Analyses were conducted to document the pre-management conditions of the vegetation on the grassy balds complex of Roan Mountain, and the changes in vegetation on Round Bald and Jane Bald in response to the hand-mowing between 1987-1988 and 1992. This was the second time that a vegetation composition analysis was conducted using the 1987 and 1988 baseline data. Information pertaining to this dataset primarily comes from one report that describes the analyses, electronic files and hardcopies of the raw data, and interviews with the originators of the transect and plot data. This metadata record documents geospatial legacy data to the extent practicable, as required by Executive Order 12906, \"Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure,\" dated April 11, 1994. Details may be missing, but given the resources available, the information provided herein is as concise as possible at this point in time.", + "license": "not-provided" + }, { "id": "NEX-DCP30.v1", "title": "Downscaled 30 Arc-Second CMIP5 Climate Projections for Studies of Climate Change Impacts in the United States", @@ -3379,6 +3626,19 @@ "description": "The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). ", "license": "not-provided" }, + { + "id": "NIPR_UAP_ELF_SYO", + "title": "1-100Hz ULF/ELF Electromagnetic Wave Observation at Syowa Station", + "catalog": "SCIOPS", + "state_date": "2000-01-01", + "end_date": "", + "bbox": "39.6, -69, 39.6, -69", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214590112-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214590112-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/NIPR_UAP_ELF_SYO", + "description": "1-100Hz ULF/ELF Electromagnetic Wave Observation at Syowa Station", + "license": "not-provided" + }, { "id": "NMMIEAI-L2-NRT.v2", "title": "OMPS-NPP L2 NM Aerosol Index swath orbital NRT", @@ -3561,6 +3821,32 @@ "description": "This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach.", "license": "not-provided" }, + { + "id": "NSF-ANT10-43485.v1", + "title": "A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea", + "catalog": "AMD_USAPDC", + "state_date": "2011-07-01", + "end_date": "2015-06-30", + "bbox": "-160, -78, -150, -68", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_USAPDC/collections/NSF-ANT10-43485.v1", + "description": "This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. ", + "license": "not-provided" + }, + { + "id": "NSF-ANT10-43517", + "title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea", + "catalog": "AMD_USAPDC", + "state_date": "2011-07-01", + "end_date": "2015-06-30", + "bbox": "163.5, -78.32, 165.35, -77.57", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_USAPDC/collections/NSF-ANT10-43517", + "description": "This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation.", + "license": "not-provided" + }, { "id": "NSF-ANT10-43621", "title": "A Comparison of Conjugate Auroral Electojet Indices", @@ -3574,6 +3860,19 @@ "description": "The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. ", "license": "not-provided" }, + { + "id": "NSF-ANT13-55533.v1", + "title": "A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization", + "catalog": "AMD_USAPDC", + "state_date": "2013-10-01", + "end_date": "2015-09-30", + "bbox": "163, -78.5, 167, -78", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_USAPDC/collections/NSF-ANT13-55533.v1", + "description": "Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis.", + "license": "not-provided" + }, { "id": "NSIDC-0212.v1", "title": "Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1", @@ -4055,6 +4354,32 @@ "description": "This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project.", "license": "not-provided" }, + { + "id": "USAP-1744755.v1", + "title": "A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean", + "catalog": "AMD_USAPDC", + "state_date": "2018-05-01", + "end_date": "2022-04-30", + "bbox": "-80, -70, -30, -45", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_USAPDC/collections/USAP-1744755.v1", + "description": "Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. ", + "license": "not-provided" + }, + { + "id": "USAP-1744989.v1", + "title": "A Multi-scale Approach to Understanding Spatial and Population Variability in Emperor Penguins", + "catalog": "AMD_USAPDC", + "state_date": "2018-07-15", + "end_date": "2022-06-30", + "bbox": "-180, -90, 180, -60", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_USAPDC/collections/USAP-1744989.v1", + "description": "This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public.", + "license": "not-provided" + }, { "id": "USAP-2130663.v1", "title": "2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science", @@ -4351,7 +4676,20 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.html", "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/above-and-below-ground-herbivore-communities-along-elevation.v1.0", - "description": "Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. ", + "description": "Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.", + "license": "not-provided" + }, + { + "id": "accessibility-of-the-swiss-forest-for-economic-wood-extraction.v1.0", + "title": "Accessibility of the Swiss forest for economic wood extraction (2021)", + "catalog": "ENVIDAT", + "state_date": "2023-01-01", + "end_date": "2023-01-01", + "bbox": "5.95587, 45.81802, 10.49203, 47.80838", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/accessibility-of-the-swiss-forest-for-economic-wood-extraction.v1.0", + "description": "Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport. A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the \"helicopter\" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based. Road transport was investigated using network analysis, based on the data set \"Forest access roads 2013\" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account. Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the \"helicopter\" category or transport with trucks under 26 tons). The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php) Input data used: - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)", "license": "not-provided" }, { @@ -4442,7 +4780,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.html", "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/aerosol-data-davos-wolfgang.v1.0", - "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. ", + "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.", "license": "not-provided" }, { @@ -4455,33 +4793,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.html", "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/aerosol-data-weissfluhjoch.v1.0", - "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. ", - "license": "not-provided" - }, - { - "id": "alnus-glutinosa-orientus-ishidae-flavescence-doree.v1.0", - "title": "Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the \u201cFlavescence dor\u00e9e\u201d epidemics", - "catalog": "ENVIDAT", - "state_date": "2021-01-01", - "end_date": "2021-01-01", - "bbox": "8.4484863, 45.8115721, 9.4372559, 46.4586735", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/alnus-glutinosa-orientus-ishidae-flavescence-doree.v1.0", - "description": "Flavescence dor\u00e9e (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dor\u00e9e phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees. ", - "license": "not-provided" - }, - { - "id": "alpine3d-simulations-of-future-climate-scenarios-for-graubunden.v1.0", - "title": "Alpine3D simulations of future climate scenarios for Graubunden", - "catalog": "ENVIDAT", - "state_date": "2019-01-01", - "end_date": "2019-01-01", - "bbox": "8.6737061, 46.2216525, 10.6347656, 47.1075228", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/alpine3d-simulations-of-future-climate-scenarios-for-graubunden.v1.0", - "description": "This is the simulation dataset from _\"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland\"_, M. Bavay, T. Gr\u00fcnewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013 A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graub\u00fcnden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models. The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095. Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.", + "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.", "license": "not-provided" }, { @@ -4563,16 +4875,16 @@ "license": "not-provided" }, { - "id": "ch2014.v1", - "title": "Alpine3D simulations of future climate scenarios CH2014", - "catalog": "ENVIDAT", - "state_date": "2014-01-01", - "end_date": "2014-01-01", - "bbox": "8.227, 46.79959, 8.227, 46.79959", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/ch2014.v1", - "description": "# Overview The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change. # Experimental Setup Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graub\u00fcnden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m \u00d7 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999\u20132012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085). # Data Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5\u20139 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400\u2013800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average. ", + "id": "blue_ice_core_DML2004_AS", + "title": "101.1 m long horizontal blue ice core collected from Scharffenbergbotnen, DML, Antarctica, in 2003/2004", + "catalog": "SCIOPS", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-180, -90, 180, -62.83", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214614210-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214614210-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/blue_ice_core_DML2004_AS", + "description": "Horizontal blue ice core collected from the surface of a blue ice area in Scharffenbergbotnen, Heimefrontfjella, DML. Samples were collected in austral summer 2003/2004 and transported to Finland for chemical analyses. The blue ice core is estimated to represent a 1000-year period of climate history 20 - 40 kyr B.P.. The results of the analyses will be available in 2005.", "license": "not-provided" }, { @@ -4588,6 +4900,19 @@ "description": "2013 Chesapeake Bay measurements.", "license": "not-provided" }, + { + "id": "darling_sst_82-93", + "title": "1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center", + "catalog": "SCIOPS", + "state_date": "1982-03-01", + "end_date": "1993-12-31", + "bbox": "-71.31, 42.85, -66.74, 47.67", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections/darling_sst_82-93", + "description": "Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine", + "license": "not-provided" + }, { "id": "eMASL1B.v1", "title": "Enhanced MODIS Airborne Simulator (eMAS) Calibrated, Geolocated Radiances L1B 50m Data", @@ -4637,7 +4962,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.html", "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/envidat-lwf-34.v2019-03-06", - "description": "Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) ", + "description": "Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)", "license": "not-provided" }, { @@ -4858,7 +5183,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.html", "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/latent-reserves-in-the-swiss-nfi.v1.0", - "description": "The files refer to the data used in Portier et al. \"\u2018Latent reserves\u2019: a hidden treasure in National Forest Inventories\" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered \u2018latent reserves\u2019, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Kl\u00f6tzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. ", + "description": "The files refer to the data used in Portier et al. \"\u2018Latent reserves\u2019: a hidden treasure in National Forest Inventories\" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered \u2018latent reserves\u2019, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Kl\u00f6tzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.", "license": "not-provided" }, { @@ -4913,6 +5238,19 @@ "description": "Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents.", "license": "not-provided" }, + { + "id": "slow-snow-compression.v1.0", + "title": "A grain-size driven transition in the deformation mechanism in slow snow compression", + "catalog": "ENVIDAT", + "state_date": "2023-01-01", + "end_date": "2023-01-01", + "bbox": "9.8417222, 46.8095077, 9.8417222, 46.8095077", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/slow-snow-compression.v1.0", + "description": "We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory. Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep. The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.", + "license": "not-provided" + }, { "id": "urn:ogc:def:EOP:VITO:VGT_S10.v1", "title": "10 Days Synthesis of SPOT VEGETATION Images (VGT-S10)", diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index 7758777..ff3c35c 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -1,6 +1,10 @@ id title catalog state_date end_date bbox url description license 0f4324af-fa0a-4aaf-9b97-89a4f3325ce1 DESIS - Hyperspectral Images - Global FEDEO 2018-08-30 -180, -52, 180, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2207458058-FEDEO.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/ not-provided 11c5f6df1abc41968d0b28fe36393c9d ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143004-FEDEO.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. not-provided +12-hourly_interpolated_surface_position_from_buoys 12-Hourly Interpolated Surface Position from Buoys SCIOPS 1979-01-01 2009-12-01 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.json This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z. not-provided +12-hourly_interpolated_surface_velocity_from_buoys 12-Hourly Interpolated Surface Velocity from Buoys SCIOPS 1979-01-01 2009-12-02 -180, 74, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.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. not-provided +12_hourly_interpolated_surface_air_pressure_from_buoys 12 Hourly Interpolated Surface Air Pressure from Buoys SCIOPS 1979-01-01 2007-11-30 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600618-SCIOPS.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 not-provided +14c_of_soil_co2_from_ipy_itex_cross_site_comparison 14C of soil CO2 from IPY ITEX Cross Site Comparison SCIOPS 2008-01-16 2008-01-21 -157.4, -36.9, 147.29, 71.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214602443-SCIOPS.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. not-provided 200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES.v1 2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG AU_AADC 2008-01-01 2008-03-20 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214305618-AU_AADC.json We collected surface seawater samples using trace clean 1L Nalgene bottles on the end of a long bamboo pole. We will analyse these samples for trace elements. Iron is the element of highest interest to our group. We will determine dissolved iron and total dissolvable iron concentrations. Samples collected from 7 sites: Sites 1, 2, 3, 4 were a transect perpendicular to the edge of the iceberg to try and determine if there is a iron concentration gradient relative to the iceberg. Sites 4, 5, 6 were along the edge of the iceberg to determine if there is any spatial variability along the iceberg edge. Site 7 was away from the iceberg to determine what the iron concentration is in the surrounding waters not influenced by the iceberg. not-provided 2019 Mali CropType Training Data.v1 2019 Mali CropType Training Data MLHUB 2020-01-01 2023-01-01 -6.9444015, 12.8185552, -6.5890481, 13.3734391 https://cmr.earthdata.nasa.gov/search/concepts/C2781412344-MLHUB.json This dataset produced by the NASA Harvest team includes crop types labels from ground referencing matched with time-series of Sentinel-2 imagery during the growing season. Ground reference data are collected using an ODK app. Crop types include Maize, Millet, Rice and Sorghum. Labels are vectorized over the Sentinel-2 grid, and provided as raster files. Funding for this dataset is provided by Lutheran World Relief, Bill & Melinda Gates Foundation, and University of Maryland NASA Harvest program. not-provided 39480 1988 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI 1988-11-24 1988-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656753-NOAA_NCEI.json Aerial photographs taken by NOAA's National Geodetic Survey during 1988 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 0.5 - 4.0 km. not-provided @@ -15,6 +19,7 @@ id title catalog state_date end_date bbox url description license 3DIMG_L2B_CMK INSAT-3D Imager Level-2B Cloud Map ISRO 2013-10-01 0.843296, -81.04153, 163.15671, 81.04153 https://cmr.earthdata.nasa.gov/search/concepts/C1214622564-ISRO.json INSAT-3D Imager Level-2B Cloud Map Product in HDF-5 Format not-provided 3DIMG_L2B_HEM INSAT-3D Imager Level-2B Precipitation Using Hydroestimator Technique ISRO 2013-10-01 0.843296, -81.04153, 163.15671, 81.04153 https://cmr.earthdata.nasa.gov/search/concepts/C1214622538-ISRO.json INSAT-3D Imager Level-2B Precipitation using Hydroestimator Technique in HDF-5 Format not-provided 3DIMG_L2B_OLR INSAT-3D Imager Level-2B Outgoing Longwave Radiation ISRO 2013-10-01 0.843296, -81.04153, 163.15671, 81.04153 https://cmr.earthdata.nasa.gov/search/concepts/C1214622556-ISRO.json INSAT-3D Imager Level-2B Outgoing Longwave Radation (OLR) in HDF-5 Format not-provided +3d_snow_models.v4.0 3D_Snow_Models ENVIDAT 2022-01-01 2022-01-01 9.8471832, 46.8146287, 9.8471832, 46.8146287 https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.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). not-provided 3fe263d2-99ed-4751-b937-d26a31ab0606 AVHRR - Vegetation Index (NDVI) - Europe FEDEO 1994-07-01 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458021-FEDEO.json "Every day, three successive NOAA-AVHRR scenes are used to derive a synthesis product in stereographic projection known as the ""Normalized Difference Vegetation Index"" for Europe and North Africa. It is calculated by dividing the difference in technical albedos between measurements in the near infrared and visible red part of the spectrum by the sum of both measurements. This value provides important information about the ""greenness"" and density of vegetation. Weekly and monthly thematic synthesis products are also derived from this daily operational product, at each step becoming successively free of clouds. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/" not-provided 7ae5a791-b667-4838-9733-a44e4cf2d715 Cartosat-1 (IRS-P5) - Panchromatic Images (PAN) - Europe, Stereographic FEDEO 2007-01-05 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458042-FEDEO.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. not-provided 802569b8-fb56-4d78-a2e8-3e4549ff475b AVHRR - Sea Surface Temperature (SST) - Europe FEDEO 1994-08-01 -35, 47.5, 51, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2207458053-FEDEO.