From 82b191eaa515abc3b755eb5660c696242cf989af Mon Sep 17 00:00:00 2001
From: Robyn Marowitz <robyn.marowitz@colorado.edu>
Date: Wed, 30 Oct 2024 17:17:47 -0600
Subject: [PATCH 1/3] Update test to include snowex and exclude iceflow

---
 .github/workflows/test.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index e747699..ec81e30 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -52,7 +52,7 @@ jobs:
           EARTHDATA_PASSWORD: ${{ secrets.EARTHDATA_PASSWORD }}
         shell: bash -l {0}
         run: |
-          find notebooks -type f -name "*.ipynb" -not -path '*\.ipynb_checkpoints/*' -not -path '*endered*' -not -path '*SnowEx*'  | xargs -I F quarto render "F"  --execute-daemon-restart
+          find notebooks -type f -name "*.ipynb" -not -path '*\.ipynb_checkpoints/*' -not -path '*endered*' -not -path '*iceflow*'  | xargs -I F quarto render "F"  --execute-daemon-restart
 
 
   win-osx:

From d2dd9a1e6760f873872d47ec88c8863a5b2e59bf Mon Sep 17 00:00:00 2001
From: Robyn Marowitz <robyn.marowitz@colorado.edu>
Date: Thu, 31 Oct 2024 15:35:57 -0600
Subject: [PATCH 2/3] Import h5py to notebook

---
 .../SMAP/working_with_smap_in_xarray.ipynb    | 2875 +++++++++++++----
 1 file changed, 2159 insertions(+), 716 deletions(-)

diff --git a/notebooks/SMAP/working_with_smap_in_xarray.ipynb b/notebooks/SMAP/working_with_smap_in_xarray.ipynb
index d67b2ec..c7c73f5 100644
--- a/notebooks/SMAP/working_with_smap_in_xarray.ipynb
+++ b/notebooks/SMAP/working_with_smap_in_xarray.ipynb
@@ -65,16 +65,7 @@
    "execution_count": 1,
    "id": "b35a4034-033a-419c-97a1-fb3a4234e799",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/apbarret/mambaforge/envs/smap-tutorial/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path  # for working with paths on the local file system\n",
     "from pprint import pprint  # for nice printing\n",
@@ -86,6 +77,7 @@
     "import rioxarray\n",
     "import xarray as xr\n",
     "import numpy as np\n",
+    "import h5py\n",
     "\n",
     "# For defining CRS and coordinate system\n",
     "from affine import Affine\n",
@@ -122,15 +114,7 @@
    "execution_count": 3,
    "id": "1b04c8b8-fe00-4452-9989-8a32012ed338",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Granules found: 31\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "results = earthaccess.search_data(\n",
     "    short_name = 'SPL3SMP',\n",
@@ -162,24 +146,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "4d7cb4e8-0854-4dfa-b2a0-28fffca94305",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Getting 31 granules, approx download size: 0.93 GB\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "QUEUEING TASKS | : 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [00:00<00:00, 6426.30it/s]\n",
-      "PROCESSING TASKS | : 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [01:28<00:00,  2.85s/it]\n",
-      "COLLECTING RESULTS | : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [00:00<00:00, 174997.88it/s]\n"
+      "'NoneType' object has no attribute 'get': You must call earthaccess.login() before you can download data\n"
      ]
     }
    ],
@@ -200,49 +175,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a474c513-0473-43eb-b2d1-f386c52272b5",
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170323_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170326_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170325_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170304_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170330_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170312_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170306_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170317_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170301_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170322_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170328_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170302_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170327_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170313_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170308_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170329_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170310_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170311_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170303_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170307_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170331_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170318_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170314_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170305_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170319_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170309_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170315_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170321_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170320_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170324_R18290_001.h5'),\n",
-       " PosixPath('/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170316_R18290_001.h5')]"
+       "[PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170327_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170330_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170309_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170323_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170324_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170320_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170319_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170304_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170313_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170317_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170329_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170310_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170307_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170303_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170314_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170315_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170302_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170306_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170311_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170328_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170301_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170316_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170312_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170305_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170318_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170321_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170325_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170322_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170308_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170331_R18290_001.h5'),\n",
+       " PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170326_R18290_001.h5')]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -297,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "759e1df8-eb4f-472d-8175-69e634c30503",
    "metadata": {},
    "outputs": [
@@ -335,6 +308,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -385,7 +359,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -393,7 +367,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -405,6 +380,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -667,47 +646,47 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 99MB\n",
        "Dimensions:                      (phony_dim_0: 406, phony_dim_1: 964,\n",
        "                                  phony_dim_2: 3)\n",
        "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2\n",
        "Data variables: (12/53)\n",
-       "    EASE_column_index            (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    EASE_row_index               (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo                       (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo_dca                   (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo_scah                  (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo_scav                  (phony_dim_0, phony_dim_1) float32 ...\n",
+       "    EASE_column_index            (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    EASE_row_index               (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo                       (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo_dca                   (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo_scah                  (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo_scav                  (phony_dim_0, phony_dim_1) float32 2MB ...\n",
        "    ...                           ...\n",
-       "    tb_v_uncorrected             (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity           (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity_dca       (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity_scah      (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity_scav      (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_water_content     (phony_dim_0, phony_dim_1) float32 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2c39295e-47b7-4b2d-93ea-b432b92889a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c39295e-47b7-4b2d-93ea-b432b92889a6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 406</li><li><span>phony_dim_1</span>: 964</li><li><span>phony_dim_2</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-55ee83ba-96d0-44a8-9d68-80ed615bcc01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55ee83ba-96d0-44a8-9d68-80ed615bcc01' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b60a4412-8db1-4c24-aa45-a1a969322252' class='xr-section-summary-in' type='checkbox'  ><label for='section-b60a4412-8db1-4c24-aa45-a1a969322252' class='xr-section-summary' >Data variables: <span>(53)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>EASE_column_index</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ded7fce6-a386-41d0-a06d-71a239b2f9ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ded7fce6-a386-41d0-a06d-71a239b2f9ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91a614ac-65be-41b7-a86f-fabcff41dcca' class='xr-var-data-in' type='checkbox'><label for='data-91a614ac-65be-41b7-a86f-fabcff41dcca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The column index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>963</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EASE_row_index</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-72a92598-7aa8-47b2-8fc7-be1c2266a40c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72a92598-7aa8-47b2-8fc7-be1c2266a40c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05409730-be89-4f43-b1d7-09d0caab2311' class='xr-var-data-in' type='checkbox'><label for='data-05409730-be89-4f43-b1d7-09d0caab2311' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The row index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>405</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e0bf23a2-5740-4975-babd-d96f45d1ec57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0bf23a2-5740-4975-babd-d96f45d1ec57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed48e286-e7a7-43f2-adad-9f0cbcbc272b' class='xr-var-data-in' type='checkbox'><label for='data-ed48e286-e7a7-43f2-adad-9f0cbcbc272b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b6bfb4d-5a02-45a9-8830-af4ec9be9ec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b6bfb4d-5a02-45a9-8830-af4ec9be9ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-769a7d2d-00ef-4c36-bcab-193e4818d0c4' class='xr-var-data-in' type='checkbox'><label for='data-769a7d2d-00ef-4c36-bcab-193e4818d0c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9746ab46-87fb-4f2b-86d3-d4bfcd4ca930' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9746ab46-87fb-4f2b-86d3-d4bfcd4ca930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d866b9ca-5b6f-41d0-95d7-90e87699d604' class='xr-var-data-in' type='checkbox'><label for='data-d866b9ca-5b6f-41d0-95d7-90e87699d604' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-H within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7dde2d11-dad1-4c83-a3a7-90845b3f9bbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dde2d11-dad1-4c83-a3a7-90845b3f9bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cae8794-ae8d-495e-9365-afadef22a2b2' class='xr-var-data-in' type='checkbox'><label for='data-0cae8794-ae8d-495e-9365-afadef22a2b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-V within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boresight_incidence</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc3029a3-0b24-484c-aa79-29a416f63ee5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc3029a3-0b24-484c-aa79-29a416f63ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-281c75e2-6db9-44a1-a42a-15d7c242fa73' class='xr-var-data-in' type='checkbox'><label for='data-281c75e2-6db9-44a1-a42a-15d7c242fa73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative angle between the antenna boresight vector and the normal to the Earth&amp;apos;s surface for all footprints within the cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bulk_density</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8314721-fb4f-41b4-9213-071db8295df4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8314721-fb4f-41b4-9213-071db8295df4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2355021f-dd47-4bfc-a7a1-821f5a731f9d' class='xr-var-data-in' type='checkbox'><label for='data-2355021f-dd47-4bfc-a7a1-821f5a731f9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated bulk_density within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>2.65</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb975380-4a17-4f0d-91e8-18969393d50b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb975380-4a17-4f0d-91e8-18969393d50b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d4979ff-21e2-46bb-9c81-6024d8737d31' class='xr-var-data-in' type='checkbox'><label for='data-6d4979ff-21e2-46bb-9c81-6024d8737d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freeze_thaw_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5d4f618-25ab-446b-9cdd-e58bf0edfc03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5d4f618-25ab-446b-9cdd-e58bf0edfc03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f28a86e6-0c95-4234-a5e2-43ab7d8b3047' class='xr-var-data-in' type='checkbox'><label for='data-f28a86e6-0c95-4234-a5e2-43ab7d8b3047' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fraction of the 36 km grid cell that is denoted as frozen.  Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_surface_status</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fddb3726-38a7-4c15-9751-b237018ebd75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fddb3726-38a7-4c15-9751-b237018ebd75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-342e7b77-cb86-4660-93d4-6d019c639414' class='xr-var-data-in' type='checkbox'><label for='data-342e7b77-cb86-4660-93d4-6d019c639414' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Indicates if the grid point lies on land (0) or water (1).</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7032d697-6d0a-4f38-8006-ba7de9006459' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7032d697-6d0a-4f38-8006-ba7de9006459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d603b75e-7d82-49dc-9b28-632456979371' class='xr-var-data-in' type='checkbox'><label for='data-d603b75e-7d82-49dc-9b28-632456979371' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b6b8aab6-b7e3-4e05-b4bf-becb1e64b696' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6b8aab6-b7e3-4e05-b4bf-becb1e64b696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34bae8ea-849e-4757-94bc-2b07d3d786a7' class='xr-var-data-in' type='checkbox'><label for='data-34bae8ea-849e-4757-94bc-2b07d3d786a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the grid cell that contains the most common land cover in that area based on the IGBP landcover map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89227cc9-b08d-46a7-8038-7dd00392a3a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89227cc9-b08d-46a7-8038-7dd00392a3a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d31c10e-5bbb-4da9-9538-c2b57afe745e' class='xr-var-data-in' type='checkbox'><label for='data-6d31c10e-5bbb-4da9-9538-c2b57afe745e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_centroid</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2de091e6-ae5e-4b9a-9163-8af62bd7076d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2de091e6-ae5e-4b9a-9163-8af62bd7076d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e80a3ec-3811-405a-89b0-17b3381c1936' class='xr-var-data-in' type='checkbox'><label for='data-5e80a3ec-3811-405a-89b0-17b3381c1936' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the latitude of the center of the  brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8c1c5093-ae7f-4a4c-a100-dbe38ea3380c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c1c5093-ae7f-4a4c-a100-dbe38ea3380c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cf414d6-5fbe-4f21-8061-e9a75ae6e202' class='xr-var-data-in' type='checkbox'><label for='data-1cf414d6-5fbe-4f21-8061-e9a75ae6e202' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_centroid</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-544f1d53-8e81-4024-a6e9-17a324184aba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-544f1d53-8e81-4024-a6e9-17a324184aba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bb69598-9ed7-4418-af0a-47e9937499a6' class='xr-var-data-in' type='checkbox'><label for='data-6bb69598-9ed7-4418-af0a-47e9937499a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the longitude of the center of the brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>179.999</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_water_body_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca61f9a1-0c6a-415b-b41d-08e6e99781ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca61f9a1-0c6a-415b-b41d-08e6e99781ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46ac4ff4-0174-4448-811c-df0a5ad8ea9c' class='xr-var-data-in' type='checkbox'><label for='data-46ac4ff4-0174-4448-811c-df0a5ad8ea9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b36b769-cfc0-416b-aa56-db97b3376de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b36b769-cfc0-416b-aa56-db97b3376de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35d80f73-d22f-404c-a7e3-7f5160e9d77d' class='xr-var-data-in' type='checkbox'><label for='data-35d80f73-d22f-404c-a7e3-7f5160e9d77d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c939eafa-f62b-4a24-b8fb-217ba6c79bb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c939eafa-f62b-4a24-b8fb-217ba6c79bb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f4bf13a-8e9a-47a3-bb4c-00385e757de5' class='xr-var-data-in' type='checkbox'><label for='data-8f4bf13a-8e9a-47a3-bb4c-00385e757de5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4eac9fcc-27c6-487c-8a86-ff45765ec2b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eac9fcc-27c6-487c-8a86-ff45765ec2b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c037bd4b-f5bf-4113-8427-2bf5cc8203d0' class='xr-var-data-in' type='checkbox'><label for='data-c037bd4b-f5bf-4113-8427-2bf5cc8203d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-H retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cd4daac5-7d21-4cab-abf1-4d86aba17eee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd4daac5-7d21-4cab-abf1-4d86aba17eee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85218d7f-2ee1-42a2-b15f-2b73c4372999' class='xr-var-data-in' type='checkbox'><label for='data-85218d7f-2ee1-42a2-b15f-2b73c4372999' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-V retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1bef639f-e78e-4fee-9aa6-4ca8fea6bfab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bef639f-e78e-4fee-9aa6-4ca8fea6bfab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b562ad7-96cc-48af-be6f-fee9a50e1ba1' class='xr-var-data-in' type='checkbox'><label for='data-9b562ad7-96cc-48af-be6f-fee9a50e1ba1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f80c1c6-ab29-4fd4-a18c-712dacf79c7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f80c1c6-ab29-4fd4-a18c-712dacf79c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f2749e2-9e2c-4941-9509-b2f658ccec13' class='xr-var-data-in' type='checkbox'><label for='data-8f2749e2-9e2c-4941-9509-b2f658ccec13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbd10139-57b8-4bb8-987d-ddd96585013a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbd10139-57b8-4bb8-987d-ddd96585013a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03d6b649-659c-4f2e-a6c4-815829f8eb2a' class='xr-var-data-in' type='checkbox'><label for='data-03d6b649-659c-4f2e-a6c4-815829f8eb2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-H within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-211bf6c1-4a83-4639-9d7d-0cfe71085043' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-211bf6c1-4a83-4639-9d7d-0cfe71085043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ca2204d-8645-4266-b95a-ceb49b5fe2af' class='xr-var-data-in' type='checkbox'><label for='data-2ca2204d-8645-4266-b95a-ceb49b5fe2af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-V within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df5a7ad4-ad84-4c85-a24b-f59b29695c3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df5a7ad4-ad84-4c85-a24b-f59b29695c3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9257c9f9-3736-4e33-aa80-aa4532cd080e' class='xr-var-data-in' type='checkbox'><label for='data-9257c9f9-3736-4e33-aa80-aa4532cd080e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34188bc2-86e6-4430-b19a-be3f4df2d378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34188bc2-86e6-4430-b19a-be3f4df2d378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-064c3bf0-81e7-4781-98b8-35db3d322119' class='xr-var-data-in' type='checkbox'><label for='data-064c3bf0-81e7-4781-98b8-35db3d322119' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_error</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f565cc4-d3c2-42ac-b050-2249de64245c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f565cc4-d3c2-42ac-b050-2249de64245c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01828a39-571f-48cc-9620-3e5f2e25b574' class='xr-var-data-in' type='checkbox'><label for='data-01828a39-571f-48cc-9620-3e5f2e25b574' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e4d953f4-962d-461a-b1eb-1699f7510263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4d953f4-962d-461a-b1eb-1699f7510263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41e607ab-451f-4fbd-82c7-fa44e98238a7' class='xr-var-data-in' type='checkbox'><label for='data-41e607ab-451f-4fbd-82c7-fa44e98238a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-H soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-798b1091-7676-4905-ae6e-d1ad1055f618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-798b1091-7676-4905-ae6e-d1ad1055f618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bc1d60a-72ba-440e-9ea6-7ffd69a6d666' class='xr-var-data-in' type='checkbox'><label for='data-5bc1d60a-72ba-440e-9ea6-7ffd69a6d666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-V soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>static_water_body_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2bba1d0-e65b-42f7-b737-b47007d7b74c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2bba1d0-e65b-42f7-b737-b47007d7b74c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c660f263-55fa-456c-8d5a-16cd229eef5f' class='xr-var-data-in' type='checkbox'><label for='data-c660f263-55fa-456c-8d5a-16cd229eef5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-073e1b46-80b8-44c5-a2f2-ff0e78193974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-073e1b46-80b8-44c5-a2f2-ff0e78193974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-859c0e2e-540f-4008-aba9-14a8863ebb84' class='xr-var-data-in' type='checkbox'><label for='data-859c0e2e-540f-4008-aba9-14a8863ebb84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s</dd><dt><span>flag_meanings :</span></dt><dd>36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radiometer_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region</dd><dt><span>long_name :</span></dt><dd>Bit flags that record ambient surface conditions for the grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_temperature</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d33a993-9965-478b-930d-cd98f2f37b70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d33a993-9965-478b-930d-cd98f2f37b70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2536cf2c-b1f9-4867-b27c-c2dc5fa3eb2e' class='xr-var-data-in' type='checkbox'><label for='data-2536cf2c-b1f9-4867-b27c-c2dc5fa3eb2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature at land surface based on GMAO GEOS-5 data.</dd><dt><span>units :</span></dt><dd>Kelvins</dd><dt><span>valid_max :</span></dt><dd>350.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_h</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98392301-7a99-4517-a1c6-30d7ed398497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98392301-7a99-4517-a1c6-30d7ed398497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-829d08af-9cac-4d94-aaf1-034619832e4f' class='xr-var-data-in' type='checkbox'><label for='data-829d08af-9cac-4d94-aaf1-034619832e4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer horizontal polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_v</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e25242fc-aa64-41cd-a5de-e190e42cf8da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e25242fc-aa64-41cd-a5de-e190e42cf8da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50c7444f-1fe9-4738-84a5-1923a6dd0d95' class='xr-var-data-in' type='checkbox'><label for='data-50c7444f-1fe9-4738-84a5-1923a6dd0d95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer vertical polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_3_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e65e2161-a39a-4c80-a484-6adda57c5310' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e65e2161-a39a-4c80-a484-6adda57c5310' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca12c2fb-906a-4679-aef2-1879936460bd' class='xr-var-data-in' type='checkbox'><label for='data-ca12c2fb-906a-4679-aef2-1879936460bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Third stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_4_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e80fbf9-e23a-41bc-91df-e4fbb583bca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e80fbf9-e23a-41bc-91df-e4fbb583bca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f073d9d-68b5-4dd6-b537-f5c5cda29065' class='xr-var-data-in' type='checkbox'><label for='data-5f073d9d-68b5-4dd6-b537-f5c5cda29065' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fourth stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48b80c3c-b836-4aff-84b4-1a3166723c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48b80c3c-b836-4aff-84b4-1a3166723c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7f2995f-3d90-45f3-a7f7-713552bb1cac' class='xr-var-data-in' type='checkbox'><label for='data-d7f2995f-3d90-45f3-a7f7-713552bb1cac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_uncorrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-721f4769-6131-49d9-90c9-853b29ee66d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-721f4769-6131-49d9-90c9-853b29ee66d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f2b9676-ac02-419b-ad8f-18abd03dc8db' class='xr-var-data-in' type='checkbox'><label for='data-3f2b9676-ac02-419b-ad8f-18abd03dc8db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_3</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc7f5fe3-da4b-4675-a8b5-2554b15434d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc7f5fe3-da4b-4675-a8b5-2554b15434d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57519bb1-aa34-4d04-b406-d88154a00911' class='xr-var-data-in' type='checkbox'><label for='data-57519bb1-aa34-4d04-b406-d88154a00911' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>3rd_Stokes_quality 3rd_Stokes_range 3rd_Stokes_RFI_detection 3rd_Stokes_RFI_correction 3rd_Stokes_NEDT 3rd_Stokes_direct_sun_correction 3rd_Stokes_reflected_sun_correction 3rd_Stokes_reflected_moon_correction 3rd_Stokes_direct_galaxy_correction 3rd_Stokes_reflected_galaxy_correction 3rd_Stokes_atmosphere_correction 3rd_Stokes_null_value_bit 3rd_Stokes_RFI_check 3rd_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 3rd Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_4</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd4025ae-dfc8-4497-b52d-5d4ee79c11c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd4025ae-dfc8-4497-b52d-5d4ee79c11c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc95fd1f-e150-4c02-a023-286b49c3633f' class='xr-var-data-in' type='checkbox'><label for='data-dc95fd1f-e150-4c02-a023-286b49c3633f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>4th_Stokes_quality 4th_Stokes_range 4th_Stokes_RFI_detection 4th_Stokes_RFI_correction 4th_Stokes_NEDT 4th_Stokes_direct_sun_correction 4th_Stokes_reflected_sun_correction 4th_Stokes_reflected_moon_correction 4th_Stokes_direct_galaxy_correction 4th_Stokes_reflected_galaxy_correction 4th_Stokes_atmosphere_correction 4th_Stokes_null_value_bit 4th_Stokes_RFI_check 4th_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 4th Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_h</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-395bc0a6-f274-4312-b7b7-f7f0ab0e10be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-395bc0a6-f274-4312-b7b7-f7f0ab0e10be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16675994-f507-42dc-9897-e74d97a78e08' class='xr-var-data-in' type='checkbox'><label for='data-16675994-f507-42dc-9897-e74d97a78e08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Horizontal_polarization_quality Horizontal_polarization_range Horizontal_polarization_RFI_detection Horizontal_polarization_RFI_correction Horizontal_polarization_NEDT Horizontal_polarization_direct_sun_correction Horizontal_polarization_reflected_sun_correction Horizontal_polarization_reflected_moon_correction Horizontal_polarization_direct_galaxy_correction Horizontal_polarization_reflected_galaxy_correction Horizontal_polarization_atmosphere_correction Horizontal_polarization_Faraday_rotation_correction Horizontal_polarization_null_value_bit Horizontal_polarization_water_correction Horizontal_polarization_RFI_check Horizontal_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the horizontal polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_v</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-215247ab-b1ba-48f0-8f67-6a8bea1210f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-215247ab-b1ba-48f0-8f67-6a8bea1210f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db57056f-9049-4768-a3e7-aba585635582' class='xr-var-data-in' type='checkbox'><label for='data-db57056f-9049-4768-a3e7-aba585635582' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Vertical_polarization_quality Vertical_polarization_range Vertical_polarization_RFI_detection Vertical_polarization_RFI_correction Vertical_polarization_NEDT Vertical_polarization_direct_sun_correction Vertical_polarization_reflected_sun_correction Vertical_polarization_reflected_moon_correction Vertical_polarization_direct_galaxy_correction Vertical_polarization_reflected_galaxy_correction Vertical_polarization_atmosphere_correction Vertical_polarization_Faraday_rotation_correction Vertical_polarization_null_value_bit Vertical_polarization_water_correction Vertical_polarization_RFI_check Vertical_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the vertical polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_seconds</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30f1fc11-6f7f-415e-b0b1-08160580ea95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30f1fc11-6f7f-415e-b0b1-08160580ea95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba0b25a-26ae-4801-8a6b-1bd041ba3c3f' class='xr-var-data-in' type='checkbox'><label for='data-6ba0b25a-26ae-4801-8a6b-1bd041ba3c3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in seconds since noon on January 1, 2000 UTC.</dd><dt><span>valid_max :</span></dt><dd>940000000.0</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_utc</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>|S24</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02bd69c5-9a39-4aa1-8548-9e3dd90c9ba8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02bd69c5-9a39-4aa1-8548-9e3dd90c9ba8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ebacea-1cda-44da-ac4b-62dfdfe56afb' class='xr-var-data-in' type='checkbox'><label for='data-90ebacea-1cda-44da-ac4b-62dfdfe56afb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in UTC.