diff --git a/docs/src/examples/Using-rio-tiler-XarrayReader.ipynb b/docs/src/examples/Using-rio-tiler-XarrayReader.ipynb
index cccf8304..8f086b7b 100644
--- a/docs/src/examples/Using-rio-tiler-XarrayReader.ipynb
+++ b/docs/src/examples/Using-rio-tiler-XarrayReader.ipynb
@@ -61,6 +61,7 @@
        "}\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",
@@ -417,11 +418,11 @@
        "    time_coverage_start:        20200115T210000Z\n",
        "    title:                      Daily MUR SST, Final product\n",
        "    uuid:                       27665bc0-d5fc-11e1-9b23-0800200c9a66\n",
-       "    westernmost_longitude:      -180.0</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-bfb16e49-b3d0-4c8b-a7be-589ff3158465' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bfb16e49-b3d0-4c8b-a7be-589ff3158465' 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>: 6443</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-175fe55c-de1c-4fe8-a658-882f054337c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-175fe55c-de1c-4fe8-a658-882f054337c4' 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'>-89.99 -89.98 ... 89.98 89.99</div><input id='attrs-ff227350-04d2-4ee8-a547-0dec02fdae12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff227350-04d2-4ee8-a547-0dec02fdae12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e4d61be-e463-43c4-aa4c-ece0260b1e23' class='xr-var-data-in' type='checkbox'><label for='data-3e4d61be-e463-43c4-aa4c-ece0260b1e23' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-af002caf-2178-4458-9a3d-5f46ca298b47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af002caf-2178-4458-9a3d-5f46ca298b47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57aa55e6-887e-444c-9dcd-6b3b7c9f0640' class='xr-var-data-in' type='checkbox'><label for='data-57aa55e6-887e-444c-9dcd-6b3b7c9f0640' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2002-06-01T09:00:00 ... 2020-01-...</div><input id='attrs-afba6762-3ca6-46f7-a18f-2eca50e96ae5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afba6762-3ca6-46f7-a18f-2eca50e96ae5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-777d13a3-425a-4eb7-a0d9-c0874f82e4ea' class='xr-var-data-in' type='checkbox'><label for='data-777d13a3-425a-4eb7-a0d9-c0874f82e4ea' title='Show/Hide data repr'><svg 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>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-06-01T09:00:00.000000000&#x27;, &#x27;2002-06-02T09:00:00.000000000&#x27;,\n",
+       "    westernmost_longitude:      -180.0</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-3cbb9720-89bf-4454-bb65-d4425a734324' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3cbb9720-89bf-4454-bb65-d4425a734324' 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>: 6443</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b2f4858c-6f2c-4137-a35f-6ef3c4c8f08b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2f4858c-6f2c-4137-a35f-6ef3c4c8f08b' 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'>-89.99 -89.98 ... 89.98 89.99</div><input id='attrs-22a242ad-dfaf-4f27-a7b8-8b59edbd1d7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22a242ad-dfaf-4f27-a7b8-8b59edbd1d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0d5144b-35b8-4477-8431-be3094024baf' class='xr-var-data-in' type='checkbox'><label for='data-e0d5144b-35b8-4477-8431-be3094024baf' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-c196ed31-5e28-48bf-af8e-ca6f764be876' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c196ed31-5e28-48bf-af8e-ca6f764be876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb82f997-de29-4696-ac3a-250106176860' class='xr-var-data-in' type='checkbox'><label for='data-eb82f997-de29-4696-ac3a-250106176860' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2002-06-01T09:00:00 ... 2020-01-...</div><input id='attrs-1e941d59-e89c-42f9-9702-6f382d11df20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e941d59-e89c-42f9-9702-6f382d11df20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0091ff27-403c-4737-973e-2bf5e8b134c4' class='xr-var-data-in' type='checkbox'><label for='data-0091ff27-403c-4737-973e-2bf5e8b134c4' title='Show/Hide data repr'><svg 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>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-06-01T09:00:00.000000000&#x27;, &#x27;2002-06-02T09:00:00.000000000&#x27;,\n",
        "       &#x27;2002-06-03T09:00:00.000000000&#x27;, ..., &#x27;2020-01-18T09:00:00.000000000&#x27;,\n",