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/ not-provided @@ -44,6 +49,8 @@ ACOS_L2S.v7.3 ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V7.3 (A ACOS_L2S.v9r ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V9r (ACOS_L2S) at GES DISC GES_DISC 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633158704-GES_DISC.json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. This data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, ""ACOS"" data are still produced and improved, using approaches applied to the OCO-2 spectra. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process. Even though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. Elimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. An important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag. Lastly, users should continue to carefully observe and weigh information from three important flags: ""sounding_qual_flag"" - quality of input data provided to the retrieval processing ""outcome_flag"" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) " not-provided ACOS_L2_Lite_FP.v7.3 ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V7.3 (ACOS_L2_Lite_FP) at GES DISC GES_DISC 2009-04-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1339230298-GES_DISC.json "The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." not-provided ACOS_L2_Lite_FP.v9r ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC GES_DISC 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1720416694-GES_DISC.json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." not-provided +ACR3L2DM.v1 ACRIM III Level 2 Daily Mean Data V001 LARC 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031504-LARC.json ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day. not-provided +ACR3L2SC.v1 ACRIM III Level 2 Shutter Cycle Data V001 LARC 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C61787524-LARC.json ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite. not-provided ADAM.Surface.Reflectance.Database ADAM Surface Reflectance Database v4.0 ESA 2005-01-01 2005-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336812-ESA.json ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1° spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. For sea-ice: Sea ice pixels (masked in the original MOD09A1 products) have been accounted for by a gap-filling approach relying on the spatial-temporal distribution of sea ice coverage provided by the CryoClim climatology for year 2005. not-provided ADEOS_OCTS_L3BM_GAC_OCC_1day ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Day) JAXA 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128761-JAXA.json "ADEOS OCTS L3BM GAC OCC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." not-provided ADEOS_OCTS_L3BM_GAC_OCC_1month ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Month) JAXA 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129571-JAXA.json "ADEOS OCTS L3BM GAC OCC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." not-provided @@ -127,6 +134,7 @@ ATL09.v006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric La ATL10.v006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_CPRD 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates 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. not-provided ATL12.v006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_CPRD 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.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. not-provided ATL13.v006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_CPRD 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.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). not-provided +ATSMIGEO.v002 MISR Geometric Parameters subset for the ARCTAS region V002 LARC 2008-04-02 2008-07-24 -157, 54, -110, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1000000541-LARC.json This file contains the Geometric Parameters subset for the ARCTAS region which measures the sun and view angles at the reference ellipsoid not-provided AU_DySno_NRT_R02.v2 NRT AMSR2 Unified L3 Global Daily 25 km EASE-Grid Snow Water Equivalent V2 LANCEAMSR2 2021-04-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2052622563-LANCEAMSR2.json The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Global Daily Snow Water Equivalent data set contains snow water equivalent (SWE) data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids). Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/daysnow/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. not-provided AU_Land_NRT_R02.v2 NRT AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture Beta V2 LANCEAMSR2 2018-04-11 -180, -89.24, 180, 89.24 https://cmr.earthdata.nasa.gov/search/concepts/C1514684539-LANCEAMSR2.json The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The GCOM-W1 NRT AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture product is a daily measurement of surface soil moisture produced by two retrieval algorithms using resampled Tb (Level-1R) data provided by JAXA: the Normalized Polarization Difference (NPD) algorithm developed by JPL and the Single Channel Algorithm (SCA) developed by USDA. Ancillary data include time, geolocation, and quality assessment. Data are stored in HDF-EOS5 and netCDF4 formats and are available via HTTPS from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level2/land/. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. The AMSR SIPS produces AMSR2 standard science quality data products and they are available at the NSIDC DAAC. Note: This is the same algorithm that generates the corresponding standard science products in the AMSR SIPS. With this beta release, we are generating NRT products in both HDF-EOS5 and netCDF with CF metadata. Version 2 corrects these issues from the previous release: a boundary condition error that resulted in the failure of a small number of version 1 product files and an error in the number of low resolution scans processed which caused only the first half of each scan to be processed. not-provided AU_Ocean.v1 AMSR-E/AMSR2 Unified L2B Global Swath Ocean Products V001 NSIDC_ECS 2002-06-01 -180, -89.24, 180, 89.24 https://cmr.earthdata.nasa.gov/search/concepts/C2176472016-NSIDC_ECS.json This AMSR Unified global ocean data set reports integrated water vapor and cloud liquid water content in the atmospheric column, plus 10-meter sea surface wind speeds. The data are derived from AMSR-E and AMSR2 brightness temperature observations that have been resampled by the Japan Aerospace Exploration Agency (JAXA) to facilitate an intercalibrated (i.e., “unified”) AMSR-E/AMSR2 data record. Ancillary files, including product history, quality assessment (QA), and file-specific metadata are also available. not-provided @@ -166,6 +174,7 @@ CDDIS_MEASURES_products_water_storage.v1 CDDIS SESES MEaSUREs products total wat CEAMARC_CASO_200708030_BIOGEOCHEMISTRYL_SAMPLES.v1 2007-08 V3 CEAMARC-CASO Samples for germanium and boron group AU_AADC 2007-12-17 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214308503-AU_AADC.json "These data describe the locations, dates, time, etc where biogeochemistry data were collected on the CEAMARC-CASO cruise in the 2007/2008 Antarctic season. See the CEAMARC-CASO events metadata record for further information. Sample codes are not descriptive. CEMARC/CASO column have underway data (no link to group site) as well as the CEAMARC and CASO sampling locations. Events are recorded by number and the associated type of sample taken. CTD - 0.4 um filtered water sample. Box corer - diatom scrape. Beam Trawl AAD - sponge sample. PHY - phytoplankton sample taken from inline surface seawater system. Van Veen grab - sediment scrape. WAT - surface water sample passed through 0.4 um filter. Description column explains the samples in more detail - eg information on what size fraction the phytoplankton were filtered at. Litres column describes the volume of water that was filtered. Depth is in metres. Time is local time. Temperature is degrees C. Storage location was for shipboard use only. The ""other"" column details any extra information that may be useful to the sample for example #2153 refers to a sample id code that the French CEAMARC group was using to code for their samples. Our aim for this voyage was to collect surface phytoplankton and water samples across a transect of the Southern Ocean, and to collect benthic sponge and coral samples in Antarctica, to (i) measure the Ge/Si and Si isotope composition to construct a nutrient profile across the Southern Ocean, and to test and calibrate these parameters as proxies for silica utilisation; and (ii) measure the B isotope composition to test the potential of biogenic silica to be used as a seawater pH proxy. We collected phytoplankton, sponges, diatom sediment scrapes and water samples at strategic locations to ensure that the entire water column was surveyed. The data that were collected were used in collaboration with palaeoenvironmental data from sediment cores and experimental culture experiments on diatoms and sponges to gain a better understanding of historical distributions of Silicon and pH in the Southern Ocean." not-provided CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS.v1 2007-08 V3 CEAMARC-CASO Bathymetry Plots Over Time During Events AU_AADC 2007-12-17 2008-01-26 