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=|S24]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b3db0f2-eba4-4eb4-aba5-1037a422aff3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b3db0f2-eba4-4eb4-aba5-1037a422aff3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3464b2c-8b60-4714-bbb8-e847750b4e31' class='xr-var-data-in' type='checkbox'><label for='data-c3464b2c-8b60-4714-bbb8-e847750b4e31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_uncorrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d1daa63-38b8-40d5-b8bb-6590eb4c1cf1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d1daa63-38b8-40d5-b8bb-6590eb4c1cf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96ddd4e7-2dd5-41f8-b81f-b85b391b777f' class='xr-var-data-in' type='checkbox'><label for='data-96ddd4e7-2dd5-41f8-b81f-b85b391b777f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86e9d5b6-f193-4762-93ed-ebf311566229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86e9d5b6-f193-4762-93ed-ebf311566229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61042627-5455-4486-99e6-b50fbaa23e9e' class='xr-var-data-in' type='checkbox'><label for='data-61042627-5455-4486-99e6-b50fbaa23e9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a08e3829-0cdc-4fb4-a1aa-b7c6ea4637f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a08e3829-0cdc-4fb4-a1aa-b7c6ea4637f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee6fca93-0990-470a-83e8-f9a5e1605629' class='xr-var-data-in' type='checkbox'><label for='data-ee6fca93-0990-470a-83e8-f9a5e1605629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-722b5631-77cc-441d-9abb-e25be3fbb677' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-722b5631-77cc-441d-9abb-e25be3fbb677' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97177dd5-cb19-4c06-a115-a55f4a56c82b' class='xr-var-data-in' type='checkbox'><label for='data-97177dd5-cb19-4c06-a115-a55f4a56c82b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-H retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7ac4ce5-e12e-48e9-b80a-a1a30fd95a1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7ac4ce5-e12e-48e9-b80a-a1a30fd95a1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca4c3e08-a9bd-4433-8e0f-16d3cce0417f' class='xr-var-data-in' type='checkbox'><label for='data-ca4c3e08-a9bd-4433-8e0f-16d3cce0417f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-V retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_water_content</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-83a77614-25cb-481b-9782-6f329bf75f6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83a77614-25cb-481b-9782-6f329bf75f6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab0a38f4-052b-4ce2-819d-073cb5106a39' class='xr-var-data-in' type='checkbox'><label for='data-ab0a38f4-052b-4ce2-819d-073cb5106a39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative measure of water in the vegetation within the 36 km grid cell.</dd><dt><span>units :</span></dt><dd>kg/m**2</dd><dt><span>valid_max :</span></dt><dd>20.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68be2421-b73a-4689-9184-ee41086200bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68be2421-b73a-4689-9184-ee41086200bf' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-4d1aab67-b2e7-4e2e-bfb2-b59421671b0c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d1aab67-b2e7-4e2e-bfb2-b59421671b0c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "    tb_v_uncorrected             (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity           (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity_dca       (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity_scah      (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity_scav      (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_water_content     (phony_dim_0, phony_dim_1) float32 2MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-09987366-1999-410c-ba10-0a1de54f304c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-09987366-1999-410c-ba10-0a1de54f304c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 406</li><li><span>phony_dim_1</span>: 964</li><li><span>phony_dim_2</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-66b7d342-6a23-41a6-bbef-8a35b5053e8a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66b7d342-6a23-41a6-bbef-8a35b5053e8a' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-958a6094-f3ad-445c-af13-19d37117b7e9' class='xr-section-summary-in' type='checkbox'  ><label for='section-958a6094-f3ad-445c-af13-19d37117b7e9' class='xr-section-summary' >Data variables: <span>(53)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>EASE_column_index</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c053fa5a-ce6f-4717-8305-2fcd96987fe5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c053fa5a-ce6f-4717-8305-2fcd96987fe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9613378-994e-4598-a3e5-044ab79261c0' class='xr-var-data-in' type='checkbox'><label for='data-a9613378-994e-4598-a3e5-044ab79261c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The column index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>963</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EASE_row_index</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13dc24ee-348b-4c65-863c-49a1d66fb962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13dc24ee-348b-4c65-863c-49a1d66fb962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fa50f66-82b2-46df-881b-64013b5f6c1e' class='xr-var-data-in' type='checkbox'><label for='data-7fa50f66-82b2-46df-881b-64013b5f6c1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The row index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>405</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55f23036-c77d-43d5-9109-aad1b2dfd122' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55f23036-c77d-43d5-9109-aad1b2dfd122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-623cf86b-bec9-4301-b4e9-ba3e38a43ffe' class='xr-var-data-in' type='checkbox'><label for='data-623cf86b-bec9-4301-b4e9-ba3e38a43ffe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ea78c91-87c4-4ab3-9685-c0e9350525b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ea78c91-87c4-4ab3-9685-c0e9350525b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c1c7207-28df-48b3-8e32-1b0d36789b86' class='xr-var-data-in' type='checkbox'><label for='data-1c1c7207-28df-48b3-8e32-1b0d36789b86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-650b90f0-80f7-4128-8ffb-46d2c094c863' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-650b90f0-80f7-4128-8ffb-46d2c094c863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdb33a57-3b10-4025-ab4a-0a43fcf43552' class='xr-var-data-in' type='checkbox'><label for='data-bdb33a57-3b10-4025-ab4a-0a43fcf43552' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-H within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04ef2f60-4a9a-44dc-8f9f-205d423d931c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04ef2f60-4a9a-44dc-8f9f-205d423d931c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dced8141-4f5c-4f93-a799-ea5b28bc82dc' class='xr-var-data-in' type='checkbox'><label for='data-dced8141-4f5c-4f93-a799-ea5b28bc82dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-V within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boresight_incidence</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcced0c9-39c0-42b1-ab03-1ab2456b62f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcced0c9-39c0-42b1-ab03-1ab2456b62f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df3cdf23-d92f-4f68-83d2-2ffe74912579' class='xr-var-data-in' type='checkbox'><label for='data-df3cdf23-d92f-4f68-83d2-2ffe74912579' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative angle between the antenna boresight vector and the normal to the Earth&amp;apos;s surface for all footprints within the cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bulk_density</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4df2091-ca2f-4512-8c35-7f8936085e86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4df2091-ca2f-4512-8c35-7f8936085e86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ecddf90-5d0c-4aeb-a1f8-7b71f3fb1e24' class='xr-var-data-in' type='checkbox'><label for='data-4ecddf90-5d0c-4aeb-a1f8-7b71f3fb1e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated bulk_density within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>2.65</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-549401b5-3aa7-45b3-9e80-4440baf10aac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-549401b5-3aa7-45b3-9e80-4440baf10aac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22c7d975-a87e-4632-908f-35a6dad3f980' class='xr-var-data-in' type='checkbox'><label for='data-22c7d975-a87e-4632-908f-35a6dad3f980' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freeze_thaw_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c43908cc-c055-4170-9199-e02e0a6ac08d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c43908cc-c055-4170-9199-e02e0a6ac08d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a89c3862-4536-4208-b2d8-721daf98bef4' class='xr-var-data-in' type='checkbox'><label for='data-a89c3862-4536-4208-b2d8-721daf98bef4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fraction of the 36 km grid cell that is denoted as frozen.  Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_surface_status</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ac3b634-1805-40bf-b1cd-452c9ae98cde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ac3b634-1805-40bf-b1cd-452c9ae98cde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-860ab464-4160-485c-8890-c7e84f676632' class='xr-var-data-in' type='checkbox'><label for='data-860ab464-4160-485c-8890-c7e84f676632' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Indicates if the grid point lies on land (0) or water (1).</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d345695a-b3d6-40e7-bf2b-0f27f998e9ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d345695a-b3d6-40e7-bf2b-0f27f998e9ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c816e30-b01b-423b-a4a9-a6491a413d5a' class='xr-var-data-in' type='checkbox'><label for='data-9c816e30-b01b-423b-a4a9-a6491a413d5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a78e969-223b-4128-802f-afb49e0a9584' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a78e969-223b-4128-802f-afb49e0a9584' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f201105-f298-4927-b1b0-200569aa4db7' class='xr-var-data-in' type='checkbox'><label for='data-7f201105-f298-4927-b1b0-200569aa4db7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the grid cell that contains the most common land cover in that area based on the IGBP landcover map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88d5b7dc-ea6f-4bdc-bc4a-cabc568a044f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88d5b7dc-ea6f-4bdc-bc4a-cabc568a044f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1194cff1-723a-4539-8d83-87ede48f98d5' class='xr-var-data-in' type='checkbox'><label for='data-1194cff1-723a-4539-8d83-87ede48f98d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_centroid</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96ba0269-36c6-4ce6-91fd-e27149644fec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ba0269-36c6-4ce6-91fd-e27149644fec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-decff3ae-cfdf-4d74-824c-2d2186a30a49' class='xr-var-data-in' type='checkbox'><label for='data-decff3ae-cfdf-4d74-824c-2d2186a30a49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the latitude of the center of the  brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5dedb489-0b7b-43c5-a9c8-21a01bfcff8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dedb489-0b7b-43c5-a9c8-21a01bfcff8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b58e0fa4-cc91-48ec-8ac8-e7546b45d81c' class='xr-var-data-in' type='checkbox'><label for='data-b58e0fa4-cc91-48ec-8ac8-e7546b45d81c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_centroid</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3bdb75e5-1bd3-4fff-b985-745ecc4d0db9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bdb75e5-1bd3-4fff-b985-745ecc4d0db9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4fb9b4b-a03b-4a5e-8b3b-a55861cf9fa4' class='xr-var-data-in' type='checkbox'><label for='data-b4fb9b4b-a03b-4a5e-8b3b-a55861cf9fa4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the longitude of the center of the brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>179.999</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_water_body_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-826f74e2-4f7f-4941-9a03-b349387f172a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-826f74e2-4f7f-4941-9a03-b349387f172a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-546cb9a3-7b6e-4284-a045-c1b94f0bef8b' class='xr-var-data-in' type='checkbox'><label for='data-546cb9a3-7b6e-4284-a045-c1b94f0bef8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c52ffb27-2d36-4f99-b5d2-d19f3f5a6d59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c52ffb27-2d36-4f99-b5d2-d19f3f5a6d59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0c16a7d-81a7-4c15-ae2b-aecd79c8cb28' class='xr-var-data-in' type='checkbox'><label for='data-c0c16a7d-81a7-4c15-ae2b-aecd79c8cb28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf8db4bb-4688-4beb-a059-6d74b3db5f0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf8db4bb-4688-4beb-a059-6d74b3db5f0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71a62f9a-5315-4205-af53-d7adf5d0e440' class='xr-var-data-in' type='checkbox'><label for='data-71a62f9a-5315-4205-af53-d7adf5d0e440' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e119a26-0e8a-416c-9c57-078198de6ed3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e119a26-0e8a-416c-9c57-078198de6ed3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c953e8c-e108-4288-859e-03b27a8ef400' class='xr-var-data-in' type='checkbox'><label for='data-0c953e8c-e108-4288-859e-03b27a8ef400' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-H retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3eb85702-34b5-4215-845b-7c87b88dcd1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3eb85702-34b5-4215-845b-7c87b88dcd1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fad867c4-8f99-482f-8fce-de7907032031' class='xr-var-data-in' type='checkbox'><label for='data-fad867c4-8f99-482f-8fce-de7907032031' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-V retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-808b2393-887c-4357-b854-3712432af19b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-808b2393-887c-4357-b854-3712432af19b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1b359b2-7c27-40c3-9310-59c52d957362' class='xr-var-data-in' type='checkbox'><label for='data-b1b359b2-7c27-40c3-9310-59c52d957362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68f3870f-98b5-4520-853a-f5785e1b766b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68f3870f-98b5-4520-853a-f5785e1b766b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f834b2bf-5fe6-401a-ae9f-e8974937cf04' class='xr-var-data-in' type='checkbox'><label for='data-f834b2bf-5fe6-401a-ae9f-e8974937cf04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f735a0dc-9541-4704-90ab-0e814bcb9189' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f735a0dc-9541-4704-90ab-0e814bcb9189' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d03dec48-c9cf-4753-ac6e-13905199b0ce' class='xr-var-data-in' type='checkbox'><label for='data-d03dec48-c9cf-4753-ac6e-13905199b0ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-H within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c9e2a27d-61b3-42e0-b6a4-24e9aa1c23d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9e2a27d-61b3-42e0-b6a4-24e9aa1c23d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dd978f6-2b59-4cc2-bd5f-c6c8c8da6869' class='xr-var-data-in' type='checkbox'><label for='data-1dd978f6-2b59-4cc2-bd5f-c6c8c8da6869' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-V within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a8eceaf-5a77-4853-b003-3f02387c9414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8eceaf-5a77-4853-b003-3f02387c9414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab3848fe-f741-44af-b6a6-b010d3c0a4b9' class='xr-var-data-in' type='checkbox'><label for='data-ab3848fe-f741-44af-b6a6-b010d3c0a4b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34f1f8ba-00d9-4e9a-9fa0-8028b269aaf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34f1f8ba-00d9-4e9a-9fa0-8028b269aaf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c98a2245-84fd-4b5e-b49b-709c0e1b5bd2' class='xr-var-data-in' type='checkbox'><label for='data-c98a2245-84fd-4b5e-b49b-709c0e1b5bd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_error</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4183f35-e813-4bf6-be1a-e13f05db350d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4183f35-e813-4bf6-be1a-e13f05db350d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34a18538-263a-45d5-8eca-5e17085376cc' class='xr-var-data-in' type='checkbox'><label for='data-34a18538-263a-45d5-8eca-5e17085376cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43945046-071d-46c3-88f5-8ad99568b9cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43945046-071d-46c3-88f5-8ad99568b9cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a871b0cf-8d35-4511-a23d-ccc126d63494' class='xr-var-data-in' type='checkbox'><label for='data-a871b0cf-8d35-4511-a23d-ccc126d63494' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-H soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e4946193-962d-485b-8ac6-94881c495d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4946193-962d-485b-8ac6-94881c495d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a3bcac5-1067-43f0-a0a0-7833e8ac7db3' class='xr-var-data-in' type='checkbox'><label for='data-1a3bcac5-1067-43f0-a0a0-7833e8ac7db3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-V soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>static_water_body_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7bc1326e-edcf-4be9-8a6d-2e39df5621f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bc1326e-edcf-4be9-8a6d-2e39df5621f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-220ee303-1f25-42a5-9732-1b39d86868cf' class='xr-var-data-in' type='checkbox'><label for='data-220ee303-1f25-42a5-9732-1b39d86868cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b6051d1-2b5c-4005-ad3a-4c7d33884c52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b6051d1-2b5c-4005-ad3a-4c7d33884c52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47148e6b-47b1-4d93-bd8f-84090a57f71d' class='xr-var-data-in' type='checkbox'><label for='data-47148e6b-47b1-4d93-bd8f-84090a57f71d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s</dd><dt><span>flag_meanings :</span></dt><dd>36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radiometer_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region</dd><dt><span>long_name :</span></dt><dd>Bit flags that record ambient surface conditions for the grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_temperature</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95a7b3b1-872d-4e33-b599-66e779158508' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95a7b3b1-872d-4e33-b599-66e779158508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d90c8e3-5486-4116-9e9b-c78efbe66c80' class='xr-var-data-in' type='checkbox'><label for='data-1d90c8e3-5486-4116-9e9b-c78efbe66c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature at land surface based on GMAO GEOS-5 data.</dd><dt><span>units :</span></dt><dd>Kelvins</dd><dt><span>valid_max :</span></dt><dd>350.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_h</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de3c2fad-7774-4d41-b1d2-d395fd96cd0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de3c2fad-7774-4d41-b1d2-d395fd96cd0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3d18546-2a7a-491f-9208-6650772a993a' class='xr-var-data-in' type='checkbox'><label for='data-a3d18546-2a7a-491f-9208-6650772a993a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer horizontal polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_v</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-660ccb66-6202-4fe3-9e8a-5389d4d5daff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-660ccb66-6202-4fe3-9e8a-5389d4d5daff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-917d1b92-7d05-4599-8274-3845cde90bed' class='xr-var-data-in' type='checkbox'><label for='data-917d1b92-7d05-4599-8274-3845cde90bed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer vertical polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_3_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7822837e-ce10-46f4-9c43-6782f8856e11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7822837e-ce10-46f4-9c43-6782f8856e11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e60f0e1-4ac7-4057-aaa4-83000ba12625' class='xr-var-data-in' type='checkbox'><label for='data-9e60f0e1-4ac7-4057-aaa4-83000ba12625' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Third stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_4_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27058abf-6207-42e3-9612-077976f0096f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27058abf-6207-42e3-9612-077976f0096f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a5623dc-bfa0-4e7b-b395-811779c74800' class='xr-var-data-in' type='checkbox'><label for='data-0a5623dc-bfa0-4e7b-b395-811779c74800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fourth stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbdbdd31-4a42-40ec-b432-a412d5883de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbdbdd31-4a42-40ec-b432-a412d5883de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34d27b2a-ab3e-49c0-8544-891303cda66a' class='xr-var-data-in' type='checkbox'><label for='data-34d27b2a-ab3e-49c0-8544-891303cda66a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_uncorrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-694cd669-337e-4a64-ac6b-b88905147668' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-694cd669-337e-4a64-ac6b-b88905147668' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e389d9b3-f503-4fff-bb5c-80599edbbe8c' class='xr-var-data-in' type='checkbox'><label for='data-e389d9b3-f503-4fff-bb5c-80599edbbe8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_3</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4286472d-d274-4689-a49b-8b760020bdc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4286472d-d274-4689-a49b-8b760020bdc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d768ce67-0cc2-4f79-a348-22d4c8c9f36b' class='xr-var-data-in' type='checkbox'><label for='data-d768ce67-0cc2-4f79-a348-22d4c8c9f36b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>3rd_Stokes_quality 3rd_Stokes_range 3rd_Stokes_RFI_detection 3rd_Stokes_RFI_correction 3rd_Stokes_NEDT 3rd_Stokes_direct_sun_correction 3rd_Stokes_reflected_sun_correction 3rd_Stokes_reflected_moon_correction 3rd_Stokes_direct_galaxy_correction 3rd_Stokes_reflected_galaxy_correction 3rd_Stokes_atmosphere_correction 3rd_Stokes_null_value_bit 3rd_Stokes_RFI_check 3rd_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 3rd Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_4</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92623364-604a-4592-bc4a-c5a59bb2c267' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92623364-604a-4592-bc4a-c5a59bb2c267' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8872ed0-a6ba-4266-94fe-1bbe9d48db78' class='xr-var-data-in' type='checkbox'><label for='data-f8872ed0-a6ba-4266-94fe-1bbe9d48db78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>4th_Stokes_quality 4th_Stokes_range 4th_Stokes_RFI_detection 4th_Stokes_RFI_correction 4th_Stokes_NEDT 4th_Stokes_direct_sun_correction 4th_Stokes_reflected_sun_correction 4th_Stokes_reflected_moon_correction 4th_Stokes_direct_galaxy_correction 4th_Stokes_reflected_galaxy_correction 4th_Stokes_atmosphere_correction 4th_Stokes_null_value_bit 4th_Stokes_RFI_check 4th_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 4th Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_h</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49181705-3a75-4c66-955b-b4084e823a95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49181705-3a75-4c66-955b-b4084e823a95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdd17f30-729a-4528-bb7b-12282fd9026e' class='xr-var-data-in' type='checkbox'><label for='data-cdd17f30-729a-4528-bb7b-12282fd9026e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Horizontal_polarization_quality Horizontal_polarization_range Horizontal_polarization_RFI_detection Horizontal_polarization_RFI_correction Horizontal_polarization_NEDT Horizontal_polarization_direct_sun_correction Horizontal_polarization_reflected_sun_correction Horizontal_polarization_reflected_moon_correction Horizontal_polarization_direct_galaxy_correction Horizontal_polarization_reflected_galaxy_correction Horizontal_polarization_atmosphere_correction Horizontal_polarization_Faraday_rotation_correction Horizontal_polarization_null_value_bit Horizontal_polarization_water_correction Horizontal_polarization_RFI_check Horizontal_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the horizontal polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_v</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca11a44b-0a56-4693-bfa9-7ccb833d1a0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca11a44b-0a56-4693-bfa9-7ccb833d1a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c63f65c0-52f6-4c79-91e3-e4c56fda30d7' class='xr-var-data-in' type='checkbox'><label for='data-c63f65c0-52f6-4c79-91e3-e4c56fda30d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Vertical_polarization_quality Vertical_polarization_range Vertical_polarization_RFI_detection Vertical_polarization_RFI_correction Vertical_polarization_NEDT Vertical_polarization_direct_sun_correction Vertical_polarization_reflected_sun_correction Vertical_polarization_reflected_moon_correction Vertical_polarization_direct_galaxy_correction Vertical_polarization_reflected_galaxy_correction Vertical_polarization_atmosphere_correction Vertical_polarization_Faraday_rotation_correction Vertical_polarization_null_value_bit Vertical_polarization_water_correction Vertical_polarization_RFI_check Vertical_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the vertical polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_seconds</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2961b5bb-677e-4295-bbd2-c0a309b4ecf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2961b5bb-677e-4295-bbd2-c0a309b4ecf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ccc061c-00cb-4ce4-8715-87ee184d69e4' class='xr-var-data-in' type='checkbox'><label for='data-3ccc061c-00cb-4ce4-8715-87ee184d69e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in seconds since noon on January 1, 2000 UTC.</dd><dt><span>valid_max :</span></dt><dd>940000000.0</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_utc</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>|S24</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0f4fc263-e05d-4de7-b75c-5fadb7b3ebc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f4fc263-e05d-4de7-b75c-5fadb7b3ebc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa8ea6dc-aad5-444f-9bb7-501b95c669be' class='xr-var-data-in' type='checkbox'><label for='data-fa8ea6dc-aad5-444f-9bb7-501b95c669be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in UTC.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=|S24]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_corrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-740ca638-138e-42b7-a99a-7786f1c59a3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-740ca638-138e-42b7-a99a-7786f1c59a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6227b479-e0a6-4e63-b3b0-5e99009e190a' class='xr-var-data-in' type='checkbox'><label for='data-6227b479-e0a6-4e63-b3b0-5e99009e190a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_uncorrected</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8c2cbcf-96a9-4650-81d2-f68eeb530c58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8c2cbcf-96a9-4650-81d2-f68eeb530c58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90c5eeae-8927-4d0e-ae8c-31aa01d0cd21' class='xr-var-data-in' type='checkbox'><label for='data-90c5eeae-8927-4d0e-ae8c-31aa01d0cd21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c7994cc-b701-43c8-a52a-75e06e1bb8d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c7994cc-b701-43c8-a52a-75e06e1bb8d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9de2f72a-92d2-4ab2-9795-2a0aa57b397e' class='xr-var-data-in' type='checkbox'><label for='data-9de2f72a-92d2-4ab2-9795-2a0aa57b397e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_dca</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cea77ec3-0cf1-4091-85f9-ac640bfcbb35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cea77ec3-0cf1-4091-85f9-ac640bfcbb35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c1c555b-4402-4a90-84a3-ee2cd60fb4a8' class='xr-var-data-in' type='checkbox'><label for='data-6c1c555b-4402-4a90-84a3-ee2cd60fb4a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scah</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9da1e8f-6d99-4133-9265-4875d5e39cc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9da1e8f-6d99-4133-9265-4875d5e39cc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ffe012-7249-4310-883d-de3b95e3ab04' class='xr-var-data-in' type='checkbox'><label for='data-84ffe012-7249-4310-883d-de3b95e3ab04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-H retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scav</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70a3b430-3ff5-4671-9765-0b87884c2b32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70a3b430-3ff5-4671-9765-0b87884c2b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3881a93a-e690-44bd-9fed-c2961c1ec07e' class='xr-var-data-in' type='checkbox'><label for='data-3881a93a-e690-44bd-9fed-c2961c1ec07e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-V retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_water_content</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1d80e5f-72ee-487e-9e5a-293e147662c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1d80e5f-72ee-487e-9e5a-293e147662c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff2f7531-6fd9-4602-8fac-d1e7b0ed58d0' class='xr-var-data-in' type='checkbox'><label for='data-ff2f7531-6fd9-4602-8fac-d1e7b0ed58d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative measure of water in the vegetation within the 36 km grid cell.</dd><dt><span>units :</span></dt><dd>kg/m**2</dd><dt><span>valid_max :</span></dt><dd>20.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-917e32e8-eb2c-47f8-a34a-a291172db020' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-917e32e8-eb2c-47f8-a34a-a291172db020' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8848fb3f-978f-43c7-9ed7-db93a4569938' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8848fb3f-978f-43c7-9ed7-db93a4569938' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 99MB\n",
        "Dimensions:                      (phony_dim_0: 406, phony_dim_1: 964,\n",
        "                                  phony_dim_2: 3)\n",
        "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2\n",
        "Data variables: (12/53)\n",
-       "    EASE_column_index            (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    EASE_row_index               (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo                       (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo_dca                   (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo_scah                  (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    albedo_scav                  (phony_dim_0, phony_dim_1) float32 ...\n",
+       "    EASE_column_index            (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    EASE_row_index               (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo                       (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo_dca                   (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo_scah                  (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    albedo_scav                  (phony_dim_0, phony_dim_1) float32 2MB ...\n",
        "    ...                           ...\n",
-       "    tb_v_uncorrected             (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity           (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity_dca       (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity_scah      (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_opacity_scav      (phony_dim_0, phony_dim_1) float32 ...\n",
-       "    vegetation_water_content     (phony_dim_0, phony_dim_1) float32 ..."
+       "    tb_v_uncorrected             (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity           (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity_dca       (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity_scah      (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_opacity_scav      (phony_dim_0, phony_dim_1) float32 2MB ...\n",
+       "    vegetation_water_content     (phony_dim_0, phony_dim_1) float32 2MB ..."
