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    "# Reading C3S SM data in python\n",
    "\n",
    "## With xarray\n",
    "\n",
    "The easiest way to load C3S SM image data as downloaded from CDS into memory\n",
    "is by using [xarray](https://docs.xarray.dev/en/stable/).\n",
    "\n",
    "When xarray is installed, it can be used to load one or multiple C3S SM\n",
    "images into memory and merge them into a data cube along the time dimension.\n",
    "\n",
    "### Loading a single C3S SM netcdf image\n",
    "In the first example we simply load one image as an xarray Dataset:"
   ],
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   "source": [
    "# to install xarray see https://docs.xarray.dev/en/stable/\n",
    "import xarray as xr\n",
    "img = xr.open_dataset(\"./../../tests/c3s_sm-test-data/img/TCDR/060_dailyImages/combined/2014/C3S-SOILMOISTURE-L3S-SSMV-COMBINED-DAILY-20140101000000-TCDR-v201801.0.0.nc\")"
   ],
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:20.637071Z",
     "start_time": "2024-08-29T14:12:20.149493Z"
    }
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  },
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   "cell_type": "markdown",
   "source": [
    "From there on we can use xarray functionality extract numpy array, plot data, convert them into pandas DataFrames etc."
   ],
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   "id": "52ee63ccc34306b"
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   "cell_type": "markdown",
   "source": [
    "### Loading and stacking multiple C3S SM images\n",
    "\n",
    "To load multiple images at once, we can use `xarray.open_mfdataset`. For this\n",
    "we have to make sure that the [`dask` library](https://www.dask.org/) is installed (e.g. via \n",
    "``conda install dask``). In the example below we just load 2 images from the\n",
    "test data.\n"
   ],
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   "cell_type": "code",
   "outputs": [
    {
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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\ndl.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: 75MB\nDimensions:         (time: 2, lat: 720, lon: 1440)\nCoordinates:\n  * lat             (lat) float32 3kB 89.88 89.62 89.38 ... -89.38 -89.62 -89.88\n  * lon             (lon) float32 6kB -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n  * time            (time) datetime64[ns] 16B 2014-01-01 2014-01-02\nData variables:\n    t0              (time, lat, lon) datetime64[ns] 17MB dask.array&lt;chunksize=(1, 360, 720), meta=np.ndarray&gt;\n    sm              (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n    sm_uncertainty  (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n    dnflag          (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n    flag            (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n    freqbandID      (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n    mode            (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\n    sensor          (time, lat, lon) float32 8MB dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;\nAttributes: (12/40)\n    title:                      C3S Surface Soil Moisture COMBINED active+pas...\n    institution:                EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)\n    contact:                    C3S_SM_Science@eodc.eu\n    source:                     WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moistu...\n    platform:                   Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1...\n    sensor:                     SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMO...\n    ...                         ...\n    geospatial_vertical_max:    0.0\n    geospatial_lat_units:       degrees_north\n    geospatial_lon_units:       degrees_east\n    geospatial_lat_resolution:  0.25 degree\n    geospatial_lon_resolution:  0.25 degree\n    spatial_resolution:         25km</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-33288ce8-7b0b-4366-bf25-8dd91898a4a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33288ce8-7b0b-4366-bf25-8dd91898a4a1' 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'>time</span>: 2</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8de66a7d-8cc8-4baf-884a-c579897ba42d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8de66a7d-8cc8-4baf-884a-c579897ba42d' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>89.88 89.62 89.38 ... -89.62 -89.88</div><input id='attrs-92fe2c05-582b-4d5e-a10e-79ae9e82f345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92fe2c05-582b-4d5e-a10e-79ae9e82f345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cad49a04-2fcc-4833-983f-8311fbbf30fd' class='xr-var-data-in' type='checkbox'><label for='data-cad49a04-2fcc-4833-983f-8311fbbf30fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd></dl></div><div class='xr-var-data'><pre>array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875],\n      