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214308504-AU_AADC.json A routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl over a slope or was the sea floor relatively flat. Note that the depth range in the plots is autoscaled to the data, so a small range in depths appears as a scatetring of points. As long as you look at the depth scale though interpretation will be ok. The R files need a file of bathymetry data in '200708V3_one_minute.csv' which is a file containing a data export from the underway PostgreSQL ship database and 'events.csv' which is a stripped down version of the events export from the ship board events database export. If you wish to run the code again you may need to change the pathnames in the R script to relevant locations. If you have opened the csv files in excel at any stage and the R script gets an error you may need to format the date/time columns as yyyy-mm-dd hh;mm:ss, save and close the file as csv without opening it again and then run the R script. However, all output files are here for every CEAMARC event. Filenames contain a reference to CEAMARC event id. Files are in eps format and can be viewed using Ghostview which is available as a free download on the internet. not-provided CEOS_CalVal_Test_Sites-Algeria3 CEOS Cal Val Test Site - Algeria 3 - Pseudo-Invariant Calibration Site (PICS) USGS_LTA 1972-08-11 5.22, 29.09, 10.01, 31.36 https://cmr.earthdata.nasa.gov/search/concepts/C1220567099-USGS_LTA.json On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes: Background: Reference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities. Requirement: Initiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner. Pseudo-Invariant Calibration Sites (PICS): Algeria 3 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments. not-provided +CH-OG-1-GPS-10S.v0.0 10 sec GPS ground tracking data SCIOPS 2001-05-28 -63.51, -45.69, 170.42, 78.87 https://cmr.earthdata.nasa.gov/search/concepts/C1214586614-SCIOPS.json This data set comprises GPS ground data of a sample rate of 10 sec, generated by decoding and sampling GPS high rate ground data. This raw data passed no quality control. The data are given in the Rinex 2.1 format. not-provided CIESIN_SEDAC_EPI_2008.v2008.00 2008 Environmental Performance Index (EPI) SEDAC 1994-01-01 2007-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001707-SEDAC.json The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents �at target�). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission. not-provided CIESIN_SEDAC_EPI_2010.v2010.00 2010 Environmental Performance Index (EPI) SEDAC 1994-01-01 2009-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179002147-SEDAC.json The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI�s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). not-provided CIESIN_SEDAC_EPI_2012.v2012.00 2012 Environmental Performance Index and Pilot Trend Environmental Performance Index SEDAC 2000-01-01 2010-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000000-SEDAC.json The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories: environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/. not-provided @@ -218,6 +227,7 @@ GGD622.v1 Active-Layer Depth of a Finnish Palsa Bog, Version 1 NSIDCV0 1993-09-0 GGD632.v1 Active-Layer and Permafrost Temperatures, Soendre Stroemfjord, Greenland, Version 1 NSIDCV0 1967-09-06 1976-02-15 50.8, 67, 50.8, 67 https://cmr.earthdata.nasa.gov/search/concepts/C1386206903-NSIDCV0.json This data set contains active-layer and permafrost temperatures from two stations in Soendre Stroemfjord, Greenland. Snow depth and snow extent were also recorded. Thermometers at Station A (67 deg N, 50.8 deg W, 50 m asl) recorded temperatures once a day from September 1967 to February 1976. Thermometers at Station B (67 deg N, 50.8 deg W, 38 m asl) recorded temperatures once a day from September 1967 to August 1970; however, only bi-weekly averages are given for Station B. Data are in tab-delimited ASCII text format and are available via FTP. not-provided GISS-CMIP5.v1 GISS ModelE2 contributions to the CMIP5 archive NCCS 0850-01-01 2100-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1542315069-NCCS.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. not-provided GMAO-CMIP5.v1 GMAO Decadal Analysis & Prediction for CMIP5 NCCS 1961-01-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1542704969-NCCS.json Studies of change and variations on decadal timescales are essential for planning satellite missions that seek to improve our understanding of linkages among various components of the Earth System. Decadal predictions using a version of the GEOS-5 AOGCM were contributed to the CMIP5 project. The dataset include a three-member ensemble initialized on December 1 of each year from 1960 to 2010. These data are available, with the designation NASA GMAO, from the CMIP5 Archive at NASA NCCS. not-provided +GOMIGEO.v002 MISR Geometric Parameters subset for the GoMACCS region V002 LARC 2006-07-30 2006-10-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1625796320-LARC.json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Geometric Parameters subset for the GoMACCS region V002 contains the Geometric Parameters which measure the sun and view angles at the reference ellipsoid. not-provided Global_Litter_Carbon_Nutrients_1244.v1 A Global Database of Litterfall Mass and Litter Pool Carbon and Nutrients ORNL_CLOUD 1827-01-01 1997-12-31 -156.7, -54.5, 176.2, 72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784385713-ORNL_CLOUD.json Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997. The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2). The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations. not-provided Global_Microbial_Biomass_C_N_P_1264.v1 A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data ORNL_CLOUD 1977-11-16 2012-06-01 -180, -90, 177.9, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2216863966-ORNL_CLOUD.json This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers. These data are from soil samples collected primarily at 0-15 cm depth with some from 0-30 cm. In addition, data were compiled for soil microbial biomass concentrations from soil profile samples to depths of 100 cm. Sampling site latitude and longitude were available for the majority of the samples that enabled assembling additional soil properties, site characteristics, vegetation distributions, biomes, and long-term climate data from several global sources of soil, land-cover, and climate data. These site attributes are included with the microbial biomass data. This data set contains two *.csv files of the soil microbial biomass C, N, P data. The first provides all compiled results emphasizing the full spatial extent of the data, while the second is a subset that provides only data from a series of profile samples emphasizing the vertical distribution of microbial biomass concentrations.There is a companion file, also in .csv format, of the references for the surveyed publications. A reference_number relates the data to the respective publication.The concentrations of soil microbial biomass, in combination with other soil databases, were used to estimate the global storage of soil microbial biomass C and N in 0-30 cm and 0-100 cm soil profiles. These storage estimates were combined with a spatial map of 12 major biomes (boreal forest, temperate coniferous forest, temperate broadleaf forest, tropical and subtropical forests, mixed forest, grassland, shrub, tundra, desert, natural wetland, cropland, and pasture) at 0.05-degree by 0.5-degree spatial resolution. The biome map and six estimates of C and N storage and C:N ration in soil microbial biomass are provided in a single netCDF format file. not-provided Global_Phosphorus_Hedley_Fract_1230.v1 A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation ORNL_CLOUD 1985-01-01 2010-12-31 -117.86, -42.5, 117.6, 63.23 https://cmr.earthdata.nasa.gov/search/concepts/C2216863440-ORNL_CLOUD.json This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites. not-provided @@ -226,6 +236,7 @@ GreenBay.v0 2010 Measurements made in Green Bay, Wisconsin OB_DAAC 2010-09-17 - IKONOS_MSI_L1B.v1 IKONOS Level 1B Multispectral 4-Band Satellite Imagery CSDA 1999-10-14 2015-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497453433-CSDA.json The IKONOS Level 1B Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the IKONOS satellite using the Optical Sensor Assembly instrument across the global land surface from October 1999 to March 2015. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The spatial resolution is 3.2m at nadir and the temporal resolution is approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. not-provided IKONOS_Pan_L1B.v1 IKONOS Level 1B Panchromatic Satellite Imagery CSDA 1999-10-24 2015-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497468825-CSDA.json The IKONOS Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the IKONOS satellite using the Optical Sensor Assembly instrument across the global land surface from October 1999 to March 2015. This data product includes panchromatic imagery with a spatial resolution of 0.82m at nadir and a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. not-provided IMS1_HYSI_GEO.v1.0 IMS-1 HYSI TOA Radiance and Reflectance Product ISRO 2008-06-22 2012-09-10 -6.0364, -78.8236, 152.6286, 78.6815 https://cmr.earthdata.nasa.gov/search/concepts/C1214622602-ISRO.json The data received from IMS1, HySI which operates in 64 spectral bands in VNIR bands(400-900nm) with 500 meter spatial resolution and swath of 128 kms. not-provided +ISERV.v1 International Space Station SERVIR Environmental Research and Visualization System V1 USGS_EROS 2013-03-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1379906336-USGS_EROS.json Abstract: The ISS SERVIR Environmental Research and Visualization System (ISERV) acquired images of the Earth's surface from the International Space Station (ISS). The goal was to improve automatic image capturing and data transfer. ISERV's main component was the optical assembly which consisted of a 9.25 inch Schmidt-Cassegrain telescope, a focal reducer (field of view enlarger), a digital single lens reflex camera, and a high precision focusing mechanism. A motorized 2-axis pointing mount allowed pointing at targets approximately 23 degrees from nadir in both along- and across-track directions. not-provided KOPRI-KPDC-00000008.v1 1998 Seismic Data, Antarctica AMD_KOPRI 1998-12-07 1998-12-11 -66.266667, -64.616667, -64.416667, -62.995 https://cmr.earthdata.nasa.gov/search/concepts/C2244292774-AMD_KOPRI.json "Korean Antarctic survey carried out as part of step 2 project in year 2 of 'the Antarctic Undersea Geological Survey' was conducted in the Ⅱ region around the northwestern continent of the Antarctic Peninsula. This area is northwest of Anvers Island, including areas around the pericontinent from the continental shelf to the continental rise zone. The investigation period for this project took a total of 8 days for moving navigation, the survey of the side lines and drilling investigation. After seismic investigation, a surface drilling investigation was conducted in coring point was decided from the reference seismic section. 10 researcher from ‘Korea Ocean Research and Development Institute’ participated in the field survey. We took on lease Russian icebreaker ""Yuzhmorgeologiya""." not-provided KOPRI-KPDC-00000009.v1 1997 Seismic Data, Antarctica AMD_KOPRI 1997-12-23 1997-12-28 -64.699722, -63.525, -62.157778, -62.041389 https://cmr.earthdata.nasa.gov/search/concepts/C2244293126-AMD_KOPRI.json Korean Antarctic survey carried out as part of step 2 project in year 1 of ‘The Antarctic Undersea Geological Survey’ in 1997 was conducted in a continental shelf in the northwestern part of the Antarctic Peninsula. The research period took a total of 8 days, including 6 days for the seismic survey and 2 days for the drilling investigation. We took on lease Norway R/V 'Polar Duke' and 10 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12 –channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. not-provided KOPRI-KPDC-00000011.v1 1996 Seismic Data, Antarctica AMD_KOPRI 1996-12-17 1996-12-26 -62.766667, -63.583333, -60.233333, -62.733333 https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.json "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker." not-provided @@ -247,6 +258,12 @@ Level_2A_aerosol_cloud_optical_products Aeolus L2A Aerosol/Cloud optical product M1_AVH09C1.v6 METOP-B AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05 Deg CMG LAADS 2013-01-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2187507677-LAADS.json The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The METOP-B AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05 Deg CMG, short-name M1_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The M1_ AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format. not-provided M1_AVH13C1.v6 METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05 Deg. CMG LAADS 2013-01-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2751635237-LAADS.json The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name M1_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (M1_AVH01C1). The M1_AVH13C1 product is available in HDF4 file format. not-provided MCD14DL_C5_NRT.v005 MODIS/Aqua+Terra Thermal Anomalies/Fire locations 1km FIRMS V005 NRT LM_FIRMS 2014-01-28 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1219768065-LM_FIRMS.json Near Real-Time (NRT) MODIS Thermal Anomalies / Fire locations processed by FIRMS (Fire Information for Resource Management System) - Land Atmosphere Near real time Capability for EOS (LANCE), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified.MCD14DL are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes. not-provided +MIANACP.v1 MISR Aerosol Climatology Product V001 LARC 1999-11-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.json MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based; 2) mixtures of pure aerosol to be compared with MISR observations; and 3) likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components, in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature, and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium and large; dirty and clean; spherical and nonspherical [Kahn et al. , 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. not-provided +MIANCAGP.v1 MISR Ancillary Geographic Product V001 LARC 1999-11-07 2005-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.json MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms which generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. not-provided +MIANCARP.v2 MISR Ancillary Radiometric Product V002 LARC 1999-12-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031521-LARC.json MIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. not-provided +MIRCCMF.v001 MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V001 LARC 2000-12-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C135857530-LARC.json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V001 contains the FIRSTLOOK Radiometric camera-by-camera Cloud Mask (RCCM) dataset produced using ancillary inputs Radiometric Camera-by-camera Cloud mask Threshold (RCCT) from the previous time period. It is used to determine whether a scene is clear, cloudy or dusty (over ocean). not-provided +MIRCCMF.v002 MISR FIRSTLOOK radiometric camera-by-camera Cloud Mask V002 LARC 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2788936281-LARC.json This is the FIRSTLOOK Radiometric Camera-by-camera Cloud Mask (RCCM) product. It contains initial estimated classifications of pixels/regions as clear or cloudy. It also has masks for the presence of glitter or dust. The FIRSTLOOK RCCM product is superceded by the final RCCM product following seasonal calibration. not-provided +MISBR.v005 MISR Browse data V005 LARC 1999-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C43677744-LARC.json This is the browse data associated with a particular granule. not-provided MURI_Camouflage.v0 A Multi University Research Initiative (MURI) Camouflage Project OB_DAAC 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. not-provided MURI_HI.v0 A Multi University Research Initiative (MURI) near the Hawaiian Islands OB_DAAC 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. not-provided MYD021KM.v6.1NRT MODIS/Aqua Calibrated Radiances 5-Min L1B Swath 1km - NRT LANCEMODIS 2017-10-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1426616847-LANCEMODIS.json The MODIS Level 1B Near Real Time (NRT) data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of electromagentic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). In addition, the Earth Bi-directional Reflectance Distribution Function (BRDF) may be determined for the solar reflective bands (1-19, 26) through knowledge of the solar irradiance (e.g., determined from MODIS solar diffuser data, and from the target illumination geometry). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime, while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channel locations for MODIS are as follows: Band Center Wavelength (um) Primary Use---- ---------------------- -----------1 0.620 - 0.670 Land/Cloud Boundaries2 0.841 - 0.876 Land/Cloud Boundaries3 0.459 - 0.479 Land/Cloud Properties4 0.545 - 0.565 Land/Cloud Properties5 1.230 - 1.250 Land/Cloud Properties6 1.628 - 1.652 Land/Cloud Properties7 2.105 - 2.155 Land/Cloud Properties8 0.405 - 0.420 Ocean Color/Phytoplankton9 0.438 - 0.448 Ocean Color/Phytoplankton10 0.483 - 0.493 Ocean Color/Phytoplankton11 0.526 - 0.536 Ocean Color/Phytoplankton12 0.546 - 0.556 Ocean Color/Phytoplankton13 0.662 - 0.672 Ocean Color/Phytoplankton14 0.673 - 0.683 Ocean Color/Phytoplankton15 0.743 - 0.753 Ocean Color/Phytoplankton16 0.862 - 0.877 Ocean Color/Phytoplankton17 0.890 - 0.920 Atmospheric Water Vapor18 0.931 - 0.941 Atmospheric Water Vapor19 0.915 - 0.965 Atmospheric Water Vapor20 3.660 - 3.840 Surface/Cloud Temperature21 3.929 - 3.989 Surface/Cloud Temperature22 3.929 - 3.989 Surface/Cloud Temperature23 4.020 - 4.080 Surface/Cloud Temperature24 4.433 - 4.498 Atmospheric Temperature25 4.482 - 4.549 Atmospheric Temperature26 1.360 - 1.390 Cirrus Clouds27 6.535 - 6.895 Water Vapor Profile28 7.175 - 7.475 Water Vapor Profile29 8.400 - 8.700 Water Vapor Profile30 9.580 - 9.880 Ozone Overburden31 10.780 - 11.280 Surface/Cloud Temperature32 11.770 - 12.270 Surface/Cloud Temperature33 13.185 - 13.485 Cloud Top Altitude34 13.485 - 13.785 Cloud Top Altitude35 13.785 - 14.085 Cloud Top Altitude36 14.085 - 14.385 Cloud Top Altitude Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500m resolution, and the rest have 1 km resolution. However, for the MODIS L1B 1 km product, the 250 m and 500 m band radiance data and their associated uncertainties have been aggregated to 1km resolution. Thus the entire channel data set is referenced to the same spatial and geolocation scales. Separate L1B products are available for the 250 m channels (MYD02QKM) and 500 m channels (MYD02HKM) that preserve the original resolution of the data. Spatial resolution for pixels at nadir is 1 km, degrading to 4.8 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 2km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B granule will nominally contain a scene built from 203 scans (or swaths) sampled 1354 times in the cross-track direction, corresponding to approximately 5 minutes worth of data. Since an individual MODIS scan (or swath) will contain 10 along-track spatial elements, the scene will be composed of (1354 x 2030) pixels, resulting in a spatial coverage of (2330 km x 2030 km). Due to the MODIS scan geometry, there will be increasing overlap occurring beyond about 25 degrees scan angle. To summarize, the MODIS L1B 1 km data product consists of: 1. Calibrated radiances and uncertainties for (2) 250 m reflected solar bands aggregated to 1km resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands aggregated to 1 km resolution 3. Calibrated radiances and uncertainties for (13) 1 km reflected solar bands and (16) infrared emissive bands 4. Geolocation subsampled at every 5th pixel across and along track 5. Satellite and solar angles subsampled at the above frequency 6. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization. The MODIS L1B data are stored in the Earth Observing System Hierarchical Data Format (HDF-EOS) which is an extension of HDF as developed by the National Center for Supercomputer Applications (NCSA) at the University of Illinois. A typical file size will be approximately 260 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. The Shortname for this product is MYD021KM not-provided @@ -257,8 +274,11 @@ MYD04_L2.v6.1NRT MODIS/Aqua Aerosol 5-Min L2 Swath 10km - NRT LANCEMODIS 2017-10 MYD09.v6.1NRT MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km NRT LANCEMODIS 2021-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2007652303-LANCEMODIS.json The MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km NRT, short name MYD09, is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), BRDF, thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI). not-provided MYD09CMA.v6.1NRT MODIS/Aqua Aerosol Optical Thickness Daily L3 Global 0.05-Deg CMA NRT LANCEMODIS 2021-02-07 -180, -81, 180, 81 https://cmr.earthdata.nasa.gov/search/concepts/C2007652084-LANCEMODIS.json The MODIS/Aqua Aerosol Optical Thickness Daily L3 Global 0.05-Deg CMA Near Real Time (NRT), short name MYD09CMA, is a daily level 3 global product. It is in linear latitude and longitude (Plate Carre) projection with a 0.05Deg spatial resolution. This product is derived from MYD09IDN, MYD09IDT and MYD09IDS for each orbit by compositing the data on the basis of minimum band 3 (459 - 479 nm band) values (after excluding pixels flagged for clouds and high solar zenith angles). not-provided MYDGB0.v6.1NRT MODIS/Aqua 5-minute GBAD data in L0 format - NRT LANCEMODIS 2017-10-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1427015288-LANCEMODIS.json MODIS/Aqua Near Real Time (NRT) 5-minute GBAD data in L0 format. not-provided +NBII_SAIN2 1986-1988 Plot-Transect Installation - Roan Mountain Massif Content Management SCIOPS 1987-01-01 1988-01-01 -82.13472, 36.08544, -82.01191, 36.15365 https://cmr.earthdata.nasa.gov/search/concepts/C1214586476-SCIOPS.json "This data set contains information on a set of transects and plots that were originally installed in 1987 and 1988 on the grassy balds of the Roan Mountain Massif (Round Bald, Engine Gap, Jane Bald, Grassy Ridge, Big Yellow Mountain (also known as Yellow Mountain), Little Hump Mountain, Bradley Gap, and Hump Mountain (also known as Big Hump Mountain). Data collected from the transects and plots were to characterize baseline conditions against which the effects of future vegetation management actions could be evaluated. This legacy dataset contains information on the baseline (pre-management) conditions of the grassy balds based on the field collections and analysis of the data collected at transects and plots installed in 1987 and 1988. More specifically, this legacy dataset contains information on the first vegetation composition analysis and first comprehensive plant inventory conducted on the Roan Mountain grassy bald complex. Information that describes this dataset primarily comes from the following sources: various field reports, memos, letters, grant proposals, hardcopies of the 1987 and 1988 data sheets, photos of the original transects and plots, and interviews with the originators of the transect and plot data. This metadata record documents legacy data to the extent practical, as required by Executive Order 12906, ""Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure"", dated April 11, 1994. Details may be missing, but given the resources available, the information provided herein is as concise as possible at this point in time." not-provided +NBII_SAIN5 1987- 1992 Plot-Transect - Community and Mowing Analysis - Roan Mountain Massif Data SCIOPS 1987-01-01 1992-01-01 -82.13472, 36.08544, -82.01191, 36.15365 https://cmr.earthdata.nasa.gov/search/concepts/C1214586477-SCIOPS.json "The transects and plots were originally installed in 1987 and 1988 on the grassy balds of the Roan Mountain Massif [Round Bald, Engine Gap, Jane Bald, Grassy Ridge, Big Yellow Mountain (also known as Yellow Mountain), Little Hump Mountain, Bradley Gap, and Hump Mountain (also known as Big Hump Mountain)]. Data collected from the transects were to characterize baseline conditions against which the effects of future vegetation management actions could be evaluated. This legacy data set represents (1) an analysis of data collected from transects and plots that were originally installed in 1987 and 1988 and revisited in 1992, and (2) information on the entry of the 1987, 1988, 1992, 1993, and 1994 data electronically in 1994. Analyses were conducted to document the pre-management conditions of the vegetation on the grassy balds complex of Roan Mountain, and the changes in vegetation on Round Bald and Jane Bald in response to the hand-mowing between 1987-1988 and 1992. This was the second time that a vegetation composition analysis was conducted using the 1987 and 1988 baseline data. Information pertaining to this dataset primarily comes from one report that describes the analyses, electronic files and hardcopies of the raw data, and interviews with the originators of the transect and plot data. This metadata record documents geospatial legacy data to the extent practicable, as required by Executive Order 12906, ""Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure,"" dated April 11, 1994. Details may be missing, but given the resources available, the information provided herein is as concise as possible at this point in time." not-provided NEX-DCP30.v1 Downscaled 30 Arc-Second CMIP5 Climate Projections for Studies of Climate Change Impacts in the United States NCCS 1950-01-01 2099-12-31 -125.0208333, 24.0625, -66.4791667, 49.9375 https://cmr.earthdata.nasa.gov/search/concepts/C1542175061-NCCS.json This NASA dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future climate patterns and climate impacts at the scale of individual neighborhoods and communities. This dataset is intended for use in scientific research only, and use of this dataset for other purposes, such as commercial applications, and engineering or design studies is not recommended without consultation with a qualified expert. Community feedback to improve and validate the dataset for modeling usage is appreciated. Email comments to bridget@climateanalyticsgroup.org. Dataset File Name: NASA Earth Exchange (NEX) Downscaled Climate Projections (NEXDCP30), https://portal.nccs.nasa.gov/portal_home/published/NEX.html not-provided NEX-GDDP.v1 NASA Earth Exchange Global Daily Downscaled Projections NCCS 1950-01-01 2100-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1374483929-NCCS.json The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). not-provided +NIPR_UAP_ELF_SYO 1-100Hz ULF/ELF Electromagnetic Wave Observation at Syowa Station SCIOPS 2000-01-01 39.6, -69, 39.6, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590112-SCIOPS.json 1-100Hz ULF/ELF Electromagnetic Wave Observation at Syowa Station not-provided NMMIEAI-L2-NRT.v2 OMPS-NPP L2 NM Aerosol Index swath orbital NRT OMINRT 2011-11-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1657477341-OMINRT.json The OMPS-NPP L2 NM Aerosol Index swath orbital V2 for Near Real Time. For the standard product see the OMPS_NPP_NMMIEAI_L2 product in CMR .The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. not-provided NMTO3NRT.v2 OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital NRT OMINRT 2011-10-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1439272084-OMINRT.json The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite.The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm.Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The L2 NM Ozone data are written using the Hierarchical Data Format Version 5 or HDF5. not-provided NPBUVO3-L2-NRT.v2 OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital NRT OMINRT 2011-10-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1439296101-OMINRT.json The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite in Near Real Time. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The L2 NP Ozone data are written using the Hierarchical Data Format Version 5 or HDF5. not-provided @@ -273,7 +293,10 @@ NRSCC_GLASS_BBE_AVHRR.v11 NRSCC_GLASS_BBE_AVHRR NRSCC 1982-01-01 2017-12-31 -180 NRSCC_GLASS_BBE_MODIS_0.05D.v11 NRSCC_GLASS_BBE_MODIS_0.05D NRSCC 2000-02-18 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351185-NRSCC.json The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from MODIS. The horizontal resolution is 0.05 Degree. not-provided NRSCC_GLASS_BBE_MODIS_1KM.v11 NRSCC_GLASS_BBE_MODIS_1KM NRSCC 2000-02-18 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351153-NRSCC.json NRSCC_GLASS_BBE_MODIS_1KM not-provided NSF-ANT05-37371 A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica AMD_USAPDC 2007-10-01 2013-09-30 40, -84, 140, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532069799-AMD_USAPDC.json This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach. not-provided +NSF-ANT10-43485.v1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea AMD_USAPDC 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. not-provided +NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. not-provided NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices AMD_USAPDC 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. not-provided +NSF-ANT13-55533.v1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization AMD_USAPDC 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. not-provided NSIDC-0212.v1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 NSIDCV0 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. not-provided OMAERUV.v003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT OMINRT 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.json The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. 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. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV 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 OMAERUV data product is about 6 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 OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ not-provided OMCLDRR.v003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.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/ not-provided @@ -311,6 +334,8 @@ Survey_1988_89_Mawson_npcms.v1 1988/89 Summer season, surveying and mapping prog Turbid9.v0 2004 Measurements made in the Chesapeake Bay OB_DAAC 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360689-OB_DAAC.json Measurements made in the Chesapeake Bay in 2004. not-provided USAP-1543498.v1 A Full Lifecycle Approach to Understanding Adélie Penguin Response to Changing Pack Ice Conditions in the Ross Sea AMD_USAPDC 2016-06-01 165, -78, -150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532074621-AMD_USAPDC.json "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Adélie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and ""NestCheck"" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Adélie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region." not-provided USAP-1643722.v1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core AMD_USAPDC 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. not-provided +USAP-1744755.v1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean AMD_USAPDC 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. not-provided +USAP-1744989.v1 A Multi-scale Approach to Understanding Spatial and Population Variability in Emperor Penguins AMD_USAPDC 2018-07-15 2022-06-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.json This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public. not-provided USAP-2130663.v1 2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science AMD_USAPDC 2021-06-01 2023-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2556670196-AMD_USAPDC.json Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation. This workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. not-provided USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province CEOS_EXTRA 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin not-provided USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province CEOS_EXTRA 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. not-provided @@ -333,29 +358,29 @@ XAERDT_L2_AHI_H09.v1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics.v1.0 A numerical solver for heat and mass transport in snow based on FEniCS ENVIDAT 2022-01-01 2022-01-01 9.8472494, 46.812044, 9.8472494, 46.812044 https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.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 not-provided a6efcb0868664248b9cb212aba44313d ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142742-FEDEO.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. not-provided aamhcpex.v1 AAMH CPEX GHRC_DAAC 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. not-provided -above-and-below-ground-herbivore-communities-along-elevation.v1.0 Above- and below-ground herbivore communities along elevation ENVIDAT 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. not-provided +above-and-below-ground-herbivore-communities-along-elevation.v1.0 Above- and below-ground herbivore communities along elevation ENVIDAT 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. not-provided +accessibility-of-the-swiss-forest-for-economic-wood-extraction.v1.0 Accessibility of the Swiss forest for economic wood extraction (2021) ENVIDAT 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.json "Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport. A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the ""helicopter"" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based. Road transport was investigated using network analysis, based on the data set ""Forest access roads 2013"" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account. Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the ""helicopter"" category or transport with trucks under 26 tons). The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php) Input data used: - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)" not-provided aces1am.v1 ACES Aircraft and Mechanical Data GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). not-provided aces1cont.v1 ACES CONTINUOUS DATA V1 GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. not-provided aces1efm.v1 ACES ELECTRIC FIELD MILL V1 GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. not-provided aces1log.v1 ACES LOG DATA GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. not-provided aces1time.v1 ACES TIMING DATA GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. not-provided aces1trig.v1 ACES TRIGGERED DATA GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. not-provided -aerosol-data-davos-wolfgang.v1.0 Aerosol Data Davos Wolfgang ENVIDAT 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. not-provided -aerosol-data-weissfluhjoch.v1.0 Aerosol Data Weissfluhjoch ENVIDAT 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. not-provided -alnus-glutinosa-orientus-ishidae-flavescence-doree.v1.0 Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the “Flavescence dorée” epidemics ENVIDAT 2021-01-01 2021-01-01 8.4484863, 45.8115721, 9.4372559, 46.4586735 https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.json Flavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dorée phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees. not-provided -alpine3d-simulations-of-future-climate-scenarios-for-graubunden.v1.0 Alpine3D simulations of future climate scenarios for Graubunden ENVIDAT 2019-01-01 2019-01-01 8.6737061, 46.2216525, 10.6347656, 47.1075228 https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.json "This is the simulation dataset from _""Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland""_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013 A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models. The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095. Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation." not-provided +aerosol-data-davos-wolfgang.v1.0 Aerosol Data Davos Wolfgang ENVIDAT 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. not-provided +aerosol-data-weissfluhjoch.v1.0 Aerosol Data Weissfluhjoch ENVIDAT 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. not-provided amprimpacts.v1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS GHRC_DAAC 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. not-provided amsua15sp.v1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. not-provided amsua16sp.v1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 GHRC_DAAC 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. not-provided asas Advanced Solid-state Array Spectroradiometer (ASAS) USGS_LTA 1988-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566261-USGS_LTA.json The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle. The ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.). Data in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994. not-provided aster_global_dem ASTER Global DEM USGS_LTA 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567908-USGS_LTA.json ASTER is capable of collecting in-track stereo using nadir- and aft-looking near infrared cameras. Since 2001, these stereo pairs have been used to produce single-scene (60- x 60-kilomenter (km)) digital elevation models (DEM) having vertical (root-mean-squared-error) accuracies generally between 10- and 25-meters (m). The methodology used by Japan's Sensor Information Laboratory Corporation (SILC) to produce the ASTER GDEM involves automated processing of the entire ASTER Level-1A archive. Stereo-correlation is used to produce over one million individual scene-based ASTER DEMs, to which cloud masking is applied to remove cloudy pixels. All cloud-screened DEMS are stacked and residual bad values and outliers are removed. Selected data are averaged to create final pixel values, and residual anomalies are corrected before partitioning the data into 1 degree (°) x 1° tiles. The ASTER GDEM covers land surfaces between 83°N and 83°S and is comprised of 22,702 tiles. Tiles that contain at least 0.01% land area are included. The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude). The data are posted on a 1 arc-second (approximately 30–m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid. not-provided b673f41b-d934-49e4-af6b-44bbdf164367 AVHRR - Land Surface Temperature (LST) - Europe, Daytime FEDEO 1998-02-23 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458008-FEDEO.