       ]
      },
-     "execution_count": 12,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -717,488 +696,2099 @@
     "gds"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "05d558e4-448d-4c7a-a188-ea5cf27dc124",
-   "metadata": {},
-   "source": [
-    "#### Add meaningful dimension names\n",
-    "\n",
-    "53 variables are loaded from `Soil_Moisture_Retrieval_Data_AM`.  However, we can immediately see some issues.  The dimensions are named `phony_dim_0`, `phony_dim_1`, and `phony_dim_2`, which is not much help.  This is because no dimensions are assigned to Datasets, so `xarray` has to generate some dummy variables.\n",
-    " \n",
-    "We'll change the names of the dimensions to something meaningful.  First, we need figure out what the dimensions are.  This is more diffcult than is should be and requires some data sleuthing and assumptions.  The description of the `landcover_class` offers some information, albeit misleading, but it is a clue.\n",
-    "\n",
-    "> An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)\n",
-    "\n",
-    "This description states that the dimensions of the landcover_class array are longitude, latitude and IGBP land cover class.  The data are in projected coordinates (the EASE Grid 2.0 Global) **so longitude and latitude cannot be dimensions**.  Longitude and latitude in projected coordinate systems must be 2D arrays.  For projected coordinate systems, coordnates are usually x and y, or easting and northing.  From the user guide, we also know that the data are on a grid with 964 columns and 406 rows, and cells that have a nominal cell-size of 36 km by 36 km.  Based on this information we can assume that `phony_dim_0` is the y-dimension and `phony_dim_1` is the x-dimension in cartesian coordinates.  The 3rd dimension (`phoney_dim_2`) appears to be land cover class.  We can lot the data to confirm that this at least looks correct.  "
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "2399bc41-3f7e-43b6-bc1f-10d9b9893e32",
+   "execution_count": 7,
+   "id": "7d30857f-9983-437f-a14e-27345a4e2b21",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f2a47c03730>"
+       "PosixPath('/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170327_R18290_001.h5')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gds.albedo.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ffe65d22-0357-455b-8896-4870cf676583",
-   "metadata": {},
-   "source": [
-    "Apart from looking like the data is upside down, our assumptions about `x` and `y` look to be correct.  So we'll rename the coordinates:\n",
-    "\n",
-    " - `phony_dim_0 --> y`\n",
-    " - `phony_dim_1 --> x`\n",
-    " - `phony_dim_2 --> igbp_class`\n",
-    "\n",
-    "We can do this using `rename_dims`, which takes a dictionary as an argument, where keys are the old dimension name and values are the new dimension name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "a445433e-d160-4ca2-9a58-a0b74ccf348b",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "cannot rename 'phony_dim_0' because it is not found in the dimensions of this dataset ('y', 'x', 'igbp_class')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_24814/2980279117.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'phony_dim_0'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'phony_dim_1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'x'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'phony_dim_2'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'igbp_class'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mgds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/mambaforge/envs/smap-tutorial/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dims_dict, **dims)\u001b[0m\n\u001b[1;32m   4319\u001b[0m         \"\"\"\n\u001b[1;32m   4320\u001b[0m         \u001b[0mdims_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meither_dict_or_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rename_dims\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4321\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdims_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4322\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4323\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   4324\u001b[0m                     \u001b[0;34mf\"cannot rename {k!r} because it is not found \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4325\u001b[0m                     \u001b[0;34mf\"in the dimensions of this dataset {tuple(self.dims)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4326\u001b[0m                 )\n",
-      "\u001b[0;31mValueError\u001b[0m: cannot rename 'phony_dim_0' because it is not found in the dimensions of this dataset ('y', 'x', 'igbp_class')"
-     ]
     }
    ],
    "source": [
-    "gds = gds.rename_dims({'phony_dim_0': 'y', 'phony_dim_1': 'x', 'phony_dim_2': 'igbp_class'})\n",
-    "gds"
+    "smap_files[0]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "de673df1-483f-47b0-9e9a-c1aadd711629",
+   "id": "05d558e4-448d-4c7a-a188-ea5cf27dc124",
    "metadata": {},
    "source": [
-    "#### Add coordinate variables\n",
+    "#### Add meaningful dimension names\n",
     "\n",
-    "[Coordinate variables](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#coordinate-types) locate the data in space and time.  Each coordinate variable is a 1-dimensional array associated with a single dimension, usually with the same name as that dimension, and has the same size as that dimensions.  Values for coordinate variables give the position of each grid cell within the dataset's coordinate system.  Two-dimensional _auxillary coordinates_ are also allowed.  These _auxillary coordinates_ need to be related to data variables by including a `coordinates` attribute that lists the _auxillary coordinates_.  Coordinate variables are not allowed to have missing values.  However, _auxillary coordinates_ can have missing values.\n",
+    "53 variables are loaded from `Soil_Moisture_Retrieval_Data_AM`.  However, we can immediately see some issues.  The dimensions are named `phony_dim_0`, `phony_dim_1`, and `phony_dim_2`, which is not much help.  This is because no dimensions are assigned to Datasets, so `xarray` has to generate some dummy variables.\n",
+    " \n",
+    "We'll change the names of the dimensions to something meaningful.  First, we need figure out what the dimensions are.  This is more diffcult than is should be and requires some data sleuthing and assumptions.  The description of the `landcover_class` offers some information, albeit misleading, but it is a clue.\n",
     "\n",
-    "The SMAP data contain 2-dimensional `latitude` and `longitude` grids but data variables do not have a `coordinates` attribute.  The data do not contain coordinate variables.  Coordinate variables allow efficient subsetting, as well as plotting and transformation.  So we need to add `x` and `y` as coordinates.  \n",
+    "> An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)\n",
     "\n",
-    "Because `latitude` and `longitude` are likely coordinate variables, we'll plot them. "
+    "This description states that the dimensions of the landcover_class array are longitude, latitude and IGBP land cover class.  The data are in projected coordinates (the EASE Grid 2.0 Global) **so longitude and latitude cannot be dimensions**.  Longitude and latitude in projected coordinate systems must be 2D arrays.  For projected coordinate systems, coordnates are usually x and y, or easting and northing.  From the user guide, we also know that the data are on a grid with 964 columns and 406 rows, and cells that have a nominal cell-size of 36 km by 36 km.  Based on this information we can assume that `phony_dim_0` is the y-dimension and `phony_dim_1` is the x-dimension in cartesian coordinates.  The 3rd dimension (`phoney_dim_2`) appears to be land cover class.  We can lot the data to confirm that this at least looks correct.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "96f26e1e-2c6d-4f57-bf51-72653e2c76d9",
+   "execution_count": 8,
+   "id": "2399bc41-3f7e-43b6-bc1f-10d9b9893e32",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f2a47238f40>"
+       "<matplotlib.collections.QuadMesh at 0x3269ac640>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2, 1)\n",
-    "gds.latitude.plot(ax=ax[0])\n",
-    "gds.longitude.plot(ax=ax[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a45a18fd-dc03-42a2-a221-2e77d405ebfb",
-   "metadata": {},
-   "source": [
-    "This looks a little strange.  Printing minima and maxima for `latitude` and `longitude`, shows that both values are outside of the expected ranges: -90 to 90 for `latitude`, and -180 to 180 or 0 to 360 for `longitude`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "fe9bc187-b64d-414d-9074-5bc5e33c3774",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Latitude:  Min = -9999.0, Max = 83.63197326660156\n",
-      "Longitude: Min = -9999.0, Max = 179.8132781982422\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Latitude:  Min = {gds.latitude.min().values}, Max = {gds.latitude.max().values}\")\n",
-    "print(f\"Longitude: Min = {gds.longitude.min().values}, Max = {gds.longitude.max().values}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba404a65-2dd2-4f1b-a538-40861dfcd199",
-   "metadata": {},
-   "source": [
-    "If we plot both latitude and soil_moisture, we can see that latitude values have been masked with -9999. where there are no valid data values.  **This is not a good or sensible approach because latitude and longitude are coordinates.  As long as latitude and longitude values are between -90 and 90, and -180 and 180 (or 0 and 360), they are valid coordinates.  While missing values are allowed for _auxillary coordinates_, coordinate values should not have missing values.  We also need coordinates for all grid cells for plotting and other operations.** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "18d8326c-fea7-4fb5-a7bc-e38f4bbfdd05",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f2a471753f0>"
+       "<Figure size 672x480 with 2 Axes>"
       ]
      },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
+     "metadata": {
+      "image/png": {
+       "height": 434,
+       "width": 620
+      }
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(2, 1)\n",
-    "gds.latitude.plot(ax=ax[0]);\n",
-    "gds.soil_moisture.plot(ax=ax[1])"
+    "gds.albedo.plot()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "43d5222c-82ba-4c58-83b5-0b13c1919ec2",
-   "metadata": {},
-   "source": [
-    "Neither the plot of latitude nor the minimum and maximum values look as we would expect.  We expect latitude to be in the range of -90 to +90.  The maximum is actually in this range but the minimum is -9999., which is suspiciously like a `_FillData` or missing data value.  We would also expect the image to show a smooth field that only varies in the vertical direction, e.g. from -90. to 90. or some similar range.  Instead it shows a set of lozenges.\n",
-    "\n",
-    "Let's set the `_FillValue` attribute for latitude to `-9999`.  In `xarray`, we have to use `where` to apply a mask everywhere a condition is not `True`.  Here we set the condition to be _not equal to -9999._  To save time and errors later, we also set the `encoding` entry for `fillvalue` to -9999.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "`encoding` is a dictionary of parameters that defines how data are \"encoded\" in the file.  It includes information about how data are compressed, chunked, the _FillValue, and data type.  Saving the `encoding` is not strictly necessary unless you want to write the data to a new file.  We'll follow these steps here to demonstrate how to preserve the encoding data. \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "c65618de-eb3d-4f07-804c-81f0199a3ed4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'chunksizes': (1, 964), 'fletcher32': False, 'shuffle': False, 'preferred_chunks': {'phony_dim_0': 1, 'phony_dim_1': 964}, 'zlib': True, 'complevel': 2, 'source': '/home/apbarret/src/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170323_R18290_001.h5', 'original_shape': (406, 964), 'dtype': dtype('<f4')}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(gds.latitude.encoding)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "efc06b5a-fe82-4732-8753-f589485bb3f5",
+   "id": "ffe65d22-0357-455b-8896-4870cf676583",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "fillvalue = -9999.\n",
+    "Apart from looking like the data is upside down, our assumptions about `x` and `y` look to be correct.  So we'll rename the coordinates:\n",
     "\n",
-    "# `where` does not preserve encoding, so we have to copy first\n",
-    "encoding = gds.latitude.encoding\n",
-    "# Apply fillvalue mask\n",
-    "gds['latitude'] = gds.latitude.where(gds.latitude != fillvalue)\n",
-    "# we copy the encoding back to DataArray\n",
-    "gds.latitude.encoding = encoding\n",
-    "# ...and update encoding to include fillvalue\n",
-    "gds.latitude.encoding['fillvalue'] = fillvalue\n",
+    " - `phony_dim_0 --> y`\n",
+    " - `phony_dim_1 --> x`\n",
+    " - `phony_dim_2 --> igbp_class`\n",
     "\n",
-    "# ...and for longitude\n",
-    "encoding = gds.longitude.encoding\n",
-    "gds[\"longitude\"] = gds.longitude.where(gds.longitude != fillvalue)\n",
-    "gds.longitude.encoding = encoding\n",
-    "gds.longitude.encoding[\"fillvalue\"] = fillvalue\n",
-    "                                       "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "511b2d12-c6ce-4fc4-b924-d5b3e4b13cf9",
-   "metadata": {},
-   "source": [
-    "Now when we check the range of the latitude values, we see that the range is between -83.63... and 83.63... as we expect."
+    "We can do this using `rename_dims`, which takes a dictionary as an argument, where keys are the old dimension name and values are the new dimension name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "ec868a8e-bf13-4967-bde6-02cc2b07738e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Latitude:  Min = -83.63197326660156, Max = 83.63197326660156\n",
-      "Longitude: Min = -179.8132781982422, Max = 179.8132781982422\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Latitude:  Min = {gds.latitude.min().values}, Max = {gds.latitude.max().values}\")\n",
-    "print(f\"Longitude: Min = {gds.longitude.min().values}, Max = {gds.longitude.max().values}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5acf9bca-d60d-4d34-9618-7d33e7236772",
-   "metadata": {},
-   "source": [
-    "Plotting the data again, we see that latitude values are within the expected range but that hasn't solved the missing data problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "c7d6df08-b03f-465a-bb21-d6a68665fde2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f2a45e7d3f0>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(3, 1)\n",
-    "gds.latitude.plot(ax=ax[0]);\n",
-    "gds.longitude.plot(ax=ax[1]);\n",
-    "gds.soil_moisture.plot(ax=ax[2])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "efc6edf9-0555-4256-86c7-c6527c9746c7",
-   "metadata": {},
-   "source": [
-    "#### Adding x and y coordinates\n",
-    "\n",
-    "Missing values for latitude and longitude can cause problems for plotting and other operations.  As noted above, the files do not include complete information about the coordinate reference system or the grid definition.  So we need to do some data sleuthing.\n",
-    "\n",
-    "Tables 4 and 5 in the [User Guide](https://nsidc.org/sites/default/files/spl3smp-v008-userguide.pdf) provide geospatial information.  Further information on EASE Grids can be found on [A Guide to EASE Grids page](https://nsidc.org/data/user-resources/help-center/guide-ease-grids).\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>Note:</b> The ATBD for the L3 SMP data gives the data array dimensions as 406 x 963.\n",
-    "</div>\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d642bf9-c8fb-431d-8467-7720a3378081",
-   "metadata": {},
-   "source": [
-    "The CRS and grid definition are independent.  The CRS defines how projected coordinates, usually in meters, relate to geographic latitude and longitude.  The grid definition relates how image coordinates (columns and rows) map to the projected coordinates.  The Guide to EASE Grids has 8 grid definitions, ranging from 1 km to 36 km cell sizes, for the Global EASE-Grid 2.0 CRS (EPSG:9633).  We could define many more grid definitions.  For more information on CRS, map projections and grids see [_Link to be added_]().\n",
-    "\n",
-    "In the [table](https://nsidc.org/data/user-resources/help-center/guide-ease-grids#anchor-10) for the 36 km Global EASE-Grid 2.0 we are given the height and width of each grid cell, and the x and y coordinates of the upper-left corner of the upper-left grid cell.  We can see that the 36 km grid cell size is not exact.  In fact the value the table gives is not exact.  The actual value runs to 9 decimal places.  This is because the grid cell height and width of the global grid were defined so that an exact number of cells span the equator {@brodzik_ease}.  This is not necessary for polar grids.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<b>Tip:</b> Exact definitions of CRS, projection and grid parameters can be found in the Grid Parameter Definition (gpd) files in the [`mapxmaps` GitHub repository](https://github.com/nsidc/mapxmaps).  These gpd files have a somewhat esoteric format, so might take a little bit to understand.  It is important that the grid definition is exact because we cannot merge datasets if the coordinates do not match, even to the 9th decimal place.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "78dc5886-d10a-4ca7-b2df-e10ac1de0c8d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_cell_width = 36032.220840584\n",
-    "grid_cell_height = -36032.220840584\n",
-    "x_upper_left_corner = -17367530.4451615\n",
-    "y_upper_left_corner = 7314540.8306386"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "927e8b99-28d0-46d0-9c0e-8f9fd6223861",
-   "metadata": {},
-   "source": [
-    "The projected coordinates of a grid cell can be found using the following formulas:\n",
-    "\n",
-    "$$\n",
-    "x = (grid\\_cell\\_width \\times row) + x\\_upper\\_left\\_corner\n",
-    "$$\n",
-    "$$\n",
-    "y = (grid\\_cell\\_height \\times column) + y\\_upper\\_left\\_corner)\n",
-    "$$\n",
-    "\n",
-    "This equations assumes that the origin of the image coordinates start at row=0. and column=0. in the upper-left corner of the upper-left grid cell.  So the center of the upper-left grid cell is row=0.5 and column=0.5.  Adding 0.5 to row and column indices solves this problem.\n",
-    "\n",
-    "We could write a function to return (x, y) coordinates for (column, row) image coordinates using the equations above.  Or we can use the `Affine` class from the `affine` package to define an affine matrix.  This is what we'll do below.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "feed97dc-b521-4240-9446-8811c8694aeb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Affine(36032.220840584, 0.0, -17367530.4451615,\n",
-       "       0.0, -36032.220840584, 7314540.8306386)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transform = Affine(grid_cell_width, 0.0, x_upper_left_corner, \n",
-    "                   0.0, grid_cell_height, y_upper_left_corner)\n",
-    "transform"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08669cb5-4848-41e7-a08b-0b3e71d21f40",
-   "metadata": {},
-   "source": [
-    "This then allows us to calculate x and y coordinates from column and row indices using matrix multiplication."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "98bd706e-78fc-4cc5-a11a-ff0cb22ca93c",
+   "execution_count": 9,
+   "id": "a445433e-d160-4ca2-9a58-a0b74ccf348b",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 99MB\n",
+       "Dimensions:                      (y: 406, x: 964, igbp_class: 3)\n",
+       "Dimensions without coordinates: y, x, igbp_class\n",
+       "Data variables: (12/53)\n",
+       "    EASE_column_index            (y, x) float32 2MB ...\n",
+       "    EASE_row_index               (y, x) float32 2MB ...\n",
+       "    albedo                       (y, x) float32 2MB ...\n",
+       "    albedo_dca                   (y, x) float32 2MB ...\n",
+       "    albedo_scah                  (y, x) float32 2MB ...\n",
+       "    albedo_scav                  (y, x) float32 2MB ...\n",
+       "    ...                           ...\n",
+       "    tb_v_uncorrected             (y, x) float32 2MB ...\n",
+       "    vegetation_opacity           (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_dca       (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scah      (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scav      (y, x) float32 2MB ...\n",
+       "    vegetation_water_content     (y, x) float32 2MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-54f22730-8cba-4c41-ab50-a17ec413acfd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54f22730-8cba-4c41-ab50-a17ec413acfd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 406</li><li><span>x</span>: 964</li><li><span>igbp_class</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1bcfd857-94c8-4d59-9a1a-9805c258b64e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1bcfd857-94c8-4d59-9a1a-9805c258b64e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-cf7ea2cd-b821-4bcd-92c0-8db0768f377e' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf7ea2cd-b821-4bcd-92c0-8db0768f377e' class='xr-section-summary' >Data variables: <span>(53)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>EASE_column_index</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-efecbb2f-53f7-4b2d-9490-69c4e03f1a89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efecbb2f-53f7-4b2d-9490-69c4e03f1a89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b6695a6-084c-4d63-b408-106472fccd7f' class='xr-var-data-in' type='checkbox'><label for='data-4b6695a6-084c-4d63-b408-106472fccd7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The column index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>963</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EASE_row_index</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25acfba8-70f5-4f3c-940c-9955f086cb44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25acfba8-70f5-4f3c-940c-9955f086cb44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de6df22d-c86e-40ee-a809-3aac3359ee6e' class='xr-var-data-in' type='checkbox'><label for='data-de6df22d-c86e-40ee-a809-3aac3359ee6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The row index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>405</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be08b8ab-45f2-487b-b068-8554d953f794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be08b8ab-45f2-487b-b068-8554d953f794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84820bdd-c74a-4113-95ea-7693e9ad5118' class='xr-var-data-in' type='checkbox'><label for='data-84820bdd-c74a-4113-95ea-7693e9ad5118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c81b1d04-5a48-4811-8080-7959485dd0df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c81b1d04-5a48-4811-8080-7959485dd0df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-609e8d85-2176-418e-872d-e85fab1a224f' class='xr-var-data-in' type='checkbox'><label for='data-609e8d85-2176-418e-872d-e85fab1a224f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6a7badc-0974-4659-896b-c10c763ac3de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a7badc-0974-4659-896b-c10c763ac3de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ade777ee-4011-4ce4-8a32-ea27712fc116' class='xr-var-data-in' type='checkbox'><label for='data-ade777ee-4011-4ce4-8a32-ea27712fc116' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-H within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ac59560-d7d0-4117-90af-1d8b49b5dd05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ac59560-d7d0-4117-90af-1d8b49b5dd05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8dc846a-c22a-4646-a517-d03e6badae6d' class='xr-var-data-in' type='checkbox'><label for='data-c8dc846a-c22a-4646-a517-d03e6badae6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-V within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boresight_incidence</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26639961-d87d-4ab1-8415-6f35007b2b3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26639961-d87d-4ab1-8415-6f35007b2b3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3585ddc0-61cc-4b30-a812-685d07d0261c' class='xr-var-data-in' type='checkbox'><label for='data-3585ddc0-61cc-4b30-a812-685d07d0261c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative angle between the antenna boresight vector and the normal to the Earth&amp;apos;s surface for all footprints within the cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bulk_density</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95781252-4110-4410-93b9-ec15c59fdabd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95781252-4110-4410-93b9-ec15c59fdabd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55bb096c-84da-4c37-ad11-10a5bc55ee61' class='xr-var-data-in' type='checkbox'><label for='data-55bb096c-84da-4c37-ad11-10a5bc55ee61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated bulk_density within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>2.65</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ad0f497-5754-4f9d-b6cf-8424a5d15a2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ad0f497-5754-4f9d-b6cf-8424a5d15a2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-145a8c5b-5a1c-4801-8e4b-e471a3c3b0f1' class='xr-var-data-in' type='checkbox'><label for='data-145a8c5b-5a1c-4801-8e4b-e471a3c3b0f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freeze_thaw_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0798f4ab-41c0-4ce9-aafd-059206e73b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0798f4ab-41c0-4ce9-aafd-059206e73b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea433e67-bf37-460e-8e18-ba23924197ac' class='xr-var-data-in' type='checkbox'><label for='data-ea433e67-bf37-460e-8e18-ba23924197ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fraction of the 36 km grid cell that is denoted as frozen.  Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_surface_status</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e441368-fe72-4152-bdd1-8dedcb0b6af2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e441368-fe72-4152-bdd1-8dedcb0b6af2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e05f7f96-f8c2-4e48-9b51-b5414f439275' class='xr-var-data-in' type='checkbox'><label for='data-e05f7f96-f8c2-4e48-9b51-b5414f439275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Indicates if the grid point lies on land (0) or water (1).