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-ef56e663-4997-4671-af4f-8def8c840689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef56e663-4997-4671-af4f-8def8c840689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87392953-b3ee-488c-b3af-7e6ef2277982' class='xr-var-data-in' type='checkbox'><label for='data-87392953-b3ee-488c-b3af-7e6ef2277982' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2014-01-01 2014-01-02</div><input id='attrs-915e09cd-c909-4e32-bb63-f82ab4089211' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-915e09cd-c909-4e32-bb63-f82ab4089211' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a838a6aa-c54c-45be-b98c-0cb2f299a8da' class='xr-var-data-in' type='checkbox'><label for='data-a838a6aa-c54c-45be-b98c-0cb2f299a8da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-02T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7576512e-76e2-48af-b25e-bf7b5699e582' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7576512e-76e2-48af-b25e-bf7b5699e582' class='xr-section-summary' >Data variables: <span>(8)</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>t0</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 360, 720), meta=np.ndarray&gt;</div><input id='attrs-384babf1-2a64-4875-81e4-4b055b1bd8ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-384babf1-2a64-4875-81e4-4b055b1bd8ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd9504bd-b223-47cc-830c-8b9f6cd39a32' class='xr-var-data-in' type='checkbox'><label for='data-bd9504bd-b223-47cc-830c-8b9f6cd39a32' title='Show/Hide data repr'><svg 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>Observation Timestamp</dd><dt><span>valid_range :</span></dt><dd>[16070.5 16071.5]</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 15.82 MiB </td>\n                        <td> 1.98 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 360, 720) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"30\" x2=\"24\" y2=\"44\" />\n  <line x1=\"10\" y1=\"60\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"67\" />\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,74.9485979497544 10.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line 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class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d36c7ed-1a94-4337-9503-4e008b0b0ae8' class='xr-var-data-in' type='checkbox'><label for='data-4d36c7ed-1a94-4337-9503-4e008b0b0ae8' title='Show/Hide data repr'><svg 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>Volumetric Soil Moisture</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" 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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>Volumetric Soil Moisture Uncertainty</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>description :</span></dt><dd>Uncertainty is the estimated standard deviation of the  observation</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"60\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines 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font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">2</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dnflag</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-166752e6-eea3-4ec7-b60b-add3e3b352ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-166752e6-eea3-4ec7-b60b-add3e3b352ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68802703-4970-45bb-b976-26bf63a1c0cd' class='xr-var-data-in' type='checkbox'><label for='data-68802703-4970-45bb-b976-26bf63a1c0cd' title='Show/Hide data repr'><svg 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>Day / Night Flag</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>flag_meanings :</span></dt><dd>NaN day night day_night_combination</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"60\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"67\" />\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" 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class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-e9e6aa92-3694-42fe-8ea7-cc4e6b60931c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9e6aa92-3694-42fe-8ea7-cc4e6b60931c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d88763-e17d-42d9-b18a-eef5ca05aa56' class='xr-var-data-in' type='checkbox'><label for='data-a8d88763-e17d-42d9-b18a-eef5ca05aa56' title='Show/Hide data repr'><svg 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>Flag</dd><dt><span>flag_values :</span></dt><dd>[  0   1   2   3   4   5   6   7   8  16  17  20  32 127]</dd><dt><span>flag_meanings :</span></dt><dd>no_data_inconsistency_detected snow_coverage_or_temperature_below_zero dense_vegetation combination_of_flag_values_1_and_2 others_no_convergence_in_the_model_thus_no_valid_sm_estimates combination_of_flag_values_1_and_4 combination_of_flag_values_2_and_4 combination_of_flag_values_1_and_2_and_4 soil_moisture_value_exceeds_physical_boundary weight_of_measurement_below_threshold combination_of_flag_values_1_and_16 combination_of_flag_values_4_and_16 all_datasets_deemed_unreliable NaN</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line 