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/" not-provided -ch2014.v1 Alpine3D simulations of future climate scenarios CH2014 ENVIDAT 2014-01-01 2014-01-01 8.227, 46.79959, 8.227, 46.79959 https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.json # Overview The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change. # Experimental Setup Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085). # Data Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average. not-provided +blue_ice_core_DML2004_AS 101.1 m long horizontal blue ice core collected from Scharffenbergbotnen, DML, Antarctica, in 2003/2004 SCIOPS 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214614210-SCIOPS.json Horizontal blue ice core collected from the surface of a blue ice area in Scharffenbergbotnen, Heimefrontfjella, DML. Samples were collected in austral summer 2003/2004 and transported to Finland for chemical analyses. The blue ice core is estimated to represent a 1000-year period of climate history 20 - 40 kyr B.P.. The results of the analyses will be available in 2005. not-provided chesapeake_val_2013.v0 2013 Chesapeake Bay measurements OB_DAAC 2013-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360188-OB_DAAC.json 2013 Chesapeake Bay measurements. not-provided +darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center SCIOPS 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine not-provided eMASL1B.v1 Enhanced MODIS Airborne Simulator (eMAS) Calibrated, Geolocated Radiances L1B 50m Data LAADS 2013-08-01 2019-08-22 -180, -35, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2801308027-LAADS.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/ not-provided eMASL2CLD.v1 Enhanced MODIS Airborne Simulator (eMAS) L2 Cloud Data LAADS 2013-08-01 2016-09-28 -180, -35, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2801723593-LAADS.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/ not-provided ef6a9266-a210-4431-a4af-06cec4274726 Cartosat-1 (IRS-P5) - Panchromatic Images (PAN) - Europe, Monographic FEDEO 2015-02-10 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207457985-FEDEO.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. not-provided -envidat-lwf-34.v2019-03-06 10-HS Pfynwald ENVIDAT 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.json Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) not-provided +envidat-lwf-34.v2019-03-06 10-HS Pfynwald ENVIDAT 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.json Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) not-provided fife_hydrology_strm_15m_1.v1 15 Minute Stream Flow Data: USGS (FIFE) ORNL_CLOUD 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie not-provided fife_sur_met_rain_30m_2.v1 30 Minute Rainfall Data (FIFE) ORNL_CLOUD 1987-05-29 1987-10-26 -96.6, 39.08, -96.55, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2977893818-ORNL_CLOUD.json 30 minute rainfall data for the Konza Prairie not-provided gov.noaa.nodc:0000029 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) NOAA_NCEI 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.json Not provided not-provided @@ -372,9 +397,10 @@ gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK.v2.0 Black Sea Ultra High Resoluti gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI.v4.0 GHRSST L2P Gridded Global Subskin Sea Surface Temperature from the Tropical Rainfall Mapping Mission (TRMM) Microwave Imager (TMI) (GDS version 1) GHRSSTCWIC 1998-01-01 2015-04-06 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2213645156-GHRSSTCWIC.json "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to SSM/I, that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in November 1997. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. In contrast to infrared SST observations, microwave retrievals can be measured through most clouds, and are also insensitive to water vapor and aerosols. Remote Sensing Systems is the producer of these gridded TMI SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project. Although the product designation is ""L2P_GRIDDED"" it is in actuality a Level 3 Collated (L3C) product as defined in the GHRSST Data Processing Specification (GDS) version 2.0. Its ""L2P_GRIDDED"" name derives from a deprecated specification in the early Pilot Project phase of GHRSST (pre 2008) and has remained for file naming continuity. In this dataset, both ascending (daytime) and descending (daytime) gridded orbital passes on packaged into the same daily file." not-provided gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P.v3.0 GHRSST Level 2P 1 m Depth Global Sea Surface Temperature version 3.0 from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite (GDS version 2) GHRSSTCWIC 2013-06-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2213644303-GHRSSTCWIC.json A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth. The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS, AVHRR and SeaWiFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications. not-provided lake_erie_aug_2014.v0 2014 Lake Erie measurements OB_DAAC 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.json 2014 Lake Erie measurements. not-provided -latent-reserves-in-the-swiss-nfi.v1.0 'Latent reserves' within the Swiss NFI ENVIDAT 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. " not-provided +latent-reserves-in-the-swiss-nfi.v1.0 'Latent reserves' within the Swiss NFI ENVIDAT 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." not-provided mbs_wilhelm_msa_hooh.v1 15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS) AU_AADC 1984-01-01 1998-12-31 86.082, -69.13, 86.084, -69.12 https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.json This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757). not-provided mosaic-cbers4-brazil-3m-1 CBERS-4/WFI Image Mosaic of Brazil - 3 Months INPE 2020-04-01 2020-06-30 -76.6054059, -33.7511817, -27.7877802, 6.3052432 https://cmr.earthdata.nasa.gov/search/concepts/C3108204634-INPE.json CBERS-4/WFI image mosaic of Brazil with 64m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 15, 16 and 13 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in April 2020 and ending in June 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 CBERS-4 scenes and was generated based on an existing CBERS-4/WFI image collection. not-provided mosaic-cbers4a-paraiba-3m-1 CBERS-4A/WFI Image Mosaic of Brazil Paraíba State - 3 Months INPE 2020-07-01 2020-09-30 -38.8134896, -8.3976443, -34.7223714, -5.87659 https://cmr.earthdata.nasa.gov/search/concepts/C3108204719-INPE.json CBERS-4A/WFI image mosaic of Brazil Paraíba State with 55m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 16, 15 and 14 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2020 and ending in September 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 50 CBERS-4A scenes and was generated based on an existing CBERS-4A/WFI image collection. not-provided pfynwaldgasexchange.v1.0 2013-2020 gas exchange at Pfynwald ENVIDAT 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. not-provided +slow-snow-compression.v1.0 A grain-size driven transition in the deformation mechanism in slow snow compression ENVIDAT 2023-01-01 2023-01-01 9.8417222, 46.8095077, 9.8417222, 46.8095077 https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.json We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory. Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep. The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans. not-provided urn:ogc:def:EOP:VITO:VGT_S10.v1 10 Days Synthesis of SPOT VEGETATION Images (VGT-S10) FEDEO 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472890-FEDEO.json The VGT-S10 are near-global or continental, 10-daily composite images which are synthesised from the 'best available' observations registered in the course of every 'dekad' by the orbiting earth observation system SPOT-VEGETATION. The products provide data from all spectral bands (SWIR, NIR, RED, BLUE), the NDVI and auxiliary data on image acquisition parameters. The VEGETATION system allows operational and near real-time applications, at global, continental and regional scales, in very broad environmentally and socio-economically critical fields. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3 not-provided