</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class</span></div><div class='xr-var-dims'>(y, x, igbp_class)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca4a3302-fb4d-4ea7-9464-e9443e525e4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca4a3302-fb4d-4ea7-9464-e9443e525e4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5441955c-e6be-4e67-8dd7-8fdb5714a32b' class='xr-var-data-in' type='checkbox'><label for='data-5441955c-e6be-4e67-8dd7-8fdb5714a32b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class_fraction</span></div><div class='xr-var-dims'>(y, x, igbp_class)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92a26437-b4a4-48a9-a695-5251990e74ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92a26437-b4a4-48a9-a695-5251990e74ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-456725e6-77d9-46ad-b8ac-bc9115675624' class='xr-var-data-in' type='checkbox'><label for='data-456725e6-77d9-46ad-b8ac-bc9115675624' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the grid cell that contains the most common land cover in that area based on the IGBP landcover map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3442a4ec-5879-44c8-b977-a586b3de60be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3442a4ec-5879-44c8-b977-a586b3de60be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6a86b0a-49a4-4677-b004-dad02b5baf9a' class='xr-var-data-in' type='checkbox'><label for='data-d6a86b0a-49a4-4677-b004-dad02b5baf9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_centroid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5744bd84-754f-4633-b133-28c5bcd5db9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5744bd84-754f-4633-b133-28c5bcd5db9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8167287d-8808-4038-b522-222560f507cf' class='xr-var-data-in' type='checkbox'><label for='data-8167287d-8808-4038-b522-222560f507cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the latitude of the center of the  brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5786b8e6-8134-4c89-9611-26752b3a512b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5786b8e6-8134-4c89-9611-26752b3a512b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3463dbba-9dae-43ad-a079-be337fdf8b5f' class='xr-var-data-in' type='checkbox'><label for='data-3463dbba-9dae-43ad-a079-be337fdf8b5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_centroid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a04cb968-7073-4b8a-9877-4e67f7a05f4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a04cb968-7073-4b8a-9877-4e67f7a05f4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69c7ab30-39dd-47c2-b868-2b8ea05bb0e4' class='xr-var-data-in' type='checkbox'><label for='data-69c7ab30-39dd-47c2-b868-2b8ea05bb0e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the longitude of the center of the brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>179.999</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_water_body_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e94dc325-d1e8-4fa7-92c0-a18b128b762e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e94dc325-d1e8-4fa7-92c0-a18b128b762e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b049cab2-dfb1-4796-ac7c-8b91be5b021e' class='xr-var-data-in' type='checkbox'><label for='data-b049cab2-dfb1-4796-ac7c-8b91be5b021e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ad27441-d315-4fa0-9be9-7167795624cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ad27441-d315-4fa0-9be9-7167795624cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a953b097-fe5f-45cc-9638-95031a182b05' class='xr-var-data-in' type='checkbox'><label for='data-a953b097-fe5f-45cc-9638-95031a182b05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f52db816-afe0-43f1-bd8b-d1b2a4f8deba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f52db816-afe0-43f1-bd8b-d1b2a4f8deba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d13db92-721f-42f7-abd5-4e7fa794f581' class='xr-var-data-in' type='checkbox'><label for='data-9d13db92-721f-42f7-abd5-4e7fa794f581' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57674d42-b809-467e-8e82-a6b48d1eb44a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57674d42-b809-467e-8e82-a6b48d1eb44a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea28a23b-b2fb-42cc-a3d6-5d0e0b278457' class='xr-var-data-in' type='checkbox'><label for='data-ea28a23b-b2fb-42cc-a3d6-5d0e0b278457' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-H retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c6f0b4a-6286-44f4-8c0f-6fbadeb493f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c6f0b4a-6286-44f4-8c0f-6fbadeb493f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e086ee0a-fac4-4008-bda7-151bdcb5d9af' class='xr-var-data-in' type='checkbox'><label for='data-e086ee0a-fac4-4008-bda7-151bdcb5d9af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-V retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78efdf2f-059d-4621-ab40-98235588f92a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78efdf2f-059d-4621-ab40-98235588f92a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfb605e6-ce15-46ac-a847-a1472f604a2d' class='xr-var-data-in' type='checkbox'><label for='data-dfb605e6-ce15-46ac-a847-a1472f604a2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-125837d7-8d08-481f-b6db-2a7bfed619c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-125837d7-8d08-481f-b6db-2a7bfed619c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df6d05d-eb49-4709-a893-fe8e7efece47' class='xr-var-data-in' type='checkbox'><label for='data-2df6d05d-eb49-4709-a893-fe8e7efece47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-20134dc3-25e8-403f-9245-c9c7c06bf12e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20134dc3-25e8-403f-9245-c9c7c06bf12e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fbda571-1b74-4359-bf18-d66db6bb6471' class='xr-var-data-in' type='checkbox'><label for='data-6fbda571-1b74-4359-bf18-d66db6bb6471' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-H within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8bd99485-dbd1-4343-9eac-c6d008d2ede4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bd99485-dbd1-4343-9eac-c6d008d2ede4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0923f10f-b692-44bc-8546-3bcc96db7784' class='xr-var-data-in' type='checkbox'><label for='data-0923f10f-b692-44bc-8546-3bcc96db7784' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-V within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9365b228-6143-4819-8795-af22437da2bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9365b228-6143-4819-8795-af22437da2bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c953f700-dba3-46c5-8fb6-6d167a887a98' class='xr-var-data-in' type='checkbox'><label for='data-c953f700-dba3-46c5-8fb6-6d167a887a98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4d566279-cf44-4cba-a798-0ae3bbd0d15d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d566279-cf44-4cba-a798-0ae3bbd0d15d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66838513-2b41-4dd8-9ee2-9d4e4755b033' class='xr-var-data-in' type='checkbox'><label for='data-66838513-2b41-4dd8-9ee2-9d4e4755b033' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_error</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7eac8907-271f-46dd-9db2-aed347c946e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7eac8907-271f-46dd-9db2-aed347c946e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e2e9947-3095-4035-a5dc-c9d869e130f2' class='xr-var-data-in' type='checkbox'><label for='data-3e2e9947-3095-4035-a5dc-c9d869e130f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52c67272-c362-4062-8784-d2e8152a343b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52c67272-c362-4062-8784-d2e8152a343b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ec67b64-e7e9-4a66-b5e1-7990a1dd8ee8' class='xr-var-data-in' type='checkbox'><label for='data-8ec67b64-e7e9-4a66-b5e1-7990a1dd8ee8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-H soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2eee70d0-f3cf-4f93-ae96-50e179e5081f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2eee70d0-f3cf-4f93-ae96-50e179e5081f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c54a4e2a-042e-4e61-8152-6a3c8c04b649' class='xr-var-data-in' type='checkbox'><label for='data-c54a4e2a-042e-4e61-8152-6a3c8c04b649' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-V soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>static_water_body_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10b7e076-35b8-4f7d-a392-b4b8fa6d13e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10b7e076-35b8-4f7d-a392-b4b8fa6d13e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2547c69-a7b5-4319-b79c-4082564815dd' class='xr-var-data-in' type='checkbox'><label for='data-c2547c69-a7b5-4319-b79c-4082564815dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8401db83-3ea7-4200-8835-795f031f42b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8401db83-3ea7-4200-8835-795f031f42b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc597a5f-b2c2-49fa-81bf-88c16ada4ca5' class='xr-var-data-in' type='checkbox'><label for='data-cc597a5f-b2c2-49fa-81bf-88c16ada4ca5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s</dd><dt><span>flag_meanings :</span></dt><dd>36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radiometer_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region</dd><dt><span>long_name :</span></dt><dd>Bit flags that record ambient surface conditions for the grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_temperature</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b2f1e4e-9cbf-4906-83f0-5cf4ba25c2e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b2f1e4e-9cbf-4906-83f0-5cf4ba25c2e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c50d35-8779-4c72-8efb-5dd0f1800086' class='xr-var-data-in' type='checkbox'><label for='data-e8c50d35-8779-4c72-8efb-5dd0f1800086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature at land surface based on GMAO GEOS-5 data.</dd><dt><span>units :</span></dt><dd>Kelvins</dd><dt><span>valid_max :</span></dt><dd>350.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_h</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-543f8bc3-7227-495f-a0c9-1d98c3adec5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-543f8bc3-7227-495f-a0c9-1d98c3adec5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8eb994a-4deb-47a2-9ecd-c64844317c98' class='xr-var-data-in' type='checkbox'><label for='data-b8eb994a-4deb-47a2-9ecd-c64844317c98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer horizontal polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-14987f3f-5228-4005-9a0d-2c81c301c13f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14987f3f-5228-4005-9a0d-2c81c301c13f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f04be86a-d831-4be4-bee0-da09c1663732' class='xr-var-data-in' type='checkbox'><label for='data-f04be86a-d831-4be4-bee0-da09c1663732' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer vertical polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_3_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ad338cf-32a3-4062-bb62-01dcf0870d34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ad338cf-32a3-4062-bb62-01dcf0870d34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94faf8b-9e6c-4a4d-baf4-55b8c7169260' class='xr-var-data-in' type='checkbox'><label for='data-b94faf8b-9e6c-4a4d-baf4-55b8c7169260' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Third stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_4_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-81654560-f4ae-4bd6-a7b3-bd56eac4731e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81654560-f4ae-4bd6-a7b3-bd56eac4731e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ad788b1-6211-4373-ac8f-dc751564a045' class='xr-var-data-in' type='checkbox'><label for='data-8ad788b1-6211-4373-ac8f-dc751564a045' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fourth stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cefe03a2-13d7-4c23-a943-538cc27883bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cefe03a2-13d7-4c23-a943-538cc27883bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e55ce0-e97e-43a6-964a-8f2c635fd7a4' class='xr-var-data-in' type='checkbox'><label for='data-44e55ce0-e97e-43a6-964a-8f2c635fd7a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_uncorrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34668b51-6632-4390-ad1b-790e7fb7c898' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34668b51-6632-4390-ad1b-790e7fb7c898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af6d0289-3b28-4c7a-945e-59d496f38a4b' class='xr-var-data-in' type='checkbox'><label for='data-af6d0289-3b28-4c7a-945e-59d496f38a4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_3</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a8628b0-f966-4fab-a363-0b6540ca5273' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a8628b0-f966-4fab-a363-0b6540ca5273' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1bff4df-59ee-4de6-b5f1-80efcae0cc05' class='xr-var-data-in' type='checkbox'><label for='data-e1bff4df-59ee-4de6-b5f1-80efcae0cc05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>3rd_Stokes_quality 3rd_Stokes_range 3rd_Stokes_RFI_detection 3rd_Stokes_RFI_correction 3rd_Stokes_NEDT 3rd_Stokes_direct_sun_correction 3rd_Stokes_reflected_sun_correction 3rd_Stokes_reflected_moon_correction 3rd_Stokes_direct_galaxy_correction 3rd_Stokes_reflected_galaxy_correction 3rd_Stokes_atmosphere_correction 3rd_Stokes_null_value_bit 3rd_Stokes_RFI_check 3rd_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 3rd Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_4</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cd2f453-8bb6-4d4a-83c1-6bedfdfed946' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cd2f453-8bb6-4d4a-83c1-6bedfdfed946' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e60faea7-dfa7-479b-9e37-4eebb0476f31' class='xr-var-data-in' type='checkbox'><label for='data-e60faea7-dfa7-479b-9e37-4eebb0476f31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>4th_Stokes_quality 4th_Stokes_range 4th_Stokes_RFI_detection 4th_Stokes_RFI_correction 4th_Stokes_NEDT 4th_Stokes_direct_sun_correction 4th_Stokes_reflected_sun_correction 4th_Stokes_reflected_moon_correction 4th_Stokes_direct_galaxy_correction 4th_Stokes_reflected_galaxy_correction 4th_Stokes_atmosphere_correction 4th_Stokes_null_value_bit 4th_Stokes_RFI_check 4th_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 4th Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_h</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce68f9ba-5f6e-4733-b7b2-e1b61037eeae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce68f9ba-5f6e-4733-b7b2-e1b61037eeae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e6f35ca-be0c-4b8e-8522-7fc3828b1153' class='xr-var-data-in' type='checkbox'><label for='data-8e6f35ca-be0c-4b8e-8522-7fc3828b1153' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Horizontal_polarization_quality Horizontal_polarization_range Horizontal_polarization_RFI_detection Horizontal_polarization_RFI_correction Horizontal_polarization_NEDT Horizontal_polarization_direct_sun_correction Horizontal_polarization_reflected_sun_correction Horizontal_polarization_reflected_moon_correction Horizontal_polarization_direct_galaxy_correction Horizontal_polarization_reflected_galaxy_correction Horizontal_polarization_atmosphere_correction Horizontal_polarization_Faraday_rotation_correction Horizontal_polarization_null_value_bit Horizontal_polarization_water_correction Horizontal_polarization_RFI_check Horizontal_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the horizontal polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84f99023-2574-4adb-9e14-447b89d861be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f99023-2574-4adb-9e14-447b89d861be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91bd1cc-617c-43c0-90c5-eafe44789c63' class='xr-var-data-in' type='checkbox'><label for='data-f91bd1cc-617c-43c0-90c5-eafe44789c63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Vertical_polarization_quality Vertical_polarization_range Vertical_polarization_RFI_detection Vertical_polarization_RFI_correction Vertical_polarization_NEDT Vertical_polarization_direct_sun_correction Vertical_polarization_reflected_sun_correction Vertical_polarization_reflected_moon_correction Vertical_polarization_direct_galaxy_correction Vertical_polarization_reflected_galaxy_correction Vertical_polarization_atmosphere_correction Vertical_polarization_Faraday_rotation_correction Vertical_polarization_null_value_bit Vertical_polarization_water_correction Vertical_polarization_RFI_check Vertical_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the vertical polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_seconds</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a96cce9c-647c-4af1-b70f-9c4f72cc7949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a96cce9c-647c-4af1-b70f-9c4f72cc7949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16858750-6b21-4913-9378-979bcb623b1c' class='xr-var-data-in' type='checkbox'><label for='data-16858750-6b21-4913-9378-979bcb623b1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in seconds since noon on January 1, 2000 UTC.</dd><dt><span>valid_max :</span></dt><dd>940000000.0</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_utc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>|S24</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d84259cb-fbbd-48c6-a8ca-654bd1f943b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d84259cb-fbbd-48c6-a8ca-654bd1f943b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b963aea-20b7-4056-8c65-2ad0d4ff6f6d' class='xr-var-data-in' type='checkbox'><label for='data-8b963aea-20b7-4056-8c65-2ad0d4ff6f6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in UTC.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=|S24]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b901ef1-90d5-4691-9355-b3718df41331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b901ef1-90d5-4691-9355-b3718df41331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b631ae7-7b5f-4471-84ca-eabc92fd9648' class='xr-var-data-in' type='checkbox'><label for='data-3b631ae7-7b5f-4471-84ca-eabc92fd9648' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_uncorrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d08bda3-a8b9-4294-b22c-87ba7afbbf93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d08bda3-a8b9-4294-b22c-87ba7afbbf93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-852c27ea-3977-411d-b66d-c2f2da5b92e7' class='xr-var-data-in' type='checkbox'><label for='data-852c27ea-3977-411d-b66d-c2f2da5b92e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2de22bb9-dfc8-45e1-a1e5-34be88db115e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2de22bb9-dfc8-45e1-a1e5-34be88db115e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51150040-4809-494c-a169-aa82ecbe017d' class='xr-var-data-in' type='checkbox'><label for='data-51150040-4809-494c-a169-aa82ecbe017d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3087413b-8b69-48de-88d6-dd2bd4801294' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3087413b-8b69-48de-88d6-dd2bd4801294' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2d3e594-d214-431a-bb6d-a5425dc4253d' class='xr-var-data-in' type='checkbox'><label for='data-e2d3e594-d214-431a-bb6d-a5425dc4253d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba9f5580-5a97-4442-9663-789ca0d038d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba9f5580-5a97-4442-9663-789ca0d038d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffdf0865-c998-4d9b-8889-f5693c50eddf' class='xr-var-data-in' type='checkbox'><label for='data-ffdf0865-c998-4d9b-8889-f5693c50eddf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-H retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e99258e0-dfc9-4b2f-9c8f-aac73109712b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e99258e0-dfc9-4b2f-9c8f-aac73109712b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b408c31-f4e4-4ab4-bb44-ac624ff7a594' class='xr-var-data-in' type='checkbox'><label for='data-9b408c31-f4e4-4ab4-bb44-ac624ff7a594' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-V retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_water_content</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56dac916-a8aa-4c4a-8b1d-48110d2e07d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56dac916-a8aa-4c4a-8b1d-48110d2e07d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d698d0ba-be89-4a4d-989e-bacbeffb9749' class='xr-var-data-in' type='checkbox'><label for='data-d698d0ba-be89-4a4d-989e-bacbeffb9749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative measure of water in the vegetation within the 36 km grid cell.</dd><dt><span>units :</span></dt><dd>kg/m**2</dd><dt><span>valid_max :</span></dt><dd>20.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e92e8d20-240f-4ea4-89d3-46c1862c18fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e92e8d20-240f-4ea4-89d3-46c1862c18fa' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-57d8b8cb-892c-48ae-a759-c46a8259ee3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-57d8b8cb-892c-48ae-a759-c46a8259ee3d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 99MB\n",
+       "Dimensions:                      (y: 406, x: 964, igbp_class: 3)\n",
+       "Dimensions without coordinates: y, x, igbp_class\n",
+       "Data variables: (12/53)\n",
+       "    EASE_column_index            (y, x) float32 2MB ...\n",
+       "    EASE_row_index               (y, x) float32 2MB ...\n",
+       "    albedo                       (y, x) float32 2MB ...\n",
+       "    albedo_dca                   (y, x) float32 2MB ...\n",
+       "    albedo_scah                  (y, x) float32 2MB ...\n",
+       "    albedo_scav                  (y, x) float32 2MB ...\n",
+       "    ...                           ...\n",
+       "    tb_v_uncorrected             (y, x) float32 2MB ...\n",
+       "    vegetation_opacity           (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_dca       (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scah      (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scav      (y, x) float32 2MB ...\n",
+       "    vegetation_water_content     (y, x) float32 2MB ..."
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds = gds.rename_dims({'phony_dim_0': 'y', 'phony_dim_1': 'x', 'phony_dim_2': 'igbp_class'})\n",
+    "gds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de673df1-483f-47b0-9e9a-c1aadd711629",
+   "metadata": {},
+   "source": [
+    "#### Add coordinate variables\n",
+    "\n",
+    "[Coordinate variables](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#coordinate-types) locate the data in space and time.  Each coordinate variable is a 1-dimensional array associated with a single dimension, usually with the same name as that dimension, and has the same size as that dimensions.  Values for coordinate variables give the position of each grid cell within the dataset's coordinate system.  Two-dimensional _auxillary coordinates_ are also allowed.  These _auxillary coordinates_ need to be related to data variables by including a `coordinates` attribute that lists the _auxillary coordinates_.  Coordinate variables are not allowed to have missing values.  However, _auxillary coordinates_ can have missing values.\n",
+    "\n",
+    "The SMAP data contain 2-dimensional `latitude` and `longitude` grids but data variables do not have a `coordinates` attribute.  The data do not contain coordinate variables.  Coordinate variables allow efficient subsetting, as well as plotting and transformation.  So we need to add `x` and `y` as coordinates.  \n",
+    "\n",
+    "Because `latitude` and `longitude` are likely coordinate variables, we'll plot them. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "96f26e1e-2c6d-4f57-bf51-72653e2c76d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x3268df0d0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 672x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 626
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 1)\n",
+    "gds.latitude.plot(ax=ax[0])\n",
+    "gds.longitude.plot(ax=ax[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a45a18fd-dc03-42a2-a221-2e77d405ebfb",
+   "metadata": {},
+   "source": [
+    "This looks a little strange.  Printing minima and maxima for `latitude` and `longitude`, shows that both values are outside of the expected ranges: -90 to 90 for `latitude`, and -180 to 180 or 0 to 360 for `longitude`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fe9bc187-b64d-414d-9074-5bc5e33c3774",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Latitude:  Min = -9999.0, Max = 83.63197326660156\n",
+      "Longitude: Min = -9999.0, Max = 179.8132781982422\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Latitude:  Min = {gds.latitude.min().values}, Max = {gds.latitude.max().values}\")\n",
+    "print(f\"Longitude: Min = {gds.longitude.min().values}, Max = {gds.longitude.max().values}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba404a65-2dd2-4f1b-a538-40861dfcd199",
+   "metadata": {},
+   "source": [
+    "If we plot both latitude and soil_moisture, we can see that latitude values have been masked with -9999. where there are no valid data values.  **This is not a good or sensible approach because latitude and longitude are coordinates.  As long as latitude and longitude values are between -90 and 90, and -180 and 180 (or 0 and 360), they are valid coordinates.  While missing values are allowed for _auxillary coordinates_, coordinate values should not have missing values.  We also need coordinates for all grid cells for plotting and other operations.** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "18d8326c-fea7-4fb5-a7bc-e38f4bbfdd05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x3231ec220>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 672x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 626
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 1)\n",
+    "gds.latitude.plot(ax=ax[0]);\n",
+    "gds.soil_moisture.plot(ax=ax[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43d5222c-82ba-4c58-83b5-0b13c1919ec2",
+   "metadata": {},
+   "source": [
+    "Neither the plot of latitude nor the minimum and maximum values look as we would expect.  We expect latitude to be in the range of -90 to +90.  The maximum is actually in this range but the minimum is -9999., which is suspiciously like a `_FillData` or missing data value.  We would also expect the image to show a smooth field that only varies in the vertical direction, e.g. from -90. to 90. or some similar range.  Instead it shows a set of lozenges.\n",
+    "\n",
+    "Let's set the `_FillValue` attribute for latitude to `-9999`.  In `xarray`, we have to use `where` to apply a mask everywhere a condition is not `True`.  Here we set the condition to be _not equal to -9999._  To save time and errors later, we also set the `encoding` entry for `fillvalue` to -9999.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "`encoding` is a dictionary of parameters that defines how data are \"encoded\" in the file.  It includes information about how data are compressed, chunked, the _FillValue, and data type.  Saving the `encoding` is not strictly necessary unless you want to write the data to a new file.  We'll follow these steps here to demonstrate how to preserve the encoding data. \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c65618de-eb3d-4f07-804c-81f0199a3ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'chunksizes': (1, 964), 'fletcher32': False, 'shuffle': False, 'preferred_chunks': {'phony_dim_0': 1, 'phony_dim_1': 964}, 'zlib': True, 'complevel': 2, 'source': '/Users/roma8902/projects/NSIDC-Data-Tutorials/notebooks/SMAP/smap_data/SMAP_L3_SM_P_20170327_R18290_001.h5', 'original_shape': (406, 964), 'dtype': dtype('<f4')}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gds.latitude.encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "efc06b5a-fe82-4732-8753-f589485bb3f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fillvalue = -9999.\n",
+    "\n",
+    "# `where` does not preserve encoding, so we have to copy first\n",
+    "encoding = gds.latitude.encoding\n",
+    "# Apply fillvalue mask\n",
+    "gds['latitude'] = gds.latitude.where(gds.latitude != fillvalue)\n",
+    "# we copy the encoding back to DataArray\n",
+    "gds.latitude.encoding = encoding\n",
+    "# ...and update encoding to include fillvalue\n",
+    "gds.latitude.encoding['fillvalue'] = fillvalue\n",
+    "\n",
+    "# ...and for longitude\n",
+    "encoding = gds.longitude.encoding\n",
+    "gds[\"longitude\"] = gds.longitude.where(gds.longitude != fillvalue)\n",
+    "gds.longitude.encoding = encoding\n",
+    "gds.longitude.encoding[\"fillvalue\"] = fillvalue\n",
+    "                                       "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "511b2d12-c6ce-4fc4-b924-d5b3e4b13cf9",
+   "metadata": {},
+   "source": [
+    "Now when we check the range of the latitude values, we see that the range is between -83.63... and 83.63... as we expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ec868a8e-bf13-4967-bde6-02cc2b07738e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Latitude:  Min = -83.63197326660156, Max = 83.63197326660156\n",
+      "Longitude: Min = -179.8132781982422, Max = 179.8132781982422\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Latitude:  Min = {gds.latitude.min().values}, Max = {gds.latitude.max().values}\")\n",
+    "print(f\"Longitude: Min = {gds.longitude.min().values}, Max = {gds.longitude.max().values}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5acf9bca-d60d-4d34-9618-7d33e7236772",
+   "metadata": {},
+   "source": [
+    "Plotting the data again, we see that latitude values are within the expected range but that hasn't solved the missing data problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c7d6df08-b03f-465a-bb21-d6a68665fde2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x32763fac0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 672x480 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 622
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(3, 1)\n",
+    "gds.latitude.plot(ax=ax[0]);\n",
+    "gds.longitude.plot(ax=ax[1]);\n",
+    "gds.soil_moisture.plot(ax=ax[2])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "efc6edf9-0555-4256-86c7-c6527c9746c7",
+   "metadata": {},
+   "source": [
+    "#### Adding x and y coordinates\n",
+    "\n",
+    "Missing values for latitude and longitude can cause problems for plotting and other operations.  As noted above, the files do not include complete information about the coordinate reference system or the grid definition.  So we need to do some data sleuthing.\n",
+    "\n",
+    "Tables 4 and 5 in the [User Guide](https://nsidc.org/sites/default/files/spl3smp-v008-userguide.pdf) provide geospatial information.  Further information on EASE Grids can be found on [A Guide to EASE Grids page](https://nsidc.org/data/user-resources/help-center/guide-ease-grids).\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Note:</b> The ATBD for the L3 SMP data gives the data array dimensions as 406 x 963.\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d642bf9-c8fb-431d-8467-7720a3378081",
+   "metadata": {},
+   "source": [
+    "The CRS and grid definition are independent.  The CRS defines how projected coordinates, usually in meters, relate to geographic latitude and longitude.  The grid definition relates how image coordinates (columns and rows) map to the projected coordinates.  The Guide to EASE Grids has 8 grid definitions, ranging from 1 km to 36 km cell sizes, for the Global EASE-Grid 2.0 CRS (EPSG:9633).  We could define many more grid definitions.  For more information on CRS, map projections and grids see [_Link to be added_]().\n",
+    "\n",
+    "In the [table](https://nsidc.org/data/user-resources/help-center/guide-ease-grids#anchor-10) for the 36 km Global EASE-Grid 2.0 we are given the height and width of each grid cell, and the x and y coordinates of the upper-left corner of the upper-left grid cell.  We can see that the 36 km grid cell size is not exact.  In fact the value the table gives is not exact.  The actual value runs to 9 decimal places.  This is because the grid cell height and width of the global grid were defined so that an exact number of cells span the equator {@brodzik_ease}.  This is not necessary for polar grids.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Tip:</b> Exact definitions of CRS, projection and grid parameters can be found in the Grid Parameter Definition (gpd) files in the [`mapxmaps` GitHub repository](https://github.com/nsidc/mapxmaps).  These gpd files have a somewhat esoteric format, so might take a little bit to understand.  It is important that the grid definition is exact because we cannot merge datasets if the coordinates do not match, even to the 9th decimal place.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "78dc5886-d10a-4ca7-b2df-e10ac1de0c8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_cell_width = 36032.220840584\n",