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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>Frequency Band Identification</dd><dt><span>flag_values :</span></dt><dd>[  0   2   4   8  10  16  18  24  26  32  34  64  66  72  74  80  82 128\n 130]</dd><dt><span>flag_meanings :</span></dt><dd>NaN C53 C66 C68 C53+C68 C69 C53+C69 C68+C69 C53+C68+C69 C73 C53+C73 X107 C53+X107 C68+X107 C53+C68+X107 C69+X107 C53+C69+X107 K194 C53+K194</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line 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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>Satellite  Mode</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>flag_meanings :</span></dt><dd>NaN ascending descending ascending_descending_combination</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"60\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n  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style=\"stroke-width:2\" />\n  <line x1=\"24\" y1=\"74\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,74.9485979497544 24.9485979497544,74.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n  <text x=\"164.948598\" y=\"44.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,44.948598)\">720</text>\n  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">2</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensor</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 720, 1440), meta=np.ndarray&gt;</div><input id='attrs-6c2183ea-22d2-4151-992f-dd39362fdfb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c2183ea-22d2-4151-992f-dd39362fdfb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae9cd20a-01b1-4d2b-b929-6df9febaabef' class='xr-var-data-in' type='checkbox'><label for='data-ae9cd20a-01b1-4d2b-b929-6df9febaabef' title='Show/Hide data repr'><svg 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>Sensor</dd><dt><span>flag_values :</span></dt><dd>[  0   1   2   4   8  16  24  32 128 130 132 136 256 264 272 280 288 512\n 544 768 800]</dd><dt><span>flag_meanings :</span></dt><dd>NaN SMMR SSMI TMI AMSRE WindSat AMSRE+WindSat AMSR2 AMIWS SSMI+AMIWS TMI+AMIWS AMSRE+AMIWS ASCATA AMSRE+ASCATA WindSat+ASCATA AMSRE+WindSat+ASCATA AMSR2+ASCATA ASCATB AMSR2+ASCATB ASCATA+ASCATB AMSR2+ASCATA+ASCATB</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 7.91 MiB </td>\n                        <td> 3.96 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (2, 720, 1440) </td>\n                        <td> (1, 720, 1440) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"60\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n  <line 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-88.125, -88.375, -88.625, -88.875, -89.125, -89.375,\n       -89.625, -89.875],\n      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9b1c95f-f8cd-43b8-ad29-81f5de9b814f' class='xr-index-data-in' type='checkbox'/><label for='index-e9b1c95f-f8cd-43b8-ad29-81f5de9b814f' 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([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,\n       -178.125, -177.875, -177.625,\n       ...\n        177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,\n        179.375,  179.625,  179.875],\n      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc14c08d-a56a-494f-b918-e005b9ed61f7' class='xr-index-data-in' type='checkbox'/><label for='index-fc14c08d-a56a-494f-b918-e005b9ed61f7' 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(DatetimeIndex([&#x27;2014-01-01&#x27;, &#x27;2014-01-02&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6738393-1764-4fcd-8c73-110d13ef268e' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6738393-1764-4fcd-8c73-110d13ef268e' class='xr-section-summary' >Attributes: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>C3S Surface Soil Moisture COMBINED active+passive Product</dd><dt><span>institution :</span></dt><dd>EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)</dd><dt><span>contact :</span></dt><dd>C3S_SM_Science@eodc.eu</dd><dt><span>source :</span></dt><dd>WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; ASCSMR02/ASCAT/MetOp-A SSM Swath Grid 12.5 km sampling; ASCSMR02/ASCAT/MetOp-B SSM Swath Grid 12.5 km sampling; LPRMv05/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv05/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv05/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv05/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv05/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv06/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv06/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 &amp; RE04;</dd><dt><span>platform :</span></dt><dd>Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS; ERS-1, ERS-2, METOP-A, METOP-B</dd><dt><span>sensor :</span></dt><dd>SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS; AMI-WS, ASCAT-A, ASCAT-B</dd><dt><span>references :</span></dt><dd>https://climate.copernicus.eu; Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014; Liu, Y.Y., Parinussa, R.M., Dorigo, W.A., De Jeu, R.A.M., Wagner, W., van Dijk, A.I.J.M., McCabe, M.F., &amp; Evans, J.P. (2011): Developing an improved soil moisture dataset by blending passive and active microwave satellite based retrievals. Hydrology and Earth System Sciences, 15, 425-436; Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012): Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012. XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia; Dorigo, W.A., Gruber, A., De Jeu, R.A.M., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R.M., Kidd, R. (2015). Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment; 162, 380-395, doi: 10.1016/j.rse.2014.07.023.; Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P., Hirschi, M., Ikonen, J., Jeu, R.d., Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., Schalie, R.v.d., Seneviratne, S.I., Smolander, T., &amp; Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: state-of-the art and future directions. Remote Sensing of Environment, accepted.</dd><dt><span>product_version :</span></dt><dd>v201801.0</dd><dt><span>id :</span></dt><dd>C3S-SOILMOISTURE-L3S-SSMV-COMBINED-DAILY-20140101000000-TCDR-v201801.0.0.nc</dd><dt><span>tracking_id :</span></dt><dd>959f5ac1-6465-4c64-a544-e91fbcaa0ef2</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>The data set was produced with funding from the Copernicus Climate Change Service.</dd><dt><span>keywords :</span></dt><dd>Soil Moisture/Water Content</dd><dt><span>naming_authority :</span></dt><dd>EODC</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>These data were produced as part of the Copernicus Climate Change Service. Service Contract No 2016/C3S_312a_LOT7_EODC/SC1</dd><dt><span>history :</span></dt><dd>2018-02-16 16:05:55 - file produced</dd><dt><span>date_created :</span></dt><dd>20180216T183011Z</dd><dt><span>creator_name :</span></dt><dd>Earth Observation Data Center (EODC)</dd><dt><span>creator_url :</span></dt><dd>http://eodc.eu</dd><dt><span>creator_email :</span></dt><dd>C3S_SM_Science@eodc.eu</dd><dt><span>project :</span></dt><dd>Copernicus Climate Change Service.</dd><dt><span>license :</span></dt><dd>Copernicus Data License</dd><dt><span>time_coverage_start :</span></dt><dd>20131231T120000Z</dd><dt><span>time_coverage_end :</span></dt><dd>20140101T120000Z</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>spatial_resolution :</span></dt><dd>25km</dd></dl></div></li></ul></div></div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask   # make sure dask is installed\n",
    "#  - conda install dask - https://www.dask.org/\n",
    "import xarray as xr\n",
    "\n",
    "ds = xr.open_mfdataset(\"./../../tests/c3s_sm-test-data/img/TCDR/060_dailyImages/combined/**/*.nc\")\n",
    "ds"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:20.876419Z",
     "start_time": "2024-08-29T14:12:20.637984Z"
    }
   },
   "id": "ec7001504aaacfc7",
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "Afterwards the image data will be loaded as dask arrays, and we can select\n",
    "time stamps to extract from the stack."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6a2fd72ae8377ab6"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
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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\ndl.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: 1MB\nDimensions:         (lat: 120, lon: 240)\nCoordinates:\n  * lat             (lat) float32 480B 59.88 59.62 59.38 ... 30.62 30.38 30.12\n  * lon             (lon) float32 960B -19.88 -19.62 -19.38 ... 39.62 39.88\n    time            datetime64[ns] 8B 2014-01-01\nData variables:\n    t0              (lat, lon) datetime64[ns] 230kB dask.array&lt;chunksize=(120, 80), meta=np.ndarray&gt;\n    sm              (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\n    sm_uncertainty  (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\n    dnflag          (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\n    flag            (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\n    freqbandID      (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\n    mode            (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\n    sensor          (lat, lon) float32 115kB dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;\nAttributes: (12/40)\n    title:                      C3S Surface Soil Moisture COMBINED active+pas...\n    institution:                EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)\n    contact:                    C3S_SM_Science@eodc.eu\n    source:                     WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moistu...\n    platform:                   Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1...\n    sensor:                     SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMO...\n    ...                         ...