        "       &#x27;2020-01-19T09:00:00.000000000&#x27;, &#x27;2020-01-20T09:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-426ca94f-77a9-4a1d-94c7-7142ee3a2d44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-426ca94f-77a9-4a1d-94c7-7142ee3a2d44' class='xr-section-summary' >Data variables: <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>analysed_sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8049923c-992e-407c-8fae-d0d776b5f05a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8049923c-992e-407c-8fae-d0d776b5f05a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5978ba76-bd9b-4092-9414-0dd04ee6f205' class='xr-var-data-in' type='checkbox'><label for='data-5978ba76-bd9b-4092-9414-0dd04ee6f205' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>&quot;Final&quot; version using Multi-Resolution Variational Analysis (MRVA) method for interpolation</dd><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>-32767</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analysis_error</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-063ca5bb-b01d-4f6d-97e0-775d2536414a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-063ca5bb-b01d-4f6d-97e0-775d2536414a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1e1f5ba-395f-40f6-bbdc-7d682c968e43' class='xr-var-data-in' type='checkbox'><label for='data-f1e1f5ba-395f-40f6-bbdc-7d682c968e43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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'>...</div><input id='attrs-d640cacd-4db8-4758-968b-93c0f0cafb69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d640cacd-4db8-4758-968b-93c0f0cafb69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e9a9a52-2cb0-431f-a001-3defefa86143' class='xr-var-data-in' type='checkbox'><label for='data-2e9a9a52-2cb0-431f-a001-3defefa86143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>mask can be used to further filter the data.</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16]</dd><dt><span>flag_meanings :</span></dt><dd>1=open-sea, 2=land, 5=open-lake, 9=open-sea with ice in the grid, 13=open-lake with ice in the grid</dd><dt><span>flag_values :</span></dt><dd>[1, 2, 5, 9, 13]</dd><dt><span>long_name :</span></dt><dd>sea/land field composite mask</dd><dt><span>source :</span></dt><dd>GMT &quot;grdlandmask&quot;, ice flag from sea_ice_fraction data</dd><dt><span>valid_max :</span></dt><dd>31</dd><dt><span>valid_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_fraction</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-405d48b7-0761-4ae4-954a-a384c9ef7dec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-405d48b7-0761-4ae4-954a-a384c9ef7dec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d0cb6d1-57de-41e8-92ef-63ed3baffa3d' class='xr-var-data-in' type='checkbox'><label for='data-2d0cb6d1-57de-41e8-92ef-63ed3baffa3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ice data interpolated by a nearest neighbor approach.</dd><dt><span>long_name :</span></dt><dd>sea ice area fraction</dd><dt><span>source :</span></dt><dd>EUMETSAT OSI-SAF, copyright EUMETSAT</dd><dt><span>standard_name :</span></dt><dd>sea ice area fraction</dd><dt><span>units :</span></dt><dd>fraction (between 0 and 1)</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c3f4fee-a831-4448-8190-9d14614ae2a5' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c3f4fee-a831-4448-8190-9d14614ae2a5' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4a4c5942-0e62-4683-a392-e02a25680611' class='xr-index-data-in' type='checkbox'/><label for='index-4a4c5942-0e62-4683-a392-e02a25680611' 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([-89.98999786376953,  -89.9800033569336, -89.97000122070312,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7a3293d-3518-426c-84ca-d32e20a68701' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7a3293d-3518-426c-84ca-d32e20a68701' class='xr-section-summary' >Data variables: <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>analysed_sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42ebaf5e-b4cc-4048-8b0a-1cf164caeed8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42ebaf5e-b4cc-4048-8b0a-1cf164caeed8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bdc0a59-a761-431e-8fcf-faee921a55cf' class='xr-var-data-in' type='checkbox'><label for='data-9bdc0a59-a761-431e-8fcf-faee921a55cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>&quot;Final&quot; version using Multi-Resolution Variational Analysis (MRVA) method for interpolation</dd><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>-32767</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analysis_error</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b77af259-3211-4e26-a1f7-b3a83a55ea20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b77af259-3211-4e26-a1f7-b3a83a55ea20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74ce1a7a-890b-46f9-a33d-20ecf0f2aa74' class='xr-var-data-in' type='checkbox'><label for='data-74ce1a7a-890b-46f9-a33d-20ecf0f2aa74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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'>...