+    "grid_cell_height = -36032.220840584\n",
+    "x_upper_left_corner = -17367530.4451615\n",
+    "y_upper_left_corner = 7314540.8306386"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "927e8b99-28d0-46d0-9c0e-8f9fd6223861",
+   "metadata": {},
+   "source": [
+    "The projected coordinates of a grid cell can be found using the following formulas:\n",
+    "\n",
+    "$$\n",
+    "x = (grid\\_cell\\_width \\times row) + x\\_upper\\_left\\_corner\n",
+    "$$\n",
+    "$$\n",
+    "y = (grid\\_cell\\_height \\times column) + y\\_upper\\_left\\_corner)\n",
+    "$$\n",
+    "\n",
+    "This equations assumes that the origin of the image coordinates start at row=0. and column=0. in the upper-left corner of the upper-left grid cell.  So the center of the upper-left grid cell is row=0.5 and column=0.5.  Adding 0.5 to row and column indices solves this problem.\n",
+    "\n",
+    "We could write a function to return (x, y) coordinates for (column, row) image coordinates using the equations above.  Or we can use the `Affine` class from the `affine` package to define an affine matrix.  This is what we'll do below.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "feed97dc-b521-4240-9446-8811c8694aeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Affine(36032.220840584, 0.0, -17367530.4451615,\n",
+       "       0.0, -36032.220840584, 7314540.8306386)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transform = Affine(grid_cell_width, 0.0, x_upper_left_corner, \n",
+    "                   0.0, grid_cell_height, y_upper_left_corner)\n",
+    "transform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08669cb5-4848-41e7-a08b-0b3e71d21f40",
+   "metadata": {},
+   "source": [
+    "This then allows us to calculate x and y coordinates from column and row indices using matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "98bd706e-78fc-4cc5-a11a-ff0cb22ca93c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17367530.445161477, -7314540.830638505)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transform * (964, 406)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "549fc024-b032-406a-acdc-f7a76dea9e49",
+   "metadata": {},
+   "source": [
+    "We'll define vectors for the row and column indices of the grid cell centers, and then use the `transform` matrix to get x and y coordinates.  These only need to be vectors as well because each column will have the same row indices and each row will have the same column indices. We'll use the `numpy.arange` function to do and start the range at 0.5 to offset the row and column indices for grid cell centers by 0.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4a314543-1a0c-482d-86d3-5905b6b11b2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/3n/jlwg76x96d92k9ttmpcq8gt00000gs/T/ipykernel_60006/13705710.py:1: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  nrows, ncolumns, nclass = gds.dims['y'], gds.dims['x'], gds.dims['igbp_class']  #data_vars['soil_moisture'][1].shape\n"
+     ]
+    }
+   ],
+   "source": [
+    "nrows, ncolumns, nclass = gds.dims['y'], gds.dims['x'], gds.dims['igbp_class']  #data_vars['soil_moisture'][1].shape\n",
+    "row = np.arange(0.5, nrows)\n",
+    "column = np.arange(0.5, ncolumns)\n",
+    "\n",
+    "x, _ = transform * (column, 0.5)\n",
+    "_, y = transform * (0.5, row)\n",
+    "igbp_class = np.arange(nclass)  # Add attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85c7ee2c-05e7-4f00-a30c-e8697c91499e",
+   "metadata": {},
+   "source": [
+    "We need to assign `x`, `y`, and `igbp_class` as coordinates.  This should be sufficient for plotting.  However, if we want to reproject the data we should define the CRS and spatial coordinates, and transform.  We do this using `rioxarray` accessors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4fda308a-6433-4174-b14e-b162a71ae729",
+   "metadata": {},
+   "outputs": [
+    {
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+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
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+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
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+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 99MB\n",
+       "Dimensions:                      (y: 406, x: 964, igbp_class: 3)\n",
+       "Coordinates:\n",
+       "  * x                            (x) float64 8kB -1.735e+07 ... 1.735e+07\n",
+       "  * y                            (y) float64 3kB 7.297e+06 ... -7.297e+06\n",
+       "  * igbp_class                   (igbp_class) int64 24B 0 1 2\n",
+       "    wgs84_easegrid2_global       int64 8B 0\n",
+       "Data variables: (12/53)\n",
+       "    EASE_column_index            (y, x) float32 2MB ...\n",
+       "    EASE_row_index               (y, x) float32 2MB ...\n",
+       "    albedo                       (y, x) float32 2MB ...\n",
+       "    albedo_dca                   (y, x) float32 2MB ...\n",
+       "    albedo_scah                  (y, x) float32 2MB ...\n",
+       "    albedo_scav                  (y, x) float32 2MB ...\n",
+       "    ...                           ...\n",
+       "    tb_v_uncorrected             (y, x) float32 2MB ...\n",
+       "    vegetation_opacity           (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_dca       (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scah      (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scav      (y, x) float32 2MB ...\n",
+       "    vegetation_water_content     (y, x) float32 2MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a28887a6-1c7d-420c-8a77-0aa46e744bb0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a28887a6-1c7d-420c-8a77-0aa46e744bb0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 406</li><li><span class='xr-has-index'>x</span>: 964</li><li><span class='xr-has-index'>igbp_class</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0c9a1b25-45c4-4137-aed3-03eb901dcdca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c9a1b25-45c4-4137-aed3-03eb901dcdca' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.735e+07 -1.731e+07 ... 1.735e+07</div><input id='attrs-63b1a2c8-0a0a-4332-b555-88fc4a450a0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63b1a2c8-0a0a-4332-b555-88fc4a450a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84befe7a-167e-4f4d-8dc1-88ce1603a54f' class='xr-var-data-in' type='checkbox'><label for='data-84befe7a-167e-4f4d-8dc1-88ce1603a54f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([-17349514.334741, -17313482.113901, -17277449.89306 , ...,\n",
+       "        17277449.89306 ,  17313482.113901,  17349514.334741])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.297e+06 7.26e+06 ... -7.297e+06</div><input id='attrs-0acd44ea-256b-4bb7-945c-9489badf60c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0acd44ea-256b-4bb7-945c-9489badf60c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1ad8f6-f599-453b-b49f-f668c16e2d26' class='xr-var-data-in' type='checkbox'><label for='data-1e1ad8f6-f599-453b-b49f-f668c16e2d26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([ 7296524.720218,  7260492.499378,  7224460.278537, ..., -7224460.278537,\n",
+       "       -7260492.499378, -7296524.720218])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>igbp_class</span></div><div class='xr-var-dims'>(igbp_class)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-93787977-a440-43dc-834e-18b5675defd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93787977-a440-43dc-834e-18b5675defd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78111c2b-eeb7-4f60-8c16-f73d0b7d1571' class='xr-var-data-in' type='checkbox'><label for='data-78111c2b-eeb7-4f60-8c16-f73d0b7d1571' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wgs84_easegrid2_global</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-6a7bc9a3-523f-46f2-936a-9124bebe0faf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a7bc9a3-523f-46f2-936a-9124bebe0faf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c7b6118-2542-4e58-839f-a128d38f2bfb' class='xr-var-data-in' type='checkbox'><label for='data-5c7b6118-2542-4e58-839f-a128d38f2bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / NSIDC EASE-Grid 2.0 Global</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_cylindrical_equal_area</dd><dt><span>standard_parallel :</span></dt><dd>30.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>0.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3622a45a-d0fc-4ad4-95f8-e2161641f45d' class='xr-section-summary-in' type='checkbox'  ><label for='section-3622a45a-d0fc-4ad4-95f8-e2161641f45d' class='xr-section-summary' >Data variables: <span>(53)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>EASE_column_index</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6138fbb-b419-4501-96c1-dc6aee33b2e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6138fbb-b419-4501-96c1-dc6aee33b2e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28074c49-ce4c-4eee-89c9-7feaac3fd9cf' class='xr-var-data-in' type='checkbox'><label for='data-28074c49-ce4c-4eee-89c9-7feaac3fd9cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The column index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>963</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EASE_row_index</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-174a2934-f726-4171-aea6-43452372585a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-174a2934-f726-4171-aea6-43452372585a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07f4421b-2122-434b-954b-c07b9998f12f' class='xr-var-data-in' type='checkbox'><label for='data-07f4421b-2122-434b-954b-c07b9998f12f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The row index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>valid_max :</span></dt><dd>405</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-abebb9ca-7f1a-4559-91ed-59a9f774eff1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abebb9ca-7f1a-4559-91ed-59a9f774eff1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb9f1336-5f03-4937-87cd-d6fcf3735ed0' class='xr-var-data-in' type='checkbox'><label for='data-eb9f1336-5f03-4937-87cd-d6fcf3735ed0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e29541aa-d9b0-4836-9d89-c35cfc55173d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e29541aa-d9b0-4836-9d89-c35cfc55173d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40bc1c36-9614-4c75-be94-b8620ff24b93' class='xr-var-data-in' type='checkbox'><label for='data-40bc1c36-9614-4c75-be94-b8620ff24b93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1db17e68-088d-4788-89cf-f3860c67aba5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1db17e68-088d-4788-89cf-f3860c67aba5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46164f44-2fa2-41fc-8414-9fa5846c8869' class='xr-var-data-in' type='checkbox'><label for='data-46164f44-2fa2-41fc-8414-9fa5846c8869' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-H within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21627ffc-c1b7-4af5-89d8-8720d7f3b265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21627ffc-c1b7-4af5-89d8-8720d7f3b265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-792e273e-e763-4e4d-a8d8-47bbb7072013' class='xr-var-data-in' type='checkbox'><label for='data-792e273e-e763-4e4d-a8d8-47bbb7072013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-V within the grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boresight_incidence</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-177a1f1f-e713-4cd1-801c-db5b9a3bc426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-177a1f1f-e713-4cd1-801c-db5b9a3bc426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56ecf047-6c4f-4f24-8455-295e303fd0bc' class='xr-var-data-in' type='checkbox'><label for='data-56ecf047-6c4f-4f24-8455-295e303fd0bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative angle between the antenna boresight vector and the normal to the Earth&amp;apos;s surface for all footprints within the cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bulk_density</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-350497b9-921b-4430-8d68-b5a0a1186d5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-350497b9-921b-4430-8d68-b5a0a1186d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fc361ca-f86e-4337-8d5a-3511a025fcac' class='xr-var-data-in' type='checkbox'><label for='data-5fc361ca-f86e-4337-8d5a-3511a025fcac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated bulk_density within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>2.65</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96744849-9c07-41c3-b954-560ef6e2a23d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96744849-9c07-41c3-b954-560ef6e2a23d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34c6889d-c6ab-4801-887b-804d2dfc6104' class='xr-var-data-in' type='checkbox'><label for='data-34c6889d-c6ab-4801-887b-804d2dfc6104' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freeze_thaw_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc124a30-3e0d-4e00-82d9-749002b70ec3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc124a30-3e0d-4e00-82d9-749002b70ec3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11c0f4dc-5837-4bce-958a-51d9c18da8b6' class='xr-var-data-in' type='checkbox'><label for='data-11c0f4dc-5837-4bce-958a-51d9c18da8b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fraction of the 36 km grid cell that is denoted as frozen.  Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_surface_status</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-76f6566e-2a4a-4f25-b4b8-077807eb75a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76f6566e-2a4a-4f25-b4b8-077807eb75a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d014214b-17e4-4f2c-9f57-584cdf781d62' class='xr-var-data-in' type='checkbox'><label for='data-d014214b-17e4-4f2c-9f57-584cdf781d62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Indicates if the grid point lies on land (0) or water (1).</dd><dt><span>valid_max :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class</span></div><div class='xr-var-dims'>(y, x, igbp_class)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e41baf0e-ca4b-499a-9c17-8801d7f6a658' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e41baf0e-ca4b-499a-9c17-8801d7f6a658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45b53ede-5285-4f3f-b57d-5cf3cbf48f17' class='xr-var-data-in' type='checkbox'><label for='data-45b53ede-5285-4f3f-b57d-5cf3cbf48f17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class_fraction</span></div><div class='xr-var-dims'>(y, x, igbp_class)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fb7ad57c-8828-446d-abde-fbbaf395ca28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb7ad57c-8828-446d-abde-fbbaf395ca28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a52f0977-1742-4661-ad2d-7321641898e0' class='xr-var-data-in' type='checkbox'><label for='data-a52f0977-1742-4661-ad2d-7321641898e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the grid cell that contains the most common land cover in that area based on the IGBP landcover map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>83.63 83.63 83.63 ... -83.63 -83.63</div><input id='attrs-2db54532-1d66-4d22-8ce8-2cb5717079f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2db54532-1d66-4d22-8ce8-2cb5717079f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb70ba1f-eeb0-4f21-8d88-a63612c72a72' class='xr-var-data-in' type='checkbox'><label for='data-bb70ba1f-eeb0-4f21-8d88-a63612c72a72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[ 83.63197,  83.63197,  83.63197, ...,  83.63197,  83.63197,\n",
+       "         83.63197],\n",
+       "       [ 81.48033,  81.48033,  81.48033, ...,  81.48033,  81.48033,\n",
+       "         81.48033],\n",
+       "       [ 79.76896,  79.76896,  79.76896, ...,  79.76896,  79.76896,\n",
+       "         79.76896],\n",
+       "       ...,\n",
+       "       [-79.76896, -79.76896, -79.76896, ..., -79.76896, -79.76896,\n",
+       "        -79.76896],\n",
+       "       [-81.48033, -81.48033, -81.48033, ..., -81.48033, -81.48033,\n",
+       "        -81.48033],\n",
+       "       [-83.63197, -83.63197, -83.63197, ..., -83.63197, -83.63197,\n",
+       "        -83.63197]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_centroid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78f916e8-0225-47e3-9bab-c1e38de114fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78f916e8-0225-47e3-9bab-c1e38de114fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d197ada-d064-47c6-9ddd-f97edf0fa61a' class='xr-var-data-in' type='checkbox'><label for='data-9d197ada-d064-47c6-9ddd-f97edf0fa61a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the latitude of the center of the  brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.4 ... 179.4 179.8</div><input id='attrs-5b8d55f6-b254-4ddd-9855-0c9027b977e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b8d55f6-b254-4ddd-9855-0c9027b977e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e3e81b-cfcd-4e1a-b6b5-31a62aa2119e' class='xr-var-data-in' type='checkbox'><label for='data-d2e3e81b-cfcd-4e1a-b6b5-31a62aa2119e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[-179.81328, -179.43983, -179.06639, ...,  179.06639,  179.43983,\n",
+       "         179.81328],\n",
+       "       [-179.81328, -179.43983, -179.06639, ...,  179.06639,  179.43983,\n",
+       "         179.81328],\n",
+       "       [-179.81328, -179.43983, -179.06639, ...,  179.06639,  179.43983,\n",
+       "         179.81328],\n",
+       "       ...,\n",
+       "       [-179.81328, -179.43983, -179.06639, ...,  179.06639,  179.43983,\n",
+       "         179.81328],\n",
+       "       [-179.81328, -179.43983, -179.06639, ...,  179.06639,  179.43983,\n",
+       "         179.81328],\n",
+       "       [-179.81328, -179.43983, -179.06639, ...,  179.06639,  179.43983,\n",
+       "         179.81328]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_centroid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-846ab1fd-69de-43be-a49c-71951949ec6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-846ab1fd-69de-43be-a49c-71951949ec6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01a3fa38-84eb-4d77-bd66-08bbd805c2d0' class='xr-var-data-in' type='checkbox'><label for='data-01a3fa38-84eb-4d77-bd66-08bbd805c2d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the longitude of the center of the brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>179.999</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_water_body_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48c3539e-5b5d-4936-9377-276b16f3c497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48c3539e-5b5d-4936-9377-276b16f3c497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac406d23-953e-4b71-a4eb-a75031027368' class='xr-var-data-in' type='checkbox'><label for='data-ac406d23-953e-4b71-a4eb-a75031027368' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f18473b-6d3a-4570-9ddc-2823a0cc47e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f18473b-6d3a-4570-9ddc-2823a0cc47e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc3a303-131a-4f14-8ad5-8818035541b4' class='xr-var-data-in' type='checkbox'><label for='data-3dc3a303-131a-4f14-8ad5-8818035541b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06075f3d-3be4-4705-aa50-6a5afe4d9c64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06075f3d-3be4-4705-aa50-6a5afe4d9c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bce7d7a-92bd-42f6-b888-cfe05fb89925' class='xr-var-data-in' type='checkbox'><label for='data-2bce7d7a-92bd-42f6-b888-cfe05fb89925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b16b574c-2ab0-488e-8721-b157011743a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b16b574c-2ab0-488e-8721-b157011743a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-085a5250-e62b-4fa6-ba48-17262b7be153' class='xr-var-data-in' type='checkbox'><label for='data-085a5250-e62b-4fa6-ba48-17262b7be153' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-H retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3966c612-84ff-4d82-ae53-2fbf79276969' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3966c612-84ff-4d82-ae53-2fbf79276969' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36edea0a-f1a7-427e-8ef1-62b75634610c' class='xr-var-data-in' type='checkbox'><label for='data-36edea0a-f1a7-427e-8ef1-62b75634610c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-V retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2f09c50-63b4-427f-b8b4-9f1d667fc175' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2f09c50-63b4-427f-b8b4-9f1d667fc175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c999595-79a4-4f18-b060-ae9dc5b29b3a' class='xr-var-data-in' type='checkbox'><label for='data-0c999595-79a4-4f18-b060-ae9dc5b29b3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e276a56a-aa1c-4125-a249-d3d9a51cc8a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e276a56a-aa1c-4125-a249-d3d9a51cc8a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29e1e38d-66fc-4e46-831c-25028972b58e' class='xr-var-data-in' type='checkbox'><label for='data-29e1e38d-66fc-4e46-831c-25028972b58e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c70f9fd-212e-4669-91ae-fbf1df579252' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c70f9fd-212e-4669-91ae-fbf1df579252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dc29b7b-eddd-42b5-afda-3cb6ca6b4aad' class='xr-var-data-in' type='checkbox'><label for='data-6dc29b7b-eddd-42b5-afda-3cb6ca6b4aad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-H within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21692e29-d727-4027-8a7b-6032ff5e98da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21692e29-d727-4027-8a7b-6032ff5e98da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67c98bde-859a-4adb-9aaa-d5031c2f4579' class='xr-var-data-in' type='checkbox'><label for='data-67c98bde-859a-4adb-9aaa-d5031c2f4579' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-V within the 36 km grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-713aa9b6-4dc7-4566-9855-a59277156699' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-713aa9b6-4dc7-4566-9855-a59277156699' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d09d7ae-8b0d-4aa8-a467-565debcb6ea6' class='xr-var-data-in' type='checkbox'><label for='data-4d09d7ae-8b0d-4aa8-a467-565debcb6ea6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd470454-200a-4181-915c-6a9ebbb7b606' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd470454-200a-4181-915c-6a9ebbb7b606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2b996b-cb0d-4aac-9730-131eb85a14d0' class='xr-var-data-in' type='checkbox'><label for='data-0f2b996b-cb0d-4aac-9730-131eb85a14d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_error</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f70722e3-76e7-45e5-8cb0-cf90c0fc2efe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f70722e3-76e7-45e5-8cb0-cf90c0fc2efe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a013317-04e2-4876-baa1-ca69c9acd1f7' class='xr-var-data-in' type='checkbox'><label for='data-3a013317-04e2-4876-baa1-ca69c9acd1f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b29bc3e9-e204-4856-b69d-160c6a241ddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b29bc3e9-e204-4856-b69d-160c6a241ddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dee6c95b-ec58-4b87-b8ac-3380b63c4749' class='xr-var-data-in' type='checkbox'><label for='data-dee6c95b-ec58-4b87-b8ac-3380b63c4749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-H soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9be2d1a1-4d52-40d4-80ad-6821461b0c42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9be2d1a1-4d52-40d4-80ad-6821461b0c42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60df0a5b-8ea0-4d4a-9ace-a2d84038a84c' class='xr-var-data-in' type='checkbox'><label for='data-60df0a5b-8ea0-4d4a-9ace-a2d84038a84c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-V soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>static_water_body_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-341a66a6-63d0-4fc7-bbe4-db76f2b9d7f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-341a66a6-63d0-4fc7-bbe4-db76f2b9d7f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c91138c9-4897-4fae-adab-e8211d3e6089' class='xr-var-data-in' type='checkbox'><label for='data-c91138c9-4897-4fae-adab-e8211d3e6089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c828c357-c765-44f1-b778-1db9d95758fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c828c357-c765-44f1-b778-1db9d95758fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a45af1e1-ac81-4d83-bf06-425600efeb5d' class='xr-var-data-in' type='checkbox'><label for='data-a45af1e1-ac81-4d83-bf06-425600efeb5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s</dd><dt><span>flag_meanings :</span></dt><dd>36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radiometer_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region</dd><dt><span>long_name :</span></dt><dd>Bit flags that record ambient surface conditions for the grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_temperature</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db6e0b5e-dc51-4227-8c95-755c1fac3f4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db6e0b5e-dc51-4227-8c95-755c1fac3f4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6d80023-c77f-48e8-8571-56dd7d93fb8c' class='xr-var-data-in' type='checkbox'><label for='data-d6d80023-c77f-48e8-8571-56dd7d93fb8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature at land surface based on GMAO GEOS-5 data.</dd><dt><span>units :</span></dt><dd>Kelvins</dd><dt><span>valid_max :</span></dt><dd>350.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_h</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fb8ad10-7ec5-44de-822c-8c645de768b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fb8ad10-7ec5-44de-822c-8c645de768b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a2e375b-d072-4f67-8876-40f8333379c0' class='xr-var-data-in' type='checkbox'><label for='data-1a2e375b-d072-4f67-8876-40f8333379c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer horizontal polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b984337f-6a2d-43b3-8278-3980416ca0e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b984337f-6a2d-43b3-8278-3980416ca0e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f553d524-c882-4ed0-b830-de084fcf0385' class='xr-var-data-in' type='checkbox'><label for='data-f553d524-c882-4ed0-b830-de084fcf0385' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer vertical polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_3_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01943aef-f03a-4b85-bfcd-1c7e0a2a4d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01943aef-f03a-4b85-bfcd-1c7e0a2a4d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e73ac0a5-73a1-4a9c-ba98-4193ba6fb2d8' class='xr-var-data-in' type='checkbox'><label for='data-e73ac0a5-73a1-4a9c-ba98-4193ba6fb2d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Third stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_4_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ceb50f27-1e1b-47ea-8ecf-cb006797c9b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceb50f27-1e1b-47ea-8ecf-cb006797c9b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc793db-86c3-4a31-b470-4786baa8a9a2' class='xr-var-data-in' type='checkbox'><label for='data-3dc793db-86c3-4a31-b470-4786baa8a9a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fourth stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-72310ecd-d1fe-4b85-a818-ec0a9b31b55f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72310ecd-d1fe-4b85-a818-ec0a9b31b55f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3641969-8782-4db8-9df2-4ae9e88e57f0' class='xr-var-data-in' type='checkbox'><label for='data-c3641969-8782-4db8-9df2-4ae9e88e57f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_uncorrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29b2365c-5496-4b1d-827d-5809c9ef3f8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29b2365c-5496-4b1d-827d-5809c9ef3f8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98e09189-bcf9-4b72-8992-601430cc1857' class='xr-var-data-in' type='checkbox'><label for='data-98e09189-bcf9-4b72-8992-601430cc1857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_3</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0daecde1-6842-4086-92b9-765d4830d020' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0daecde1-6842-4086-92b9-765d4830d020' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77b789f0-ee7b-4e7d-a999-8180f46fd9e3' class='xr-var-data-in' type='checkbox'><label for='data-77b789f0-ee7b-4e7d-a999-8180f46fd9e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>3rd_Stokes_quality 3rd_Stokes_range 3rd_Stokes_RFI_detection 3rd_Stokes_RFI_correction 3rd_Stokes_NEDT 3rd_Stokes_direct_sun_correction 3rd_Stokes_reflected_sun_correction 3rd_Stokes_reflected_moon_correction 3rd_Stokes_direct_galaxy_correction 3rd_Stokes_reflected_galaxy_correction 3rd_Stokes_atmosphere_correction 3rd_Stokes_null_value_bit 3rd_Stokes_RFI_check 3rd_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 3rd Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_4</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af5509ff-5691-47e4-b6f7-fff6e2f30434' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af5509ff-5691-47e4-b6f7-fff6e2f30434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-813ce095-9f7e-413e-8d3f-f53ebd1adf1d' class='xr-var-data-in' type='checkbox'><label for='data-813ce095-9f7e-413e-8d3f-f53ebd1adf1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>4th_Stokes_quality 4th_Stokes_range 4th_Stokes_RFI_detection 4th_Stokes_RFI_correction 4th_Stokes_NEDT 4th_Stokes_direct_sun_correction 4th_Stokes_reflected_sun_correction 4th_Stokes_reflected_moon_correction 4th_Stokes_direct_galaxy_correction 4th_Stokes_reflected_galaxy_correction 4th_Stokes_atmosphere_correction 4th_Stokes_null_value_bit 4th_Stokes_RFI_check 4th_Stokes_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 4th Stokes brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_h</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a2f8d0d-c5fa-4a72-9fd4-cf251a84a0a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a2f8d0d-c5fa-4a72-9fd4-cf251a84a0a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd377018-2c42-4733-9511-e24b79da2537' class='xr-var-data-in' type='checkbox'><label for='data-dd377018-2c42-4733-9511-e24b79da2537' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Horizontal_polarization_quality Horizontal_polarization_range Horizontal_polarization_RFI_detection Horizontal_polarization_RFI_correction Horizontal_polarization_NEDT Horizontal_polarization_direct_sun_correction Horizontal_polarization_reflected_sun_correction Horizontal_polarization_reflected_moon_correction Horizontal_polarization_direct_galaxy_correction Horizontal_polarization_reflected_galaxy_correction Horizontal_polarization_atmosphere_correction Horizontal_polarization_Faraday_rotation_correction Horizontal_polarization_null_value_bit Horizontal_polarization_water_correction Horizontal_polarization_RFI_check Horizontal_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the horizontal polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ebe95e36-36da-4a91-9773-f3f7ce8c94db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebe95e36-36da-4a91-9773-f3f7ce8c94db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11c8966-1005-4c53-a026-1409776bc34b' class='xr-var-data-in' type='checkbox'><label for='data-e11c8966-1005-4c53-a026-1409776bc34b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Vertical_polarization_quality Vertical_polarization_range Vertical_polarization_RFI_detection Vertical_polarization_RFI_correction Vertical_polarization_NEDT Vertical_polarization_direct_sun_correction Vertical_polarization_reflected_sun_correction Vertical_polarization_reflected_moon_correction Vertical_polarization_direct_galaxy_correction Vertical_polarization_reflected_galaxy_correction Vertical_polarization_atmosphere_correction Vertical_polarization_Faraday_rotation_correction Vertical_polarization_null_value_bit Vertical_polarization_water_correction Vertical_polarization_RFI_check Vertical_polarization_RFI_clean</dd><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the vertical polarization brightness temperature within each grid cell</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_seconds</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5214eb95-f182-4ea1-871a-f7724bdab694' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5214eb95-f182-4ea1-871a-f7724bdab694' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b594d1-8fdf-4a91-ac6f-c3e7c725d885' class='xr-var-data-in' type='checkbox'><label for='data-f1b594d1-8fdf-4a91-ac6f-c3e7c725d885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in seconds since noon on January 1, 2000 UTC.