\n    geospatial_vertical_max:    0.0\n    geospatial_lat_units:       degrees_north\n    geospatial_lon_units:       degrees_east\n    geospatial_lat_resolution:  0.25 degree\n    geospatial_lon_resolution:  0.25 degree\n    spatial_resolution:         25km</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-bf281115-2d91-4b86-8086-48e36afb7f7b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf281115-2d91-4b86-8086-48e36afb7f7b' 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'>lat</span>: 120</li><li><span class='xr-has-index'>lon</span>: 240</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e041b1e7-e047-4081-88b9-501a69f356c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e041b1e7-e047-4081-88b9-501a69f356c4' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>59.88 59.62 59.38 ... 30.38 30.12</div><input id='attrs-09e83dd2-e3b1-42bb-83f4-9420b6eec684' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09e83dd2-e3b1-42bb-83f4-9420b6eec684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91416b91-ff05-41c2-91d5-5b7aa0fb8055' class='xr-var-data-in' type='checkbox'><label for='data-91416b91-ff05-41c2-91d5-5b7aa0fb8055' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd></dl></div><div class='xr-var-data'><pre>array([59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625,\n       55.375, 55.125, 54.875, 54.625, 54.375, 54.125, 53.875, 53.625, 53.375,\n       53.125, 52.875, 52.625, 52.375, 52.125, 51.875, 51.625, 51.375, 51.125,\n       50.875, 50.625, 50.375, 50.125, 49.875, 49.625, 49.375, 49.125, 48.875,\n       48.625, 48.375, 48.125, 47.875, 47.625, 47.375, 47.125, 46.875, 46.625,\n       46.375, 46.125, 45.875, 45.625, 45.375, 45.125, 44.875, 44.625, 44.375,\n       44.125, 43.875, 43.625, 43.375, 43.125, 42.875, 42.625, 42.375, 42.125,\n       41.875, 41.625, 41.375, 41.125, 40.875, 40.625, 40.375, 40.125, 39.875,\n       39.625, 39.375, 39.125, 38.875, 38.625, 38.375, 38.125, 37.875, 37.625,\n       37.375, 37.125, 36.875, 36.625, 36.375, 36.125, 35.875, 35.625, 35.375,\n       35.125, 34.875, 34.625, 34.375, 34.125, 33.875, 33.625, 33.375, 33.125,\n       32.875, 32.625, 32.375, 32.125, 31.875, 31.625, 31.375, 31.125, 30.875,\n       30.625, 30.375, 30.125], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-19.88 -19.62 ... 39.62 39.88</div><input id='attrs-b2b7ab63-6d8f-450d-b13b-ba8412ccf8d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b7ab63-6d8f-450d-b13b-ba8412ccf8d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34982508-42b5-4c90-9592-1f3d21beba37' class='xr-var-data-in' type='checkbox'><label for='data-34982508-42b5-4c90-9592-1f3d21beba37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([-19.875, -19.625, -19.375, ...,  39.375,  39.625,  39.875],\n      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2014-01-01</div><input id='attrs-999b0ece-6855-416e-bbb4-795fe57c9c76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-999b0ece-6855-416e-bbb4-795fe57c9c76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a08ba88a-4777-48e8-9f4f-5cf6a6218eef' class='xr-var-data-in' type='checkbox'><label for='data-a08ba88a-4777-48e8-9f4f-5cf6a6218eef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2014-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f301803-7fcb-498e-a7f5-0fb7db8b780d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f301803-7fcb-498e-a7f5-0fb7db8b780d' class='xr-section-summary' >Data variables: <span>(8)</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>t0</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 80), meta=np.ndarray&gt;</div><input id='attrs-254d7255-8d5b-4006-a4f8-ce290c677dab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-254d7255-8d5b-4006-a4f8-ce290c677dab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad88a3b5-971f-4a71-99d8-3eea83ec1937' class='xr-var-data-in' type='checkbox'><label for='data-ad88a3b5-971f-4a71-99d8-3eea83ec1937' title='Show/Hide data repr'><svg 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>Observation Timestamp</dd><dt><span>valid_range :</span></dt><dd>[16070.5 16071.5]</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 225.00 kiB </td>\n                        <td> 150.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 160) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 2 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" 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meta=np.ndarray&gt;</div><input id='attrs-4cd4dbd2-c6f2-47c7-bb36-51aa1460ae85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cd4dbd2-c6f2-47c7-bb36-51aa1460ae85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d73d24f-c64a-4649-aada-106badde07ba' class='xr-var-data-in' type='checkbox'><label for='data-3d73d24f-c64a-4649-aada-106badde07ba' title='Show/Hide data repr'><svg 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>Volumetric Soil Moisture</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >240</text>\n  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">120</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sm_uncertainty</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;</div><input