</div><input id='attrs-835dff86-35f8-4939-b1a8-d42261b89e97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-835dff86-35f8-4939-b1a8-d42261b89e97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f79ff98f-df84-41cd-945a-61fb1db63913' class='xr-var-data-in' type='checkbox'><label for='data-f79ff98f-df84-41cd-945a-61fb1db63913' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>mask can be used to further filter the data.</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16]</dd><dt><span>flag_meanings :</span></dt><dd>1=open-sea, 2=land, 5=open-lake, 9=open-sea with ice in the grid, 13=open-lake with ice in the grid</dd><dt><span>flag_values :</span></dt><dd>[1, 2, 5, 9, 13]</dd><dt><span>long_name :</span></dt><dd>sea/land field composite mask</dd><dt><span>source :</span></dt><dd>GMT &quot;grdlandmask&quot;, ice flag from sea_ice_fraction data</dd><dt><span>valid_max :</span></dt><dd>31</dd><dt><span>valid_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_fraction</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f321a0e8-c8f4-4d6d-979b-873a11e1ca92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f321a0e8-c8f4-4d6d-979b-873a11e1ca92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66b94105-7db9-474d-9ec0-33cc4d7ddc9c' class='xr-var-data-in' type='checkbox'><label for='data-66b94105-7db9-474d-9ec0-33cc4d7ddc9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ice data interpolated by a nearest neighbor approach.</dd><dt><span>long_name :</span></dt><dd>sea ice area fraction</dd><dt><span>source :</span></dt><dd>EUMETSAT OSI-SAF, copyright EUMETSAT</dd><dt><span>standard_name :</span></dt><dd>sea ice area fraction</dd><dt><span>units :</span></dt><dd>fraction (between 0 and 1)</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[4174832052000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9343d203-d959-48be-93f4-4df35b981995' class='xr-section-summary-in' type='checkbox'  ><label for='section-9343d203-d959-48be-93f4-4df35b981995' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dcbb9580-0869-4424-9cbe-d9a4faf4f604' class='xr-index-data-in' type='checkbox'/><label for='index-dcbb9580-0869-4424-9cbe-d9a4faf4f604' 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([-89.98999786376953,  -89.9800033569336, -89.97000122070312,\n",
        "       -89.95999908447266, -89.94999694824219, -89.94000244140625,\n",
        "       -89.93000030517578, -89.91999816894531, -89.91000366210938,\n",
        "        -89.9000015258789,\n",
@@ -430,7 +431,7 @@
        "        89.93000030517578,  89.94000244140625,  89.94999694824219,\n",
        "        89.95999908447266,  89.97000122070312,   89.9800033569336,\n",
        "        89.98999786376953],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=17999))</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-7a2e8b00-e29f-4779-b528-21a08c9a3708' class='xr-index-data-in' type='checkbox'/><label for='index-7a2e8b00-e29f-4779-b528-21a08c9a3708' 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([-179.99000549316406, -179.97999572753906, -179.97000122070312,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=17999))</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-5e3f7d34-e87f-4822-adac-49acbea820d5' class='xr-index-data-in' type='checkbox'/><label for='index-5e3f7d34-e87f-4822-adac-49acbea820d5' 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([-179.99000549316406, -179.97999572753906, -179.97000122070312,\n",
        "        -179.9600067138672,  -179.9499969482422, -179.94000244140625,\n",
        "       -179.92999267578125,  -179.9199981689453, -179.91000366210938,\n",
        "       -179.89999389648438,\n",
@@ -439,7 +440,7 @@
        "        179.94000244140625,   179.9499969482422,   179.9600067138672,\n",
        "        179.97000122070312,  179.97999572753906,  179.99000549316406,\n",
        "                     180.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=36000))</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-f60da839-4a02-4dc4-a8d6-2b18bf8b74c8' class='xr-index-data-in' type='checkbox'/><label for='index-f60da839-4a02-4dc4-a8d6-2b18bf8b74c8' 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;2002-06-01 09:00:00&#x27;, &#x27;2002-06-02 09:00:00&#x27;,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=36000))</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-1cb8fa85-ed17-42d9-af27-7f47ee5e9298' class='xr-index-data-in' type='checkbox'/><label for='index-1cb8fa85-ed17-42d9-af27-7f47ee5e9298' 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;2002-06-01 09:00:00&#x27;, &#x27;2002-06-02 09:00:00&#x27;,\n",
        "               &#x27;2002-06-03 09:00:00&#x27;, &#x27;2002-06-04 09:00:00&#x27;,\n",
        "               &#x27;2002-06-05 09:00:00&#x27;, &#x27;2002-06-06 09:00:00&#x27;,\n",
        "               &#x27;2002-06-07 09:00:00&#x27;, &#x27;2002-06-08 09:00:00&#x27;,\n",
@@ -450,7 +451,7 @@
        "               &#x27;2020-01-15 09:00:00&#x27;, &#x27;2020-01-16 09:00:00&#x27;,\n",
        "               &#x27;2020-01-17 09:00:00&#x27;, &#x27;2020-01-18 09:00:00&#x27;,\n",