</dd><dt><span>valid_max :</span></dt><dd>940000000.0</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_utc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>|S24</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30ea66b6-e024-4b62-9e5e-d1b3374a8801' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30ea66b6-e024-4b62-9e5e-d1b3374a8801' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ed104d6-8693-48f0-910b-12ce79a8cbc6' class='xr-var-data-in' type='checkbox'><label for='data-5ed104d6-8693-48f0-910b-12ce79a8cbc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in UTC.</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=|S24]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6aa5fc67-d0c6-4c7b-ae91-37b1dc0e80e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6aa5fc67-d0c6-4c7b-ae91-37b1dc0e80e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8a4cb6d-6a7f-4134-b381-dd2bb8300d43' class='xr-var-data-in' type='checkbox'><label for='data-a8a4cb6d-6a7f-4134-b381-dd2bb8300d43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_uncorrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17d26b78-1943-4f89-b414-b69e45effba8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17d26b78-1943-4f89-b414-b69e45effba8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df19b268-5d7b-4084-bc71-47ea64d8293a' class='xr-var-data-in' type='checkbox'><label for='data-df19b268-5d7b-4084-bc71-47ea64d8293a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_max :</span></dt><dd>330.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc4c5f38-b874-4c38-b1c6-53dc4bf7284b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc4c5f38-b874-4c38-b1c6-53dc4bf7284b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c3a07ee-98b0-4967-986c-1d73cdf72745' class='xr-var-data-in' type='checkbox'><label for='data-7c3a07ee-98b0-4967-986c-1d73cdf72745' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f9af964f-3e6a-4564-9b10-5a9fcc5b884d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9af964f-3e6a-4564-9b10-5a9fcc5b884d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01c3f505-bbf9-428f-9bdf-aae238e426cd' class='xr-var-data-in' type='checkbox'><label for='data-01c3f505-bbf9-428f-9bdf-aae238e426cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ef9319e-84e9-4831-b956-b6951d0436e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ef9319e-84e9-4831-b956-b6951d0436e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01478f17-ccb0-4829-9d7c-b1e336479f90' class='xr-var-data-in' type='checkbox'><label for='data-01478f17-ccb0-4829-9d7c-b1e336479f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-H retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eee88763-9285-481c-ae05-082f4f1d3784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eee88763-9285-481c-ae05-082f4f1d3784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8255cb-a075-40c0-b3e0-f56f11bded10' class='xr-var-data-in' type='checkbox'><label for='data-6d8255cb-a075-40c0-b3e0-f56f11bded10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-V retrieval in the grid cell.</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_water_content</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4dcd717-a712-4be8-97de-892605959a2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4dcd717-a712-4be8-97de-892605959a2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee9b1ac-da50-4cac-9327-6c4ce3f1d2bf' class='xr-var-data-in' type='checkbox'><label for='data-9ee9b1ac-da50-4cac-9327-6c4ce3f1d2bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative measure of water in the vegetation within the 36 km grid cell.</dd><dt><span>units :</span></dt><dd>kg/m**2</dd><dt><span>valid_max :</span></dt><dd>20.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84746201-490b-4b15-8dde-75254f346ff7' class='xr-section-summary-in' type='checkbox'  ><label for='section-84746201-490b-4b15-8dde-75254f346ff7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3e249b72-4894-459c-ac31-230df94dc771' class='xr-index-data-in' type='checkbox'/><label for='index-3e249b72-4894-459c-ac31-230df94dc771' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -17349514.33474121, -17313482.113900624,  -17277449.89306004,\n",
+       "       -17241417.672219455,  -17205385.45137887, -17169353.230538286,\n",
+       "         -17133321.0096977, -17097288.788857117, -17061256.568016537,\n",
+       "       -17025224.347175952,\n",
+       "       ...\n",
+       "        17025224.347175926,  17061256.568016514,  17097288.788857095,\n",
+       "        17133321.009697683,  17169353.230538264,  17205385.451378845,\n",
+       "        17241417.672219433,  17277449.893060014,  17313482.113900602,\n",
+       "        17349514.334741183],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=964))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cce94723-8d0a-43e4-96c3-339daaf8a01e' class='xr-index-data-in' type='checkbox'/><label for='index-cce94723-8d0a-43e4-96c3-339daaf8a01e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 7296524.720218307,  7260492.499377724,  7224460.278537139,\n",
+       "        7188428.057696556,  7152395.836855971,  7116363.616015388,\n",
+       "        7080331.395174803,   7044299.17433422,  7008266.953493635,\n",
+       "        6972234.732653052,\n",
+       "       ...\n",
+       "       -6972234.732652956,  -7008266.95349354, -7044299.174334125,\n",
+       "       -7080331.395174707, -7116363.616015292, -7152395.836855876,\n",
+       "       -7188428.057696461, -7224460.278537043, -7260492.499377628,\n",
+       "       -7296524.720218212],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=406))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>igbp_class</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-acd6869a-f6cf-4c90-a2f8-ada70ba7a6ef' class='xr-index-data-in' type='checkbox'/><label for='index-acd6869a-f6cf-4c90-a2f8-ada70ba7a6ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;igbp_class&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ace2271c-ddf2-4a81-bf0e-2d10267938b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ace2271c-ddf2-4a81-bf0e-2d10267938b2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 99MB\n",
+       "Dimensions:                      (y: 406, x: 964, igbp_class: 3)\n",
+       "Coordinates:\n",
+       "  * x                            (x) float64 8kB -1.735e+07 ... 1.735e+07\n",
+       "  * y                            (y) float64 3kB 7.297e+06 ... -7.297e+06\n",
+       "  * igbp_class                   (igbp_class) int64 24B 0 1 2\n",
+       "    wgs84_easegrid2_global       int64 8B 0\n",
+       "Data variables: (12/53)\n",
+       "    EASE_column_index            (y, x) float32 2MB ...\n",
+       "    EASE_row_index               (y, x) float32 2MB ...\n",
+       "    albedo                       (y, x) float32 2MB ...\n",
+       "    albedo_dca                   (y, x) float32 2MB ...\n",
+       "    albedo_scah                  (y, x) float32 2MB ...\n",
+       "    albedo_scav                  (y, x) float32 2MB ...\n",
+       "    ...                           ...\n",
+       "    tb_v_uncorrected             (y, x) float32 2MB ...\n",
+       "    vegetation_opacity           (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_dca       (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scah      (y, x) float32 2MB ...\n",
+       "    vegetation_opacity_scav      (y, x) float32 2MB ...\n",
+       "    vegetation_water_content     (y, x) float32 2MB ..."
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds = gds.assign_coords(x=x, y=y, igbp_class=igbp_class)\n",
+    "\n",
+    "gds.rio.write_crs(6933, grid_mapping_name='wgs84_easegrid2_global', inplace=True)\n",
+    "gds.rio.set_spatial_dims(x_dim='x', y_dim='y', inplace=True)\n",
+    "gds.rio.write_coordinate_system(inplace=True)\n",
+    "gds.rio.write_transform(transform)\n",
+    "gds.rio.write_grid_mapping('wgs84_easegrid2_global', inplace=True)\n",
+    "gds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "29cb574a-7f44-4924-b275-0fce25606569",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wgs84_easegrid2_global\n",
+      "(-17367530.4451615, -7314540.830638505, 17367530.445161473, 7314540.8306386)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gds.latitude.rio.grid_mapping)\n",
+    "print(gds.rio.bounds())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "70f54aaa-a1fc-46a2-826d-737bd71aa386",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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sUWpqqoqKik77XIGBgerVq5fi4uJqTS/XunVrJScnm1PINUa/3KqqKm3btk1r1qzRqlWrtHLlSm3durXBz3umbDabHn30UT3yyCPy8/NzdTimQ4cOacmSJUpJSdHevXvNUVpaelL7h4WFyWazqbCwsNY+VqtVISEhCgkJqZU0DQgIUGVlpTndYWVlpRISEtSjRw/16NFDvXv3lpeXl6Tq3/GlS5dq06ZNSktL0759+3TkyBGVlJSouLhYJSUlMgzDTHCFhobKYrGY64uLi1VZWWlOoehwOBQWFqaoqCgFBASorKxMJSUlysvLU2pq6jGfs5+fX60kh5eXlyIjIxUVFaXIyEiz2jA3N1c5OTny8/PT4MGDNWzYMI0YMUItWrQ4w3+h2goKCpSSkqKCgoI60z3WjDEkJMSc8tKZwA4KCmr0hI3dbteuXbu0fft2s5J0//792r9/v/Ly8sx/R+e/l8ViMZPqAQEBatasmXr16qXevXurd+/eatu2bZNIOjmnNnX+zCorK+Xr6ytfX1/5+fnJZrOZ1a5Wq9Uczpsc/P3960zH6+ScWtb5/8xut6uqqsocFRUVKi0tVUlJidnz8Kqrrjrha0hgYKBatWpVa8TGxpr/N2uOkJCQ0369LCoqqlVRDtfjmnLDq/kz7jfcM5KBPy2aLInfGeBUkQwEADRpR48e1c6dO82Lnnl5eerSpYuGDBlyzA+ZpaWlevvttzVhwgRzWWpqqtq0aWN+P2/ePI0aNarWfi1btlSnTp3Utm1btW3bVueee666d+9uvn4CAJqGDRs26I033tCCBQu0b9++WuvCwsJ07rnn6u6779bgwYMVGxt7xhcsc3NzdeTIER05ckSHDx/W7t27tX37dm3fvl07d+4844qb2NhYtW3bVgEBAeY0jgUFBWrdurV69Oihnj17qkePHurUqZN54fhs5Lxg7awGKiwsVHp6un7++WelpKRo8+bNJ/2z6tSpkwYNGqR+/fqprKzMrI47dOiQ8vLyak17eTrTN7Zr106vv/66S6fZMwxDn3zyiaZMmaLNmzef1D6+vr668MILNWjQIJ1zzjlq27atWrVqpaCgIHObqqoqFRUVydvbW4GBgW6VtHE4HDp06JD279+vkJAQRUdHKyIiotGT/E2ZYRhu9TvTlGRnZ+vRRx/VrFmz6mXaVmd/RmfSOiIiQpGRkYqMjFRERIR8fHxqJTl3796tpUuXaseOHfLz81O7du2UnJysjh07qlu3burRo4datWrF748LcE254ZEMBOBEMhAA0CQtWrRIr7/+ur766qtj9v2LjIzUnXfeqSeeeEI2m03l5eV65JFH9O6779aa9uyiiy7SwoUL63xwfP/99/WXv/xFGRkZJ4wjISFBo0aN0sSJE5WUlFQ/Tw4AcNYoKCjQ1q1btWXLFn3++edasGDBSe/r6+urhIQEBQYGqrS01ByhoaHq37+/Bg4cqJ49eyo5OdmsFHM4HMrMzNSGDRv0+OOPa82aNWcUv8ViUcuWLZWUlKSkpCS1a9dObdu2Vbt27ZSUlNRkprd2OBzav3+/tmzZogMHDqi8vFxlZWUqLS1VQECA4uPjlZCQoKSkpJO+0ccwDJWWlpr96oqKisw+Z8XFxbWqtQ4cOKDnnnvO7Gnn5eWllJQUde/evSGerhlfeXm58vPzzcrRo0ePatu2bXr//fePWU3p7e2t+Ph4tWnTRgkJCYqPjzd7Jvbs2bNW4g9A05Senq5Nmzbp4MGD2r9/v1nt6fx6slXF9S0sLMz825WQkKDw8HCzL6hzOKeWDQoKUnBwsGJjY/m7doa4ptzwSAYCcCIZCABocj766CNdf/31J7Vthw4d9Nhjj2ndunV6+eWXzeU+Pj667bbbNGXKFIWEhBxz3+LiYi1cuFALFizQDz/8oNTU1OP27rBarXr88cf1+OOPn9WVEwDcg8Ph0ObNm7VkyRJlZWWpbdu26tChg6Kjo81eRnl5efL29tagQYNO+v17QUGB2RvtwIEDstvtZq8jm80mHx8fdezYUZ07d5ZhGDp06JA2bNig7OxsFRUVmRVXZWVltaYUq6qqksVikZeXl3kXv7+/f62LboGBgfL19ZXNZpOvr68CAgLUrVu34/4NbiiGYejo0aNm37zU1FQdOXJEmZmZyszM1NGjR1VUVKSioiIVFxcf9+9+bGysLr74YsXGxuro0aM6evSoDh8+rB9//FGn+hHNarUqISFBVVVVOnTo0DFvcvmtuLg4tW/fXq1bt1ZERIQiIiIUHh6uqKgoRUdHmyMyMvKk+lSh/mVlZWn16tVau3at9u7dq5SUFG3bts1c/80332jEiBGndEyHw6Gvv/5ac+bMUWZmpnJyclRcXFxnir/CwkIVFBT8buWil5eXLrvsMg0fPlxDhgxRYmIi72MAnDbDMJSTk6P9+/crMzPTnELXOZzV5zX7M+bl5SknJ+d3k4jNmjXT+eefr8LCQu3cuVPp6emn/Hr7Wz4+Prr00ks1evRotWzZUs2bN1fz5s0VERHBVKQniWvKDY9kIAAnkoEAgCbnoYce0gsvvCCpugLwgQceUFxcnKKjo+Xn56evv/5ab7/9tnn3vVQ9FU1JSYkk6bzzztOcOXMUHx9/Sud1OBw6ePCgduzYoQ0bNmjdunVaunSpDh8+bG7TrVs33XPPPRo9erRiYmLO/MkC8CjFxcU6dOiQDh06pKNHj9aacjA7O9vso5SWllbrb9iJeHl5qX///rrkkks0YsQIdenSRZKUn5+v7OxsHTp0SDNnztR///tfZWVlndQxzzvvPGVkZOjIkSOn+1RPis1m04UXXqgBAwaoZ8+eiomJqdX/Lj8/v07S8bcffwzDkMPhqDWcPcCcP9/fTh/52/5ap6Jfv3567LHHNHLkyGNOR7Znzx598cUX2rNnj9l7raKiQv7+/ubYt2+fDhw4cFLnGzFihHr06KHmzZurWbNmatOmjdq3b9/oSVScvIULF+q+++7Tzp07j7tNYmKitm3bJpvNdlLHtNvteu+99/Tss88qNTX1jGP08/PT2LFj9fDDD5t/MwDAlUpLS3X06FHl5uaqqqqq1ut6eHi42rVrV+t1t6SkRFu2bNG6deu0du1abd++3bzZqT6ShJ07d1avXr3Us2dPxcXFmb03w8LC5OfnV+e9h/PGKueNTxaLxVznfK/i7PXoSbim3PBqJQMvekI+Pm6eDKws0U/fPimJ3xngVJEMBAB4nLy8PB04cEBZWVnKyclRVVWVvLy85O3tLS8vLz355JNau3atJGn79u1q3759nWMcPHhQDz/8sD755BM5HI466wcNGqS///3vGjp06Bn1lqisrNQHH3ygiRMnqqioyFzu7e2tu+66S1OnTlVAgHu/WQcaUmVlpXlnt7MyzWazKTAw0Jw2sSa73a4ffvhB//3vf7Vr165a0z9FREToxhtvVMeOHU94PmdyyDmVn3Nav/LycvP8ztGhQwdFRkbWOkZhYaEyMjLMajBnUs9ZiXOsr/n5+crMzKw1TXFDCQwMVFlZ2RklvJoii8WiqKgoRUVFKTg4WIGBgQoMDFRoaKjat2+vTp06qXPnzmrbtu0Z9yQyDEPp6en64Ycf9Msvv2jbtm1mH6QWLVqYY8iQIRowYEA9PUM0luHDh+vbb7897vqxY8fqscceO+EUoYWFhfrhhx+0fPlyrV69WmvWrKnTp8vLy0sREREKDAw0K3y9vb3l7e2t4OBghYSEmMNZPRoZGamoqCidf/75CgsLq6+nDABnjYqKCh08eFCFhYXme7zS0lIVFxeblf9FRUUqKCjQ4sWLzc+VjcXLy0u+vr7y8/OTn59frb/VISEhioqKUmxsrOLi4hQbG6tmzZqZVf9BQUFnXV9Erik3PJKBAJxIBgLAMRw4cECpqanav3+/7Ha7AgMDzSnKgoKC5OfnV+tir/Pir7+/P9OBNLLy8nK9++67+vnnn7Vjxw7t2LFD2dnZtSr5jichIUG7d+8+4d2Vu3fv1sMPP6zPP//8mOsHDx6sV199VZ06dTqj55Gamqr7779fc+fOrbX8zjvv1Ouvv35GxwY8SVVVlebPn69PPvlEmzZt0q5du445JaLValWfPn10wQUXKDAwUN7e3nI4HPrrX//6u+eIiYmRr6+v+bfd4XCY01GVlZWdUrw2m02jRo2S1WrV3r17lZqaqpycnFM6xqnw8vJSixYt1KpVK3Xt2lUXXXSRkpKStHv3bm3btk35+fnmnemhoaHKyMjQ/PnztXz58t+dDrBVq1YaPHhwrT5gNptNlZWVqqysNKcW/OSTT7Ry5UolJCSoZ8+e6tmzp1q1aqXg4GCzx46fn1+txIPz77DdbpfD4ZDdbldZWZkKCwtrXXirqKgwx5EjR/TVV19p1apVDfbzDAgIUGhoqEJDQ2vFHxUVVatvXosWLRQZGelxd+vDNd577z3dfPPNx1w3evRozZkz55jrHA6H1q5dqzfffFPvvffeMaeo9fHx0U033aQ//elP6tKlC+9bAeAMbd++Xdu2bdORI0d0+PBhHTlyRPv27dPatWt19OhRV4dXi6+vr1q1aqXOnTurS5cuuvrqq9WhQweXxsQ15YZHMhCAE8lAADiGhrxbrnv37po0aZLGjx/PBZh68Oqrr+ree+89rX3fe+89XXDBBXI4HMrNzVVOTo6OHj2qnJwcFRQUmBefCwsL9dZbb5n7xcfHa9++feb30dHR2rFjR728/h06dEizZ8/WfffdZy5bvHixhg0bdsbHBtxNeXm50tPTtWfPHq1bt05r1qzRDz/8oMzMzHo5vjORVQ9vhxuEl5eXQkJCFBwcrODgYEVHR6tFixaKi4tTXFycoqKiFB4ebk45FR4ermbNmp1WQqqwsFBLly7VwoULtWXLFoWGhppVbpGRkeratauGDh16Via78vPztX79eq1Zs0bFxcWKjIw0q5hCQ0Nls9lqJR2P9drr7FNYs1+hc1+gsR09elRRUVHHXDd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+      "text/plain": [
+       "<Figure size 1152x672 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 536,
+       "width": 897
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EASEGrid2 = ccrs.epsg(gds.rio.crs.to_epsg())\n",
+    "\n",
+    "fig = plt.figure(figsize=(12,7))\n",
+    "ax = fig.add_subplot(projection=EASEGrid2)\n",
+    "\n",
+    "ax.coastlines()\n",
+    "gds.soil_moisture.plot(ax=ax,)\n",
+    "ax.set_title('Soil Moisture');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae67f0aa-b858-4ee1-aa29-3961df9c54cc",
+   "metadata": {},
+   "source": [
+    "## Extra Stuff to be refined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1d00eaa2-7b6e-477a-b71c-6d90d09aa0a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'soil_moisture': (['y', 'x'],\n",
+       "  array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "         [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "         [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "         ...,\n",
+       "         [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "         [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "         [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
+       "        dtype=float32),\n",
+       "  {'_FillValue': np.float32(-9999.0),\n",
+       "   'coordinates': '/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude',\n",
+       "   'long_name': 'Representative DCA soil moisture measurement for the Earth based grid cell.',\n",
+       "   'units': 'cm**3/cm**3',\n",
+       "   'valid_max': np.float32(0.5),\n",
+       "   'valid_min': np.float32(0.02),\n",
+       "   'grid_mapping': 'WGS84_NSIDC_EASEGrid_2.0_Global'}),\n",
+       " 'retrieval_qual_flag': (['y', 'x'],\n",
+       "  array([[15, 15, 15, ..., 15, 15, 15],\n",
+       "         [15, 15, 15, ..., 15, 15, 15],\n",
+       "         [15, 15, 15, ..., 15, 15, 15],\n",
+       "         ...,\n",
+       "         [15, 15, 15, ..., 15, 15, 15],\n",
+       "         [15, 15, 15, ..., 15, 15, 15],\n",
+       "         [15, 15, 15, ..., 15, 15, 15]], dtype=uint16),\n",
+       "  {'_FillValue': np.uint16(65534),\n",
+       "   'coordinates': '/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude',\n",
+       "   'flag_masks': '1s, 2s, 4s, 8s',\n",
+       "   'flag_meanings': 'Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success',\n",
+       "   'long_name': 'Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.',\n",
+       "   'grid_mapping': 'WGS84_NSIDC_EASEGrid_2.0_Global'})}"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_mapping_name = \"WGS84_NSIDC_EASEGrid_2.0_Global\"\n",
+    "datasets = ['Soil_Moisture_Retrieval_Data_AM/soil_moisture', 'Soil_Moisture_Retrieval_Data_AM/retrieval_qual_flag']\n",
+    "data_vars = {}\n",
+    "dims = ['y', 'x']\n",
+    "with h5py.File(smap_files[0]) as root:\n",
+    "    for dataset in datasets:\n",
+    "        ds = root[dataset]\n",
+    "        path = ds.name\n",
+    "        name = path.split('/')[-1]\n",
+    "        attrs = {k: (v.decode('UTF-8') if hasattr(v, 'decode') else v) for k, v in dict(ds.attrs).items()}\n",
+    "        attrs['grid_mapping'] = grid_mapping_name  # Add so that dataset is CF-compliant and geoferenced\n",
+    "        data_vars[name] = (dims, ds[:], attrs)\n",
+    "\n",
+    "data_vars\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "39afd220-8f13-4993-a25a-5ec0513476be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x32689b820>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 672x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 265,
+       "width": 574
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(data_vars['soil_moisture'][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1d7878b4-eec8-4214-8479-df2a8aab1016",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(406, 964)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_vars['soil_moisture'][1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5329fb83-8b95-42db-8ac1-7efe555a0c4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x327b49390>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 672x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 442,
+       "width": 569
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolormesh(x, y, data_vars['soil_moisture'][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "083d7efa-0fb3-418b-93b6-04132a0375fe",
+   "metadata": {},
+   "source": [
+    "We also need to define the CRS.  We know that the CRS has the EPSG code 6933.  We can use `pyproj` to get the CRS definition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcbd0150-c72c-453b-8045-489f702b8349",
+   "metadata": {},
+   "source": [
+    "Base on [CF-Conventions](http://cfconventions.org/cf-conventions/cf-conventions.html#grid-mappings-and-projections), we add the CRS as a variable with the same name as we used for the `grid_mapping` attribute of each data variable.  We define this variable as an integer.  The grid mapping information is then copied as attributes for that variable.  We use the `to_cf()` method of the `pyproj.CRS` object to do this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "037dc012-8f52-4144-b929-fb3169dcebeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  margin-bottom: 0;\n",
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+       ".xr-section-inline-details {\n",
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+       "  padding-bottom: 4px;\n",
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+       "  grid-column: 2 / -1;\n",
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+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
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+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       "\n",
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+       "  list-style: none;\n",
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+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:              (y: 406, x: 964)\n",
+       "Coordinates:\n",
+       "  * x                    (x) float64 8kB -1.735e+07 -1.731e+07 ... 1.735e+07\n",
+       "  * y                    (y) float64 3kB 7.297e+06 7.26e+06 ... -7.297e+06\n",
+       "Data variables:\n",
+       "    soil_moisture        (y, x) float32 2MB -9.999e+03 -9.999e+03 ... -9.999e+03\n",
+       "    retrieval_qual_flag  (y, x) uint16 783kB 15 15 15 15 15 ... 15 15 15 15 15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a72efd1d-0270-465f-8f4d-6c35a7d73110' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a72efd1d-0270-465f-8f4d-6c35a7d73110' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 406</li><li><span class='xr-has-index'>x</span>: 964</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9737d801-445f-4238-b4c8-0a88ad83c03b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9737d801-445f-4238-b4c8-0a88ad83c03b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.735e+07 -1.731e+07 ... 1.735e+07</div><input id='attrs-6e78b8f5-9719-40ea-b414-da88aa428684' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e78b8f5-9719-40ea-b414-da88aa428684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68118d60-15f8-4275-a89a-ee8e492c86ac' class='xr-var-data-in' type='checkbox'><label for='data-68118d60-15f8-4275-a89a-ee8e492c86ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-17349514.334741, -17313482.113901, -17277449.89306 , ...,\n",
+       "        17277449.89306 ,  17313482.113901,  17349514.334741])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.297e+06 7.26e+06 ... -7.297e+06</div><input id='attrs-0bd2330e-de82-4e38-8665-c68308cb6462' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bd2330e-de82-4e38-8665-c68308cb6462' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99aee70c-3a41-434a-a32d-29df2b4c1b29' class='xr-var-data-in' type='checkbox'><label for='data-99aee70c-3a41-434a-a32d-29df2b4c1b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 7296524.720218,  7260492.499378,  7224460.278537, ..., -7224460.278537,\n",
+       "       -7260492.499378, -7296524.720218])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffa962f0-f527-415b-ad92-994fbbd1f471' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffa962f0-f527-415b-ad92-994fbbd1f471' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-7cf3cd10-528d-459d-829e-1c1793b1b98b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cf3cd10-528d-459d-829e-1c1793b1b98b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc2f42f2-c85e-4db7-9107-693460ecc134' class='xr-var-data-in' type='checkbox'><label for='data-cc2f42f2-c85e-4db7-9107-693460ecc134' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd><dt><span>grid_mapping :</span></dt><dd>WGS84_NSIDC_EASEGrid_2.0_Global</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       ...,\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>15 15 15 15 15 ... 15 15 15 15 15</div><input id='attrs-707616e4-8f86-4756-b887-9b0f724616e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-707616e4-8f86-4756-b887-9b0f724616e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a99bb02-177c-4831-845a-6af35d22fa05' class='xr-var-data-in' type='checkbox'><label for='data-1a99bb02-177c-4831-845a-6af35d22fa05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>65534</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd><dt><span>grid_mapping :</span></dt><dd>WGS84_NSIDC_EASEGrid_2.0_Global</dd></dl></div><div class='xr-var-data'><pre>array([[15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       ...,\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15]], dtype=uint16)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-745e337c-d7bc-4c97-be2d-b71a934fc59e' class='xr-section-summary-in' type='checkbox'  ><label for='section-745e337c-d7bc-4c97-be2d-b71a934fc59e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d83d84eb-01dc-4b53-9161-a5cfed999e5d' class='xr-index-data-in' type='checkbox'/><label for='index-d83d84eb-01dc-4b53-9161-a5cfed999e5d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -17349514.33474121, -17313482.113900624,  -17277449.89306004,\n",
+       "       -17241417.672219455,  -17205385.45137887, -17169353.230538286,\n",
+       "         -17133321.0096977, -17097288.788857117, -17061256.568016537,\n",
+       "       -17025224.347175952,\n",
+       "       ...\n",
+       "        17025224.347175926,  17061256.568016514,  17097288.788857095,\n",
+       "        17133321.009697683,  17169353.230538264,  17205385.451378845,\n",
+       "        17241417.672219433,  17277449.893060014,  17313482.113900602,\n",
+       "        17349514.334741183],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=964))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d9c4342c-4146-4b47-8c6d-23644f312393' class='xr-index-data-in' type='checkbox'/><label for='index-d9c4342c-4146-4b47-8c6d-23644f312393' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 7296524.720218307,  7260492.499377724,  7224460.278537139,\n",
+       "        7188428.057696556,  7152395.836855971,  7116363.616015388,\n",
+       "        7080331.395174803,   7044299.17433422,  7008266.953493635,\n",
+       "        6972234.732653052,\n",
+       "       ...\n",
+       "       -6972234.732652956,  -7008266.95349354, -7044299.174334125,\n",
+       "       -7080331.395174707, -7116363.616015292, -7152395.836855876,\n",
+       "       -7188428.057696461, -7224460.278537043, -7260492.499377628,\n",
+       "       -7296524.720218212],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=406))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20857789-851f-4cf2-a1c2-914cf75dcf76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-20857789-851f-4cf2-a1c2-914cf75dcf76' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "(17367530.445161477, -7314540.830638505)"
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:              (y: 406, x: 964)\n",
+       "Coordinates:\n",
+       "  * x                    (x) float64 8kB -1.735e+07 -1.731e+07 ... 1.735e+07\n",
+       "  * y                    (y) float64 3kB 7.297e+06 7.26e+06 ... -7.297e+06\n",
+       "Data variables:\n",
+       "    soil_moisture        (y, x) float32 2MB -9.999e+03 -9.999e+03 ... -9.999e+03\n",
+       "    retrieval_qual_flag  (y, x) uint16 783kB 15 15 15 15 15 ... 15 15 15 15 15"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "transform * (964, 406)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "549fc024-b032-406a-acdc-f7a76dea9e49",
-   "metadata": {},
-   "source": [
-    "We'll define vectors for the row and column indices of the grid cell centers, and then use the `transform` matrix to get x and y coordinates.  These only need to be vectors as well because each column will have the same row indices and each row will have the same column indices. We'll use the `numpy.arange` function to do and start the range at 0.5 to offset the row and column indices for grid cell centers by 0.5."