id='attrs-d32e6af2-ce71-42d4-bd7c-2e7a5e928983' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d32e6af2-ce71-42d4-bd7c-2e7a5e928983' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92813bb0-3d1a-4440-b309-ad1dba2da0b8' class='xr-var-data-in' type='checkbox'><label for='data-92813bb0-3d1a-4440-b309-ad1dba2da0b8' title='Show/Hide data repr'><svg 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>Volumetric Soil Moisture Uncertainty</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>description :</span></dt><dd>Uncertainty is the estimated standard deviation of the  observation</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >240</text>\n  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">120</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dnflag</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;</div><input id='attrs-3bb3aa1b-088a-481d-99e1-b0d66d578c49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bb3aa1b-088a-481d-99e1-b0d66d578c49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb70f529-a470-4b3a-a08d-e672d23667e9' class='xr-var-data-in' type='checkbox'><label for='data-cb70f529-a470-4b3a-a08d-e672d23667e9' title='Show/Hide data repr'><svg 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>Day / Night Flag</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>flag_meanings :</span></dt><dd>NaN day night day_night_combination</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >240</text>\n  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">120</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;</div><input id='attrs-33b469b3-a0f3-445c-acfa-7d627f5a7fc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33b469b3-a0f3-445c-acfa-7d627f5a7fc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e40c2f8-8e43-43ca-8e2a-7b52da81ed25' class='xr-var-data-in' type='checkbox'><label for='data-6e40c2f8-8e43-43ca-8e2a-7b52da81ed25' title='Show/Hide data repr'><svg 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>Flag</dd><dt><span>flag_values :</span></dt><dd>[  0   1   2   3   4   5   6   7   8  16  17  20  32 127]</dd><dt><span>flag_meanings :</span></dt><dd>no_data_inconsistency_detected snow_coverage_or_temperature_below_zero dense_vegetation combination_of_flag_values_1_and_2 others_no_convergence_in_the_model_thus_no_valid_sm_estimates combination_of_flag_values_1_and_4 combination_of_flag_values_2_and_4 combination_of_flag_values_1_and_2_and_4 soil_moisture_value_exceeds_physical_boundary weight_of_measurement_below_threshold combination_of_flag_values_1_and_16 combination_of_flag_values_4_and_16 all_datasets_deemed_unreliable NaN</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >240</text>\n  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">120</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freqbandID</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;</div><input id='attrs-16176921-c38c-4725-b997-3fad1493cf01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16176921-c38c-4725-b997-3fad1493cf01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1437dd6d-8a61-439b-b5e1-9ddaa96b79b0' class='xr-var-data-in' type='checkbox'><label for='data-1437dd6d-8a61-439b-b5e1-9ddaa96b79b0' title='Show/Hide data repr'><svg 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>Frequency Band Identification</dd><dt><span>flag_values :</span></dt><dd>[  0   2   4   8  10  16  18  24  26  32  34  64  66  72  74  80  82 128\n 130]</dd><dt><span>flag_meanings :</span></dt><dd>NaN C53 C66 C68 C53+C68 C69 C53+C69 C68+C69 C53+C68+C69 C73 C53+C73 X107 C53+X107 C68+X107 C53+C68+X107 C69+X107 C53+C69+X107 K194 C53+K194</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >240</text>\n  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">120</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mode</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;</div><input id='attrs-a5d7d3ee-0c1e-4fdf-aca9-2610098e086f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5d7d3ee-0c1e-4fdf-aca9-2610098e086f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-054d31cf-1b74-4451-9f5c-5647d98e6b91' class='xr-var-data-in' type='checkbox'><label for='data-054d31cf-1b74-4451-9f5c-5647d98e6b91' title='Show/Hide data repr'><svg 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>Satellite  Mode</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>flag_meanings :</span></dt><dd>NaN ascending descending ascending_descending_combination</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >240</text>\n  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">120</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensor</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(120, 240), meta=np.ndarray&gt;</div><input id='attrs-f7f1faf4-db25-4bc2-aa4b-d9212f03d03a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7f1faf4-db25-4bc2-aa4b-d9212f03d03a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fb2fcf1-0b74-40e1-b931-3240889ce808' class='xr-var-data-in' type='checkbox'><label for='data-3fb2fcf1-0b74-40e1-b931-3240889ce808' title='Show/Hide data repr'><svg 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>Sensor</dd><dt><span>flag_values :</span></dt><dd>[  