        "               &#x27;2020-01-19 09:00:00&#x27;, &#x27;2020-01-20 09:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6443, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37e5b896-5845-4cbd-8d4f-d57b2d5ecc7b' class='xr-section-summary-in' type='checkbox'  ><label for='section-37e5b896-5845-4cbd-8d4f-d57b2d5ecc7b' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Observation Dataset v1.0</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement:  These data were provided by JPL under support by NASA MEaSUREs program.</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>MUR = &quot;Multi-scale Ultra-high Resolution&quot;</dd><dt><span>creator_email :</span></dt><dd>ghrsst@podaac.jpl.nasa.gov</dd><dt><span>creator_name :</span></dt><dd>JPL MUR SST project</dd><dt><span>creator_url :</span></dt><dd>http://mur.jpl.nasa.gov</dd><dt><span>date_created :</span></dt><dd>20200124T010755Z</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.009999999776482582</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.009999999776482582</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>history :</span></dt><dd>created at nominal 4-day latency; replaced nrt (1-day latency) version.</dd><dt><span>id :</span></dt><dd>MUR-JPL-L4-GLOB-v04.1</dd><dt><span>institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>These data are available free of charge under data policy of JPL PO.DAAC.</dd><dt><span>metadata_link :</span></dt><dd>http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&amp;shortName=MUR-JPL-L4-GLOB-v04.1</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>platform :</span></dt><dd>Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Ships</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>04.1</dd><dt><span>project :</span></dt><dd>NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program</dd><dt><span>publisher_email :</span></dt><dd>ghrsst-po@nceo.ac.uk</dd><dt><span>publisher_name :</span></dt><dd>GHRSST Project Office</dd><dt><span>publisher_url :</span></dt><dd>http://www.ghrsst.org</dd><dt><span>references :</span></dt><dd>http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST</dd><dt><span>sensor :</span></dt><dd>MODIS, AMSR2, AVHRR, in-situ</dd><dt><span>source :</span></dt><dd>MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>spatial_resolution :</span></dt><dd>0.01 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20200116T090000Z</dd><dt><span>stop_time :</span></dt><dd>20200116T090000Z</dd><dt><span>summary :</span></dt><dd>A merged, multi-sensor L4 Foundation SST analysis product from JPL.</dd><dt><span>time_coverage_end :</span></dt><dd>20200116T210000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20200115T210000Z</dd><dt><span>title :</span></dt><dd>Daily MUR SST, Final product</dd><dt><span>uuid :</span></dt><dd>27665bc0-d5fc-11e1-9b23-0800200c9a66</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6443, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55cded82-061c-4b06-81c8-ccab7db1ed8a' class='xr-section-summary-in' type='checkbox'  ><label for='section-55cded82-061c-4b06-81c8-ccab7db1ed8a' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Observation Dataset v1.0</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement:  These data were provided by JPL under support by NASA MEaSUREs program.</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>MUR = &quot;Multi-scale Ultra-high Resolution&quot;</dd><dt><span>creator_email :</span></dt><dd>ghrsst@podaac.jpl.nasa.gov</dd><dt><span>creator_name :</span></dt><dd>JPL MUR SST project</dd><dt><span>creator_url :</span></dt><dd>http://mur.jpl.nasa.gov</dd><dt><span>date_created :</span></dt><dd>20200124T010755Z</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.009999999776482582</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.009999999776482582</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>history :</span></dt><dd>created at nominal 4-day latency; replaced nrt (1-day latency) version.</dd><dt><span>id :</span></dt><dd>MUR-JPL-L4-GLOB-v04.1</dd><dt><span>institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>These data are available free of charge under data policy of JPL PO.DAAC.</dd><dt><span>metadata_link :</span></dt><dd>http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&amp;shortName=MUR-JPL-L4-GLOB-v04.1</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>platform :</span></dt><dd>Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Ships</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>04.1</dd><dt><span>project :</span></dt><dd>NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program</dd><dt><span>publisher_email :</span></dt><dd>ghrsst-po@nceo.ac.uk</dd><dt><span>publisher_name :</span></dt><dd>GHRSST Project Office</dd><dt><span>publisher_url :</span></dt><dd>http://www.ghrsst.org</dd><dt><span>references :</span></dt><dd>http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST</dd><dt><span>sensor :</span></dt><dd>MODIS, AMSR2, AVHRR, in-situ</dd><dt><span>source :</span></dt><dd>MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>spatial_resolution :</span></dt><dd>0.01 degrees</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20200116T090000Z</dd><dt><span>stop_time :</span></dt><dd>20200116T090000Z</dd><dt><span>summary :</span></dt><dd>A merged, multi-sensor L4 Foundation SST analysis product from JPL.