+    "ds = xr.Dataset(data_vars, coords={'x': x, 'y': y})\n",
+    "ds"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "4a314543-1a0c-482d-86d3-5905b6b11b2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nrows, ncolumns, nclass = gds.dims['y'], gds.dims['x'], gds.dims['igbp_class']  #data_vars['soil_moisture'][1].shape\n",
-    "row = np.arange(0.5, nrows)\n",
-    "column = np.arange(0.5, ncolumns)\n",
-    "\n",
-    "x, _ = transform * (column, 0.5)\n",
-    "_, y = transform * (0.5, row)\n",
-    "igbp_class = np.arange(nclass)  # Add attributes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "85c7ee2c-05e7-4f00-a30c-e8697c91499e",
-   "metadata": {},
-   "source": [
-    "We need to assign `x`, `y`, and `igbp_class` as coordinates.  This should be sufficient for plotting.  However, if we want to reproject the data we should define the CRS and spatial coordinates, and transform.  We do this using `rioxarray` accessors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "4fda308a-6433-4174-b14e-b162a71ae729",
+   "id": "667dbefd-1a3e-4b9f-94a7-dfc0cb314961",
    "metadata": {},
    "outputs": [
     {
@@ -1235,6 +2825,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -1285,7 +2876,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1293,7 +2884,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1305,6 +2897,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1567,41 +3163,30 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:                      (y: 406, x: 964, igbp_class: 3)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:                          (y: 406, x: 964)\n",
        "Coordinates:\n",
-       "  * x                            (x) float64 -1.735e+07 -1.731e+07 ... 1.735e+07\n",
-       "  * y                            (y) float64 7.297e+06 7.26e+06 ... -7.297e+06\n",
-       "  * igbp_class                   (igbp_class) int64 0 1 2\n",
-       "    wgs84_easegrid2_global       int64 0\n",
-       "Data variables: (12/53)\n",
-       "    EASE_column_index            (y, x) float32 ...\n",
-       "    EASE_row_index               (y, x) float32 ...\n",
-       "    albedo                       (y, x) float32 ...\n",
-       "    albedo_dca                   (y, x) float32 ...\n",
-       "    albedo_scah                  (y, x) float32 ...\n",
-       "    albedo_scav                  (y, x) float32 ...\n",
-       "    ...                           ...\n",
-       "    tb_v_uncorrected             (y, x) float32 ...\n",
-       "    vegetation_opacity           (y, x) float32 ...\n",
-       "    vegetation_opacity_dca       (y, x) float32 ...\n",
-       "    vegetation_opacity_scah      (y, x) float32 ...\n",
-       "    vegetation_opacity_scav      (y, x) float32 ...\n",
-       "    vegetation_water_content     (y, x) float32 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f50525ec-d198-4562-8cc0-58c9642f15e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f50525ec-d198-4562-8cc0-58c9642f15e6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 406</li><li><span class='xr-has-index'>x</span>: 964</li><li><span class='xr-has-index'>igbp_class</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f0f2d52d-498f-4ac1-8da2-51d4b084b76e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0f2d52d-498f-4ac1-8da2-51d4b084b76e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.735e+07 -1.731e+07 ... 1.735e+07</div><input id='attrs-f537a3b5-5529-4a28-8f4c-caa4ac002e9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f537a3b5-5529-4a28-8f4c-caa4ac002e9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1262209-ba66-47e1-a5dd-aa4e48e26de2' class='xr-var-data-in' type='checkbox'><label for='data-a1262209-ba66-47e1-a5dd-aa4e48e26de2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([-17349514.334741, -17313482.113901, -17277449.89306 , ...,\n",
-       "        17277449.89306 ,  17313482.113901,  17349514.334741])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.297e+06 7.26e+06 ... -7.297e+06</div><input id='attrs-8177573b-53cf-4734-8d67-fa95ee2b337b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8177573b-53cf-4734-8d67-fa95ee2b337b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01d8a576-85a8-4c59-88d0-3b05721be6d3' class='xr-var-data-in' type='checkbox'><label for='data-01d8a576-85a8-4c59-88d0-3b05721be6d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([ 7296524.720218,  7260492.499378,  7224460.278537, ..., -7224460.278537,\n",
-       "       -7260492.499378, -7296524.720218])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>igbp_class</span></div><div class='xr-var-dims'>(igbp_class)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-b2bc9e46-4830-4903-9279-87fff6b5f5a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2bc9e46-4830-4903-9279-87fff6b5f5a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d603cd27-99fc-4921-a742-307e7e3add5a' class='xr-var-data-in' type='checkbox'><label for='data-d603cd27-99fc-4921-a742-307e7e3add5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wgs84_easegrid2_global</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ba629afa-4450-43de-9ff3-05e19b4edd50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba629afa-4450-43de-9ff3-05e19b4edd50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe7e37b7-ebad-4e05-96cd-8c630fcace37' class='xr-var-data-in' type='checkbox'><label for='data-fe7e37b7-ebad-4e05-96cd-8c630fcace37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / NSIDC EASE-Grid 2.0 Global</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_cylindrical_equal_area</dd><dt><span>standard_parallel :</span></dt><dd>30.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>0.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1dc8a88a-e866-4e8d-8e1f-2c8a8c3aba12' class='xr-section-summary-in' type='checkbox'  ><label for='section-1dc8a88a-e866-4e8d-8e1f-2c8a8c3aba12' class='xr-section-summary' >Data variables: <span>(53)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>EASE_column_index</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f5b81f7-339d-4af5-bd34-288dd0996a11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f5b81f7-339d-4af5-bd34-288dd0996a11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48a28586-33f3-48e0-a964-c1463a02830a' class='xr-var-data-in' type='checkbox'><label for='data-48a28586-33f3-48e0-a964-c1463a02830a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The column index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>963</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EASE_row_index</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d635b7d7-dae4-4284-b0c4-926565b1a1f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d635b7d7-dae4-4284-b0c4-926565b1a1f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7bf8a3-bf1d-4cc9-956e-15a04e3ab1b0' class='xr-var-data-in' type='checkbox'><label for='data-1a7bf8a3-bf1d-4cc9-956e-15a04e3ab1b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The row index of the 36 km EASE grid cell that contains the associated data.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>405</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba1b1e6c-048c-42d3-ac5a-f5444b2bf78c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba1b1e6c-048c-42d3-ac5a-f5444b2bf78c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d1ef032-d46f-4701-8213-f3696a6085c4' class='xr-var-data-in' type='checkbox'><label for='data-6d1ef032-d46f-4701-8213-f3696a6085c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7ae021f9-2f97-4955-bf49-ad2056580205' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ae021f9-2f97-4955-bf49-ad2056580205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f46381ee-cc71-42a8-b165-d9da99cb2c03' class='xr-var-data-in' type='checkbox'><label for='data-f46381ee-cc71-42a8-b165-d9da99cb2c03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in DCA within the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-275472c0-2283-41d8-9cb1-12d0859cdad2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-275472c0-2283-41d8-9cb1-12d0859cdad2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-391c2c5c-ffe0-4cdf-8960-2a853838c60b' class='xr-var-data-in' type='checkbox'><label for='data-391c2c5c-ffe0-4cdf-8960-2a853838c60b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-H within the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albedo_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-423fee9f-6417-45fd-9a7f-dcfa49307069' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-423fee9f-6417-45fd-9a7f-dcfa49307069' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98b125ec-55ef-48f6-a986-0d57964f0128' class='xr-var-data-in' type='checkbox'><label for='data-98b125ec-55ef-48f6-a986-0d57964f0128' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>Diffuse reflecting power of the Earth&amp;apos;s surface used in SCA-V within the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boresight_incidence</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42451715-303c-47b3-bb17-42b3f655b413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42451715-303c-47b3-bb17-42b3f655b413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd8d2513-6686-4570-90f2-9a9d534a59a6' class='xr-var-data-in' type='checkbox'><label for='data-fd8d2513-6686-4570-90f2-9a9d534a59a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative angle between the antenna boresight vector and the normal to the Earth&amp;apos;s surface for all footprints within the cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bulk_density</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4da076d-58f5-40fc-8909-ef259dd32c68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4da076d-58f5-40fc-8909-ef259dd32c68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59405dbd-6c3e-463d-90ba-fa2ebe7f1419' class='xr-var-data-in' type='checkbox'><label for='data-59405dbd-6c3e-463d-90ba-fa2ebe7f1419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated bulk_density within the 36 km grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>2.65</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clay_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f72f457c-60f0-475d-be04-b1368d241100' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72f457c-60f0-475d-be04-b1368d241100' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-397f2ef4-1906-4aa3-9ae6-561261a704ca' class='xr-var-data-in' type='checkbox'><label for='data-397f2ef4-1906-4aa3-9ae6-561261a704ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freeze_thaw_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d02fa74-e92c-4fd8-96f8-21fa30a66070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d02fa74-e92c-4fd8-96f8-21fa30a66070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d22e024-b136-4113-ac0e-916bd6082d72' class='xr-var-data-in' type='checkbox'><label for='data-3d22e024-b136-4113-ac0e-916bd6082d72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fraction of the 36 km grid cell that is denoted as frozen.  Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_surface_status</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4305066-b189-4199-a916-bca408080ec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4305066-b189-4199-a916-bca408080ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-345c564a-932b-423c-ab12-14487253e8bb' class='xr-var-data-in' type='checkbox'><label for='data-345c564a-932b-423c-ab12-14487253e8bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Indicates if the grid point lies on land (0) or water (1).</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class</span></div><div class='xr-var-dims'>(y, x, igbp_class)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a23b06fd-6bab-4379-8834-95319de2d0fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a23b06fd-6bab-4379-8834-95319de2d0fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce0a2a93-9981-4714-855f-be49c7da924b' class='xr-var-data-in' type='checkbox'><label for='data-ce0a2a93-9981-4714-855f-be49c7da924b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>An enumerated type that specifies the most common landcover class in the grid cell based on the IGBP landcover map. The array order is longitude (ascending), followed by latitude (descending), and followed by IGBP land cover type descending dominance (only the first three types are listed)</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landcover_class_fraction</span></div><div class='xr-var-dims'>(y, x, igbp_class)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70168796-534a-419c-bea6-35a40b807bc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70168796-534a-419c-bea6-35a40b807bc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1c84678-8b9a-43cd-a967-ac848d41b4ad' class='xr-var-data-in' type='checkbox'><label for='data-e1c84678-8b9a-43cd-a967-ac848d41b4ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>The fraction of the grid cell that contains the most common land cover in that area based on the IGBP landcover map.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[1174152 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>83.63 83.63 83.63 ... -83.63 -83.63</div><input id='attrs-890a2640-d653-44e6-af84-231781e6e2fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-890a2640-d653-44e6-af84-231781e6e2fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84c7a1e1-2cb2-47ac-8ebf-4a6c14f7e3c1' class='xr-var-data-in' type='checkbox'><label for='data-84c7a1e1-2cb2-47ac-8ebf-4a6c14f7e3c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[ 83.63197,  83.63197,  83.63197, ...,  83.63197,  83.63197,\n",
-       "         83.63197],\n",
-       "       [ 81.48033,  81.48033,  81.48033, ...,  81.48033,  81.48033,\n",
-       "         81.48033],\n",
-       "       [ 79.76896,  79.76896,  79.76896, ...,  79.76896,  79.76896,\n",
-       "         79.76896],\n",
+       "  * x                                (x) float64 8kB -1.735e+07 ... 1.735e+07\n",
+       "  * y                                (y) float64 3kB 7.297e+06 ... -7.297e+06\n",
+       "    WGS84_NSIDC_EASEGrid_2.0_Global  int64 8B 0\n",
+       "Data variables:\n",
+       "    soil_moisture                    (y, x) float32 2MB -9.999e+03 ... -9.999...\n",
+       "    retrieval_qual_flag              (y, x) uint16 783kB 15 15 15 ... 15 15 15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c1e1a770-9ba8-479a-90ed-07d59a5c03e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c1e1a770-9ba8-479a-90ed-07d59a5c03e1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 406</li><li><span class='xr-has-index'>x</span>: 964</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3707324e-695f-496f-9290-e907ff6bdceb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3707324e-695f-496f-9290-e907ff6bdceb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.735e+07 -1.731e+07 ... 1.735e+07</div><input id='attrs-cfc4ff3c-4df8-4b9b-91c2-c446e6a2876c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfc4ff3c-4df8-4b9b-91c2-c446e6a2876c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6e7f96e-376c-4d3f-ba83-518737efd72f' class='xr-var-data-in' type='checkbox'><label for='data-f6e7f96e-376c-4d3f-ba83-518737efd72f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-17349514.334741, -17313482.113901, -17277449.89306 , ...,\n",
+       "        17277449.89306 ,  17313482.113901,  17349514.334741])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.297e+06 7.26e+06 ... -7.297e+06</div><input id='attrs-7f03cdb4-9b1a-4e2c-b216-486cb61e5daf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f03cdb4-9b1a-4e2c-b216-486cb61e5daf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d13ad856-f3ef-44e1-88b2-dba35aa1419d' class='xr-var-data-in' type='checkbox'><label for='data-d13ad856-f3ef-44e1-88b2-dba35aa1419d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 7296524.720218,  7260492.499378,  7224460.278537, ..., -7224460.278537,\n",
+       "       -7260492.499378, -7296524.720218])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WGS84_NSIDC_EASEGrid_2.0_Global</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-cb2af2ac-e08c-4efc-b9aa-5c98cc305a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb2af2ac-e08c-4efc-b9aa-5c98cc305a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4581f468-65cc-440d-9e21-e6df08bba1a8' class='xr-var-data-in' type='checkbox'><label for='data-4581f468-65cc-440d-9e21-e6df08bba1a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / NSIDC EASE-Grid 2.0 Global</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_cylindrical_equal_area</dd><dt><span>standard_parallel :</span></dt><dd>30.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>0.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / NSIDC EASE-Grid 2.0 Global&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Cylindrical_Equal_Area&quot;],PARAMETER[&quot;standard_parallel_1&quot;,30],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;6933&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32a55507-3945-4453-b4c9-842b5cd0711f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32a55507-3945-4453-b4c9-842b5cd0711f' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-886b9a20-4a90-41bb-aeff-b2cae95d59b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-886b9a20-4a90-41bb-aeff-b2cae95d59b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9d03800-9e8f-46df-a7b9-8704b40c01e9' class='xr-var-data-in' type='checkbox'><label for='data-f9d03800-9e8f-46df-a7b9-8704b40c01e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_max :</span></dt><dd>0.5</dd><dt><span>valid_min :</span></dt><dd>0.02</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
        "       ...,\n",
-       "       [-79.76896, -79.76896, -79.76896, ..., -79.76896, -79.76896,\n",
-       "        -79.76896],\n",
-       "       [-81.48033, -81.48033, -81.48033, ..., -81.48033, -81.48033,\n",
-       "        -81.48033],\n",
-       "       [-83.63197, -83.63197, -83.63197, ..., -83.63197, -83.63197,\n",
-       "        -83.63197]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_centroid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d1d394a-b35c-4d07-97c9-e092c2ef56b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d1d394a-b35c-4d07-97c9-e092c2ef56b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2904a4eb-6a84-4f82-aad6-a70cf52487be' class='xr-var-data-in' type='checkbox'><label for='data-2904a4eb-6a84-4f82-aad6-a70cf52487be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the latitude of the center of the  brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b88f15ef-680e-4739-8b45-fa6f33bf50fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b88f15ef-680e-4739-8b45-fa6f33bf50fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-defcd1f0-af4a-405d-a1da-6d5e9577becf' class='xr-var-data-in' type='checkbox'><label for='data-defcd1f0-af4a-405d-a1da-6d5e9577becf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of the center of the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_centroid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca336c08-11cd-4004-83ac-59700e1d2fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca336c08-11cd-4004-83ac-59700e1d2fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d257a07-3c8a-4bad-b5bc-42d03a12eb20' class='xr-var-data-in' type='checkbox'><label for='data-9d257a07-3c8a-4bad-b5bc-42d03a12eb20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Weighted average of the longitude of the center of the brightness temperature footprints that fall within the EASE grid cell.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>179.999</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radar_water_body_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a9bc57e-93a7-4673-9cfb-812851d8c68d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a9bc57e-93a7-4673-9cfb-812851d8c68d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcb741ed-8560-4db6-a5f1-ab8b282540c0' class='xr-var-data-in' type='checkbox'><label for='data-bcb741ed-8560-4db6-a5f1-ab8b282540c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-503c0e4a-2133-4e11-98fa-ca30535f08ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-503c0e4a-2133-4e11-98fa-ca30535f08ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-277e4c42-4d4c-435e-b398-d41abbc22215' class='xr-var-data-in' type='checkbox'><label for='data-277e4c42-4d4c-435e-b398-d41abbc22215' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-379b5951-ded3-4a39-b4d4-6c4f73eeb16c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-379b5951-ded3-4a39-b4d4-6c4f73eeb16c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb5e3f2a-3695-4675-96de-2b4ca1d9ce20' class='xr-var-data-in' type='checkbox'><label for='data-cb5e3f2a-3695-4675-96de-2b4ca1d9ce20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0f478baf-46a7-4708-a6cb-0b7438a2dec1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f478baf-46a7-4708-a6cb-0b7438a2dec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-409de6ed-4505-4884-9dfc-a3ab364b6d40' class='xr-var-data-in' type='checkbox'><label for='data-409de6ed-4505-4884-9dfc-a3ab364b6d40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-H retrieval algorithms that generate soil moisture for the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d80d7251-cd0d-4aec-ae5c-8cea48646326' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d80d7251-cd0d-4aec-ae5c-8cea48646326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b589fe36-f404-40cd-a5e9-7c9b39d540bd' class='xr-var-data-in' type='checkbox'><label for='data-b589fe36-f404-40cd-a5e9-7c9b39d540bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the SCA-V retrieval algorithms that generate soil moisture for the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3546f694-325a-4fde-a3bc-e61a05c994d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3546f694-325a-4fde-a3bc-e61a05c994d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f82597c-ad7a-4188-a98d-eecb68d3a13d' class='xr-var-data-in' type='checkbox'><label for='data-1f82597c-ad7a-4188-a98d-eecb68d3a13d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e562bf0-6a97-4c19-86ea-fb609be7d24c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e562bf0-6a97-4c19-86ea-fb609be7d24c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b6dabd5-0ecd-4d2b-a417-4780553493ac' class='xr-var-data-in' type='checkbox'><label for='data-7b6dabd5-0ecd-4d2b-a417-4780553493ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a47b6211-0a88-469f-9be0-58dc618e20bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a47b6211-0a88-469f-9be0-58dc618e20bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77fc37e6-9e09-40d1-bec7-54665a127208' class='xr-var-data-in' type='checkbox'><label for='data-77fc37e6-9e09-40d1-bec7-54665a127208' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-H within the 36 km grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roughness_coefficient_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc5eb92b-01c3-4faa-9bfa-ada77745a1b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc5eb92b-01c3-4faa-9bfa-ada77745a1b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fab4d25c-74e2-4cc9-9a57-59eaf75c479e' class='xr-var-data-in' type='checkbox'><label for='data-fab4d25c-74e2-4cc9-9a57-59eaf75c479e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>A unitless value that is indicative of bare soil roughness used in SCA-V within the 36 km grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-801e61b8-6f4a-4d9d-97ad-b0f5edf07ccf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-801e61b8-6f4a-4d9d-97ad-b0f5edf07ccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d2f67ab-9f99-42aa-8597-a14078a76b52' class='xr-var-data-in' type='checkbox'><label for='data-9d2f67ab-9f99-42aa-8597-a14078a76b52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_min :</span></dt><dd>0.02</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0dd0663-bc22-4998-a6ad-3b574fe1cbdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0dd0663-bc22-4998-a6ad-3b574fe1cbdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95cec810-ae1a-4577-961f-c57f5cc58cc2' class='xr-var-data-in' type='checkbox'><label for='data-95cec810-ae1a-4577-961f-c57f5cc58cc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative DCA soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>valid_min :</span></dt><dd>0.02</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_error</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fcfe94e7-ce4f-4c04-ae99-dd4ecc52c813' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcfe94e7-ce4f-4c04-ae99-dd4ecc52c813' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d7ff1bb-4338-4955-9432-aefe57dbf658' class='xr-var-data-in' type='checkbox'><label for='data-1d7ff1bb-4338-4955-9432-aefe57dbf658' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>0.2</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d2a3fef-3c7c-4191-b7c7-2c4f47ada5d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d2a3fef-3c7c-4191-b7c7-2c4f47ada5d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-648c6505-517a-4e92-9596-6a49d2b00477' class='xr-var-data-in' type='checkbox'><label for='data-648c6505-517a-4e92-9596-6a49d2b00477' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-H soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.02</dd><dt><span>valid_max :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_moisture_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad7d843f-1dac-486e-b526-4c82c4638d32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad7d843f-1dac-486e-b526-4c82c4638d32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f2cc18b-ea04-4fcd-bdff-2d8c4b59b4a8' class='xr-var-data-in' type='checkbox'><label for='data-5f2cc18b-ea04-4fcd-bdff-2d8c4b59b4a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative SCA-V soil moisture measurement for the Earth based grid cell.