0   1   2   4   8  16  24  32 128 130 132 136 256 264 272 280 288 512\n 544 768 800]</dd><dt><span>flag_meanings :</span></dt><dd>NaN SMMR SSMI TMI AMSRE WindSat AMSRE+WindSat AMSR2 AMIWS SSMI+AMIWS TMI+AMIWS AMSRE+AMIWS ASCATA AMSRE+ASCATA WindSat+ASCATA AMSRE+WindSat+ASCATA AMSR2+ASCATA ASCATB AMSR2+ASCATB ASCATA+ASCATB AMSR2+ASCATA+ASCATB</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 112.50 kiB </td>\n                        <td> 112.50 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (120, 240) </td>\n                        <td> (120, 240) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" 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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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b075d53e-ecda-46ed-afbb-9e65d63e36b9' class='xr-index-data-in' type='checkbox'/><label for='index-b075d53e-ecda-46ed-afbb-9e65d63e36b9' 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([59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n       57.625,\n       ...\n       32.375, 32.125, 31.875, 31.625, 31.375, 31.125, 30.875, 30.625, 30.375,\n       30.125],\n      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-668cff76-add9-4df2-91ea-63000e4b2903' class='xr-index-data-in' type='checkbox'/><label for='index-668cff76-add9-4df2-91ea-63000e4b2903' 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([-19.875, -19.625, -19.375, -19.125, -18.875, -18.625, -18.375, -18.125,\n       -17.875, -17.625,\n       ...\n        37.625,  37.875,  38.125,  38.375,  38.625,  38.875,  39.125,  39.375,\n        39.625,  39.875],\n      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=240))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b741a28-7509-4274-8281-6747ae4ea277' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b741a28-7509-4274-8281-6747ae4ea277' class='xr-section-summary' >Attributes: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>C3S Surface Soil Moisture COMBINED active+passive Product</dd><dt><span>institution :</span></dt><dd>EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)</dd><dt><span>contact :</span></dt><dd>C3S_SM_Science@eodc.eu</dd><dt><span>source :</span></dt><dd>WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; ASCSMR02/ASCAT/MetOp-A SSM Swath Grid 12.5 km sampling; ASCSMR02/ASCAT/MetOp-B SSM Swath Grid 12.5 km sampling; LPRMv05/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv05/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv05/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv05/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv05/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv06/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv06/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 &amp; RE04;</dd><dt><span>platform :</span></dt><dd>Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS; ERS-1, ERS-2, METOP-A, METOP-B</dd><dt><span>sensor :</span></dt><dd>SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS; AMI-WS, ASCAT-A, ASCAT-B</dd><dt><span>references :</span></dt><dd>https://climate.copernicus.eu; Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014; Liu, Y.Y., Parinussa, R.M., Dorigo, W.A., De Jeu, R.A.M., Wagner, W., van Dijk, A.I.J.M., McCabe, M.F., &amp; Evans, J.P. (2011): Developing an improved soil moisture dataset by blending passive and active microwave satellite based retrievals. Hydrology and Earth System Sciences, 15, 425-436; Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012): Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012. XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia; Dorigo, W.A., Gruber, A., De Jeu, R.A.M., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R.M., Kidd, R. (2015). Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment; 162, 380-395, doi: 10.1016/j.rse.2014.07.023.; Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P., Hirschi, M., Ikonen, J., Jeu, R.d., Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., Schalie, R.v.d., Seneviratne, S.I., Smolander, T., &amp; Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: state-of-the art and future directions. Remote Sensing of Environment, accepted.</dd><dt><span>product_version :</span></dt><dd>v201801.0</dd><dt><span>id :</span></dt><dd>C3S-SOILMOISTURE-L3S-SSMV-COMBINED-DAILY-20140101000000-TCDR-v201801.0.0.nc</dd><dt><span>tracking_id :</span></dt><dd>959f5ac1-6465-4c64-a544-e91fbcaa0ef2</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>The data set was produced with funding from the Copernicus Climate Change Service.</dd><dt><span>keywords :</span></dt><dd>Soil Moisture/Water Content</dd><dt><span>naming_authority :</span></dt><dd>EODC</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>These data were produced as part of the Copernicus Climate Change Service. Service Contract No 2016/C3S_312a_LOT7_EODC/SC1</dd><dt><span>history :</span></dt><dd>2018-02-16 16:05:55 - file produced</dd><dt><span>date_created :</span></dt><dd>20180216T183011Z</dd><dt><span>creator_name :</span></dt><dd>Earth Observation Data Center (EODC)</dd><dt><span>creator_url :</span></dt><dd>http://eodc.eu</dd><dt><span>creator_email :</span></dt><dd>C3S_SM_Science@eodc.eu</dd><dt><span>project :</span></dt><dd>Copernicus Climate Change Service.</dd><dt><span>license :</span></dt><dd>Copernicus Data License</dd><dt><span>time_coverage_start :</span></dt><dd>20131231T120000Z</dd><dt><span>time_coverage_end :</span></dt><dd>20140101T120000Z</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>spatial_resolution :</span></dt><dd>25km</dd></dl></div></li></ul></div></div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the first time stamp and a spatial subset\n",
    "img = ds.isel(time=0).sel(lon=slice(-20, 40), lat=slice(60, 30))\n",
    "img"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:20.901124Z",
     "start_time": "2024-08-29T14:12:20.877123Z"
    }
   },
   "id": "7b0aa781d5913bc0",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "## With c3s_sm.interface\n",
    "We provide our own image readers in this package. They are mainly used for the conversion\n",
    "to time series, but can also be used to load the data as numpy arrays. They are based on \n",
    "the netCDF4 python package (don't require xarray). There is one reader\n",
    "to read a single netcdf file, and one to find the image for a date in a file collection.\n",
    "\n",
    "For most users the above described use of xarray is probably preferred!\n",
    "\n",
    "### Image reader"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3f0a6530658cfee7"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from c3s_sm.interface import C3SImg\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "img = C3SImg(\"./../../tests/c3s_sm-test-data/img/TCDR/060_dailyImages/combined/2014/C3S-SOILMOISTURE-L3S-SSMV-COMBINED-DAILY-20140101000000-TCDR-v201801.0.0.nc\").read(datetime(2014,1,1))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:21.424493Z",
     "start_time": "2024-08-29T14:12:20.902073Z"
    }
   },
   "id": "341e3469ea4e236",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "This will create an Image object which is basically just a container for numpy arrays, netcdf attributes and dimension variables (lat, lon, time)."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "603ffc8037c94fa7"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7feec9378b00>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(img.data['sm'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:21.833559Z",
     "start_time": "2024-08-29T14:12:21.425158Z"
    }
   },
   "id": "ee094cd218caaee",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "('Image coordinates',\n array([[ 89.875,  89.875,  89.875, ...,  89.875,  89.875,  89.875],\n        [ 89.625,  89.625,  89.625, ...,  89.625,  89.625,  89.625],\n        [ 89.375,  89.375,  89.375, ...,  89.375,  89.375,  89.375],\n        ...,\n        [-89.375, -89.375, -89.375, ..., -89.375, -89.375, -89.375],\n        [-89.625, -89.625, -89.625, ..., -89.625, -89.625, -89.625],\n        [-89.875, -89.875, -89.875, ..., -89.875, -89.875, -89.875]]),\n array([[-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n        [-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n        [-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n        ...,\n        [-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n        [-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n        [-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875]]),\n datetime.datetime(2014, 1, 1, 0, 0))"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Image coordinates\", img.lat, img.lon, img.timestamp"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:21.837888Z",
     "start_time": "2024-08-29T14:12:21.834498Z"
    }
   },
   "id": "7fd94ceb0bdcead9",
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Multi-image reader\n",
    "\n",
    "The image to time series conversion requires a class that reads image data for a time stamp from a directory with multiple files. The class is used as follows:\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "74b13fe620e54260"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from c3s_sm.interface import C3S_Nc_Img_Stack\n",
    "from datetime import datetime\n",
    "\n",
    "stack = C3S_Nc_Img_Stack(\"./../../tests/c3s_sm-test-data/img/TCDR/060_dailyImages/combined/\", fillval=np.nan)\n",
    "ts = datetime(2014, 1, 1)\n",
    "image = stack.read(ts)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:22.078616Z",
     "start_time": "2024-08-29T14:12:21.838651Z"
    }
   },
   "id": "ea60a4a0297b9619",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'SM at 2014-01-01')"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image.data['sm'])\n",
    "plt.title(f\"SM at {str(ts.date())}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-29T14:12:22.263382Z",
     "start_time": "2024-08-29T14:12:22.079298Z"
    }
   },
   "id": "f62aaaad53276709",
   "execution_count": 8
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}