</dd><dt><span>units :</span></dt><dd>cm**3/cm**3</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.02</dd><dt><span>valid_max :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>static_water_body_fraction</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a08d2069-38a7-4af2-bb02-b9073e8f1790' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a08d2069-38a7-4af2-bb02-b9073e8f1790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a98119-491d-46cf-affb-ceca1063e289' class='xr-var-data-in' type='checkbox'><label for='data-59a98119-491d-46cf-affb-ceca1063e289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26d7a71d-c9c7-4809-9cdc-e19bcd6fd247' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26d7a71d-c9c7-4809-9cdc-e19bcd6fd247' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7ffd387-109b-4699-9986-e31c2bb84c04' class='xr-var-data-in' type='checkbox'><label for='data-d7ffd387-109b-4699-9986-e31c2bb84c04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that record ambient surface conditions for the grid cell</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s</dd><dt><span>flag_meanings :</span></dt><dd>36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radiometer_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_temperature</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8df58c10-69ef-49b8-bc8b-2b4589a492ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8df58c10-69ef-49b8-bc8b-2b4589a492ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e487c13d-8064-407e-aeac-db3ce1ee65f1' class='xr-var-data-in' type='checkbox'><label for='data-e487c13d-8064-407e-aeac-db3ce1ee65f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature at land surface based on GMAO GEOS-5 data.</dd><dt><span>units :</span></dt><dd>Kelvins</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>350.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_h</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61919cfc-1b59-4b8a-b6d8-3111cbee22b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61919cfc-1b59-4b8a-b6d8-3111cbee22b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-568f3ece-5154-4441-965f-57c78f4e3ba8' class='xr-var-data-in' type='checkbox'><label for='data-568f3ece-5154-4441-965f-57c78f4e3ba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer horizontal polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_water_fraction_mb_v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aacc05ee-c05f-4b47-8456-477c499304e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aacc05ee-c05f-4b47-8456-477c499304e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a6cf9ab-c76a-40f5-b0d8-ceedda2f816d' class='xr-var-data-in' type='checkbox'><label for='data-5a6cf9ab-c76a-40f5-b0d8-ceedda2f816d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Gain weighted fraction of static water within the radiometer vertical polarization brightness temperature antenna pattern in 36 km Earth grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_3_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-682c5c00-13cf-4e13-8549-73e67fe71c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-682c5c00-13cf-4e13-8549-73e67fe71c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc535297-07ae-4847-9182-3f745bbb3b6b' class='xr-var-data-in' type='checkbox'><label for='data-fc535297-07ae-4847-9182-3f745bbb3b6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Third stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>330.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_4_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-891ae4fc-748b-442e-935f-ba048a578095' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-891ae4fc-748b-442e-935f-ba048a578095' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ffde5b7-f6ab-4f4f-99ac-b8403ea4313c' class='xr-var-data-in' type='checkbox'><label for='data-7ffde5b7-f6ab-4f4f-99ac-b8403ea4313c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fourth stokes parameter for each 36 km grid cell calculated with an adjustment for the presence of water bodies</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>330.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d7a5788-3ce5-46ed-94a6-f54e509c768d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d7a5788-3ce5-46ed-94a6-f54e509c768d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6143d05-f116-46e6-bdd6-8126b10be0ad' class='xr-var-data-in' type='checkbox'><label for='data-c6143d05-f116-46e6-bdd6-8126b10be0ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_max :</span></dt><dd>330.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_h_uncorrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f35f5320-7501-4ad2-a677-c942e19ed4c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f35f5320-7501-4ad2-a677-c942e19ed4c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7ac2257-7f39-4246-9d28-6977d471aad2' class='xr-var-data-in' type='checkbox'><label for='data-c7ac2257-7f39-4246-9d28-6977d471aad2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Horizontal polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>330.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_3</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e50722de-6132-4452-a13a-89d39933816a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e50722de-6132-4452-a13a-89d39933816a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8087f8c8-4af9-481a-9a05-bc7947197759' class='xr-var-data-in' type='checkbox'><label for='data-8087f8c8-4af9-481a-9a05-bc7947197759' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 3rd Stokes brightness temperature within each grid cell</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>3rd_Stokes_quality 3rd_Stokes_range 3rd_Stokes_RFI_detection 3rd_Stokes_RFI_correction 3rd_Stokes_NEDT 3rd_Stokes_direct_sun_correction 3rd_Stokes_reflected_sun_correction 3rd_Stokes_reflected_moon_correction 3rd_Stokes_direct_galaxy_correction 3rd_Stokes_reflected_galaxy_correction 3rd_Stokes_atmosphere_correction 3rd_Stokes_null_value_bit 3rd_Stokes_RFI_check 3rd_Stokes_RFI_clean</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_4</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4864e448-de83-4301-a233-b11b1431097a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4864e448-de83-4301-a233-b11b1431097a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ac9f56-9a92-4730-a5e1-fd2abe892f0d' class='xr-var-data-in' type='checkbox'><label for='data-57ac9f56-9a92-4730-a5e1-fd2abe892f0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the 4th Stokes brightness temperature within each grid cell</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 4096s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>4th_Stokes_quality 4th_Stokes_range 4th_Stokes_RFI_detection 4th_Stokes_RFI_correction 4th_Stokes_NEDT 4th_Stokes_direct_sun_correction 4th_Stokes_reflected_sun_correction 4th_Stokes_reflected_moon_correction 4th_Stokes_direct_galaxy_correction 4th_Stokes_reflected_galaxy_correction 4th_Stokes_atmosphere_correction 4th_Stokes_null_value_bit 4th_Stokes_RFI_check 4th_Stokes_RFI_clean</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_h</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64dd9a23-40b1-4614-acb0-257035e53830' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64dd9a23-40b1-4614-acb0-257035e53830' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c5a1ce8-e496-4ef7-8f28-e8802e54aba0' class='xr-var-data-in' type='checkbox'><label for='data-4c5a1ce8-e496-4ef7-8f28-e8802e54aba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the horizontal polarization brightness temperature within each grid cell</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Horizontal_polarization_quality Horizontal_polarization_range Horizontal_polarization_RFI_detection Horizontal_polarization_RFI_correction Horizontal_polarization_NEDT Horizontal_polarization_direct_sun_correction Horizontal_polarization_reflected_sun_correction Horizontal_polarization_reflected_moon_correction Horizontal_polarization_direct_galaxy_correction Horizontal_polarization_reflected_galaxy_correction Horizontal_polarization_atmosphere_correction Horizontal_polarization_Faraday_rotation_correction Horizontal_polarization_null_value_bit Horizontal_polarization_water_correction Horizontal_polarization_RFI_check Horizontal_polarization_RFI_clean</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_qual_flag_v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b7b0086-bb78-4688-80f3-ef55b34ac2ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b7b0086-bb78-4688-80f3-ef55b34ac2ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c95de38-9fed-4847-92e3-27eaf721fbea' class='xr-var-data-in' type='checkbox'><label for='data-8c95de38-9fed-4847-92e3-27eaf721fbea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bit flags that represent the quality of the vertical polarization brightness temperature within each grid cell</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s, 4096s, 8192s, 16384s, 32768s</dd><dt><span>flag_meanings :</span></dt><dd>Vertical_polarization_quality Vertical_polarization_range Vertical_polarization_RFI_detection Vertical_polarization_RFI_correction Vertical_polarization_NEDT Vertical_polarization_direct_sun_correction Vertical_polarization_reflected_sun_correction Vertical_polarization_reflected_moon_correction Vertical_polarization_direct_galaxy_correction Vertical_polarization_reflected_galaxy_correction Vertical_polarization_atmosphere_correction Vertical_polarization_Faraday_rotation_correction Vertical_polarization_null_value_bit Vertical_polarization_water_correction Vertical_polarization_RFI_check Vertical_polarization_RFI_clean</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_seconds</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7649d453-97a9-4075-a6e7-e7a954b806f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7649d453-97a9-4075-a6e7-e7a954b806f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28b6fcb1-1b52-44a1-bb0f-0c56398ba23b' class='xr-var-data-in' type='checkbox'><label for='data-28b6fcb1-1b52-44a1-bb0f-0c56398ba23b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in seconds since noon on January 1, 2000 UTC.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd><dt><span>valid_max :</span></dt><dd>940000000.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_time_utc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42aeacba-e5c8-4b7e-ba68-471068a897d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42aeacba-e5c8-4b7e-ba68-471068a897d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dfc0c42-d0e7-40ed-92c0-447fb2c1c70a' class='xr-var-data-in' type='checkbox'><label for='data-5dfc0c42-d0e7-40ed-92c0-447fb2c1c70a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arithmetic average of the acquisition time of all of the brightness temperature footprints with a center that falls within the EASE grid cell in UTC.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_corrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b7a611a-47d0-465f-b8bc-dc11727bb1d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b7a611a-47d0-465f-b8bc-dc11727bb1d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5a39984-a0a3-4f3a-ad3e-a976246ec6f4' class='xr-var-data-in' type='checkbox'><label for='data-b5a39984-a0a3-4f3a-ad3e-a976246ec6f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell adjusted for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>330.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb_v_uncorrected</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6dd1df92-5547-4037-acc8-3850e3e73ec7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dd1df92-5547-4037-acc8-3850e3e73ec7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d81c5cf-6a36-43be-ac2d-872e5706c023' class='xr-var-data-in' type='checkbox'><label for='data-5d81c5cf-6a36-43be-ac2d-872e5706c023' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical polarization brightness temperature in 36 km Earth grid cell before adjustment for the presence of water bodies.</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>330.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b57d334a-9888-4947-81d8-8bffd814ff6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b57d334a-9888-4947-81d8-8bffd814ff6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3c5503f-ef00-4d10-b155-251e60b3e86e' class='xr-var-data-in' type='checkbox'><label for='data-f3c5503f-ef00-4d10-b155-251e60b3e86e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_dca</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2eb6a976-90de-4525-a592-81c00b3691e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2eb6a976-90de-4525-a592-81c00b3691e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f6b61dc-7f4f-475f-a1a4-f584f0fe03b1' class='xr-var-data-in' type='checkbox'><label for='data-2f6b61dc-7f4f-475f-a1a4-f584f0fe03b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the DCA retrieval in the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scah</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-530f21a5-1574-477a-ac3d-d75093d450c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-530f21a5-1574-477a-ac3d-d75093d450c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e45532-df26-4505-a1a8-8fd0ee975f4c' class='xr-var-data-in' type='checkbox'><label for='data-f3e45532-df26-4505-a1a8-8fd0ee975f4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-H retrieval in the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_opacity_scav</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6990c6d-fc91-41d4-889c-0eb74fa06166' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6990c6d-fc91-41d4-889c-0eb74fa06166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34d9412c-b7ed-42b6-8b99-2864b7c8f573' class='xr-var-data-in' type='checkbox'><label for='data-34d9412c-b7ed-42b6-8b99-2864b7c8f573' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The measured opacity of the vegetation used in the SCA-V retrieval in the grid cell.</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>-999999.9</dd><dt><span>valid_max :</span></dt><dd>999999.9</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vegetation_water_content</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c9eab3f6-b69b-4828-8175-9a14543e286a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9eab3f6-b69b-4828-8175-9a14543e286a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d3b644f-bd90-4d85-86fa-f091832dc552' class='xr-var-data-in' type='checkbox'><label for='data-3d3b644f-bd90-4d85-86fa-f091832dc552' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Representative measure of water in the vegetation within the 36 km grid cell.</dd><dt><span>units :</span></dt><dd>kg/m**2</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>20.0</dd></dl></div><div class='xr-var-data'><pre>[391384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18bb8305-7862-4f6c-be1e-bf9535de5e6d' class='xr-section-summary-in' type='checkbox'  ><label for='section-18bb8305-7862-4f6c-be1e-bf9535de5e6d' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b5bcd36-35fb-4908-af29-de2ece88495f' class='xr-index-data-in' type='checkbox'/><label for='index-5b5bcd36-35fb-4908-af29-de2ece88495f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -17349514.33474121, -17313482.113900624,  -17277449.89306004,\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
+       "       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_qual_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>15 15 15 15 15 ... 15 15 15 15 15</div><input id='attrs-ff7407bc-ff30-49cd-bbb7-042cfebb5a9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff7407bc-ff30-49cd-bbb7-042cfebb5a9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-677ccecd-724f-46dd-8448-1a7f061df297' class='xr-var-data-in' type='checkbox'><label for='data-677ccecd-724f-46dd-8448-1a7f061df297' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>65534</dd><dt><span>coordinates :</span></dt><dd>/Soil_Moisture_Retrieval_Data_AM/latitude /Soil_Moisture_Retrieval_Data_AM/longitude</dd><dt><span>flag_masks :</span></dt><dd>1s, 2s, 4s, 8s</dd><dt><span>flag_meanings :</span></dt><dd>Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success</dd><dt><span>long_name :</span></dt><dd>Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.</dd></dl></div><div class='xr-var-data'><pre>array([[15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       ...,\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15]], dtype=uint16)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2613813-2253-4cc0-abb9-53d2df3c4e9a' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2613813-2253-4cc0-abb9-53d2df3c4e9a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-27b073be-5d30-4245-a886-927d49b42065' class='xr-index-data-in' type='checkbox'/><label for='index-27b073be-5d30-4245-a886-927d49b42065' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -17349514.33474121, -17313482.113900624,  -17277449.89306004,\n",
        "       -17241417.672219455,  -17205385.45137887, -17169353.230538286,\n",
        "         -17133321.0096977, -17097288.788857117, -17061256.568016537,\n",
        "       -17025224.347175952,\n",
@@ -1610,7 +3195,7 @@
        "        17133321.009697683,  17169353.230538264,  17205385.451378845,\n",
        "        17241417.672219433,  17277449.893060014,  17313482.113900602,\n",
        "        17349514.334741183],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=964))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4cf5c43c-adb1-4167-979d-c087e9a8ef96' class='xr-index-data-in' type='checkbox'/><label for='index-4cf5c43c-adb1-4167-979d-c087e9a8ef96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 7296524.720218307,  7260492.499377724,  7224460.278537139,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=964))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4c4770c-7dbe-41b5-b3e3-966894ff748e' class='xr-index-data-in' type='checkbox'/><label for='index-c4c4770c-7dbe-41b5-b3e3-966894ff748e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 7296524.720218307,  7260492.499377724,  7224460.278537139,\n",
        "        7188428.057696556,  7152395.836855971,  7116363.616015388,\n",
        "        7080331.395174803,   7044299.17433422,  7008266.953493635,\n",
        "        6972234.732653052,\n",
@@ -1619,215 +3204,73 @@
        "       -7080331.395174707, -7116363.616015292, -7152395.836855876,\n",
        "       -7188428.057696461, -7224460.278537043, -7260492.499377628,\n",
        "       -7296524.720218212],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=406))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>igbp_class</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-35be9f7a-25dc-4542-97e4-db2dc0e7cb86' class='xr-index-data-in' type='checkbox'/><label for='index-35be9f7a-25dc-4542-97e4-db2dc0e7cb86' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;igbp_class&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1ce05b9-1891-48e6-90b7-23e55c9220e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d1ce05b9-1891-48e6-90b7-23e55c9220e6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=406))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4082342f-47a1-4f67-b8d1-20c8c85d0898' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4082342f-47a1-4f67-b8d1-20c8c85d0898' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:                      (y: 406, x: 964, igbp_class: 3)\n",
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:                          (y: 406, x: 964)\n",
        "Coordinates:\n",
-       "  * x                            (x) float64 -1.735e+07 -1.731e+07 ... 1.735e+07\n",
-       "  * y                            (y) float64 7.297e+06 7.26e+06 ... -7.297e+06\n",
-       "  * igbp_class                   (igbp_class) int64 0 1 2\n",
-       "    wgs84_easegrid2_global       int64 0\n",
-       "Data variables: (12/53)\n",
-       "    EASE_column_index            (y, x) float32 ...\n",
-       "    EASE_row_index               (y, x) float32 ...\n",
-       "    albedo                       (y, x) float32 ...\n",
-       "    albedo_dca                   (y, x) float32 ...\n",
-       "    albedo_scah                  (y, x) float32 ...\n",
-       "    albedo_scav                  (y, x) float32 ...\n",
-       "    ...                           ...\n",
-       "    tb_v_uncorrected             (y, x) float32 ...\n",
-       "    vegetation_opacity           (y, x) float32 ...\n",
-       "    vegetation_opacity_dca       (y, x) float32 ...\n",
-       "    vegetation_opacity_scah      (y, x) float32 ...\n",
-       "    vegetation_opacity_scav      (y, x) float32 ...\n",
-       "    vegetation_water_content     (y, x) float32 ..."
+       "  * x                                (x) float64 8kB -1.735e+07 ... 1.735e+07\n",
+       "  * y                                (y) float64 3kB 7.297e+06 ... -7.297e+06\n",
+       "    WGS84_NSIDC_EASEGrid_2.0_Global  int64 8B 0\n",
+       "Data variables:\n",
+       "    soil_moisture                    (y, x) float32 2MB -9.999e+03 ... -9.999...\n",
+       "    retrieval_qual_flag              (y, x) uint16 783kB 15 15 15 ... 15 15 15"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gds = gds.assign_coords(x=x, y=y, igbp_class=igbp_class)\n",
+    "ds.rio.write_crs(6933, inplace=True)\n",
     "\n",
-    "gds.rio.write_crs(6933, grid_mapping_name='wgs84_easegrid2_global', inplace=True)\n",
-    "gds.rio.set_spatial_dims(x_dim='x', y_dim='y', inplace=True)\n",
-    "gds.rio.write_coordinate_system(inplace=True)\n",
-    "gds.rio.write_transform(transform)\n",
-    "gds.rio.write_grid_mapping('wgs84_easegrid2_global', inplace=True)\n",
-    "gds"
+    "#ds[grid_mapping_name].attrs = crs.to_cf()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "29cb574a-7f44-4924-b275-0fce25606569",
+   "execution_count": 30,
+   "id": "58167732-336b-49a1-b915-2b3683336226",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wgs84_easegrid2_global\n",
-      "(-17367530.4451615, -7314540.830638505, 17367530.445161473, 7314540.8306386)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(gds.latitude.rio.grid_mapping)\n",
-    "print(gds.rio.bounds())"
+    "ds =xr.decode_cf(ds)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "70f54aaa-a1fc-46a2-826d-737bd71aa386",
+   "execution_count": 31,
+   "id": "f479de98-57b8-401d-8ae6-f5372c835dc1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x700 with 2 Axes>"
+       "<matplotlib.collections.QuadMesh at 0x327c3e260>"
       ]
      },
+     "execution_count": 61,
      "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 672x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 449,
+       "width": 621
+      }
+     },
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "EASEGrid2 = ccrs.epsg(gds.rio.crs.to_epsg())\n",
-    "\n",
-    "fig = plt.figure(figsize=(12,7))\n",
-    "ax = fig.add_subplot(projection=EASEGrid2)\n",
-    "\n",
-    "ax.coastlines()\n",
-    "gds.soil_moisture.plot(ax=ax,)\n",
-    "ax.set_title('Soil Moisture');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae67f0aa-b858-4ee1-aa29-3961df9c54cc",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "## Extra Stuff to be refined"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d00eaa2-7b6e-477a-b71c-6d90d09aa0a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_mapping_name = \"WGS84_NSIDC_EASEGrid_2.0_Global\"\n",
-    "datasets = ['Soil_Moisture_Retrieval_Data_AM/soil_moisture', 'Soil_Moisture_Retrieval_Data_AM/retrieval_qual_flag']\n",
-    "data_vars = {}\n",
-    "dims = ['y', 'x']\n",
-    "with h5py.File(smap_files[0]) as root:\n",
-    "    for dataset in datasets:\n",
-    "        ds = root[dataset]\n",
-    "        path = ds.name\n",
-    "        name = path.split('/')[-1]\n",
-    "        attrs = {k: (v.decode('UTF-8') if hasattr(v, 'decode') else v) for k, v in dict(ds.attrs).items()}\n",
-    "        attrs['grid_mapping'] = grid_mapping_name  # Add so that dataset is CF-compliant and geoferenced\n",
-    "        data_vars[name] = (dims, ds[:], attrs)\n",
-    "\n",
-    "data_vars\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39afd220-8f13-4993-a25a-5ec0513476be",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(data_vars['soil_moisture'][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d7878b4-eec8-4214-8479-df2a8aab1016",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_vars['soil_moisture'][1].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5329fb83-8b95-42db-8ac1-7efe555a0c4f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.pcolormesh(x, y, data_vars['soil_moisture'][1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "083d7efa-0fb3-418b-93b6-04132a0375fe",
-   "metadata": {},
-   "source": [
-    "We also need to define the CRS.  We know that the CRS has the EPSG code 6933.  We can use `pyproj` to get the CRS definition."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fcbd0150-c72c-453b-8045-489f702b8349",
-   "metadata": {},
-   "source": [
-    "Base on [CF-Conventions](http://cfconventions.org/cf-conventions/cf-conventions.html#grid-mappings-and-projections), we add the CRS as a variable with the same name as we used for the `grid_mapping` attribute of each data variable.  We define this variable as an integer.  The grid mapping information is then copied as attributes for that variable.  We use the `to_cf()` method of the `pyproj.CRS` object to do this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "037dc012-8f52-4144-b929-fb3169dcebeb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.Dataset(data_vars, coords={'x': x, 'y': y})\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "667dbefd-1a3e-4b9f-94a7-dfc0cb314961",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds.rio.write_crs(6933, inplace=True)\n",
-    "\n",
-    "#ds[grid_mapping_name].attrs = crs.to_cf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58167732-336b-49a1-b915-2b3683336226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds =xr.decode_cf(ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f479de98-57b8-401d-8ae6-f5372c835dc1",
-   "metadata": {},
-   "outputs": [],
    "source": [
     "ds.soil_moisture.plot()"
    ]
@@ -1855,7 +3298,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

From 3235633148fdf32ecbcdc121544a6afd4cdd9d49 Mon Sep 17 00:00:00 2001
From: Robyn Marowitz <robyn.marowitz@colorado.edu>
Date: Mon, 4 Nov 2024 11:23:49 -0700
Subject: [PATCH 3/3] Update gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 9ed8e86..abe5f44 100644
--- a/.gitignore
+++ b/.gitignore
@@ -20,6 +20,7 @@ notebook/static/lab/*bundle.js
 notebooks/itslive/data/*.nc
 notebooks/iceflow/data
 notebooks/*/Outputs
+notebooks/SMAP/smap_data
 .python-version
 node_modules
 *.py[co]