</dd><dt><span>time_coverage_end :</span></dt><dd>20200116T210000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20200115T210000Z</dd><dt><span>title :</span></dt><dd>Daily MUR SST, Final product</dd><dt><span>uuid :</span></dt><dd>27665bc0-d5fc-11e1-9b23-0800200c9a66</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 117TB\n",
@@ -535,6 +536,7 @@
        "}\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",
@@ -879,11 +881,11 @@
        "    standard_name:  sea_surface_foundation_temperature\n",
        "    units:          kelvin\n",
        "    valid_max:      32767\n",
-       "    valid_min:      -32767</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6443</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cfc4e52a-21ae-43b9-bf28-7ac8f18a3fc2' class='xr-array-in' type='checkbox' checked><label for='section-cfc4e52a-21ae-43b9-bf28-7ac8f18a3fc2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[4174832052000 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-fa6bd48f-e47c-4457-8d6e-931698f785b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa6bd48f-e47c-4457-8d6e-931698f785b0' 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'>-89.99 -89.98 ... 89.98 89.99</div><input id='attrs-7c7b7cee-1360-4868-8883-47be7f3734b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c7b7cee-1360-4868-8883-47be7f3734b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f31a44cd-170c-4b1c-a20f-210f74d5f75a' class='xr-var-data-in' type='checkbox'><label for='data-f31a44cd-170c-4b1c-a20f-210f74d5f75a' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-8a010229-fe35-46bc-87dc-1820f6626ddf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a010229-fe35-46bc-87dc-1820f6626ddf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15fe1718-f930-460f-b3ae-ea9098711642' class='xr-var-data-in' type='checkbox'><label for='data-15fe1718-f930-460f-b3ae-ea9098711642' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2002-06-01T09:00:00 ... 2020-01-...</div><input id='attrs-bb1666b0-8be1-49a9-9878-b3b760066c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb1666b0-8be1-49a9-9878-b3b760066c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d726ba92-bb84-4e62-b027-fd4115f7f67d' class='xr-var-data-in' type='checkbox'><label for='data-d726ba92-bb84-4e62-b027-fd4115f7f67d' title='Show/Hide data repr'><svg 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>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-06-01T09:00:00.000000000&#x27;, &#x27;2002-06-02T09:00:00.000000000&#x27;,\n",
+       "    valid_min:      -32767</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6443</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-48b80a33-47ac-4bf6-aae2-11de1cdc9cd8' class='xr-array-in' type='checkbox' checked><label for='section-48b80a33-47ac-4bf6-aae2-11de1cdc9cd8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[4174832052000 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-d40dc7c4-a337-4642-a9cb-054dd88064f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d40dc7c4-a337-4642-a9cb-054dd88064f5' 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'>-89.99 -89.98 ... 89.98 89.99</div><input id='attrs-a66d6c27-79bd-42df-b4c8-6367e854418b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a66d6c27-79bd-42df-b4c8-6367e854418b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d1c18fd-b1bf-4d72-b434-860e1e24bba3' class='xr-var-data-in' type='checkbox'><label for='data-9d1c18fd-b1bf-4d72-b434-860e1e24bba3' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-6772989f-72dd-4bdd-91c6-ced688959e1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6772989f-72dd-4bdd-91c6-ced688959e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11072c09-f2af-4ee1-9a72-e361f350e1cb' class='xr-var-data-in' type='checkbox'><label for='data-11072c09-f2af-4ee1-9a72-e361f350e1cb' title='Show/Hide data repr'><svg 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>comment :</span></dt><dd>none</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2002-06-01T09:00:00 ... 2020-01-...</div><input id='attrs-e07a4e7b-44cc-4904-ba2a-b68987bbff94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e07a4e7b-44cc-4904-ba2a-b68987bbff94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d82ce52-e25d-4ab7-83d2-d001fb2af05a' class='xr-var-data-in' type='checkbox'><label for='data-0d82ce52-e25d-4ab7-83d2-d001fb2af05a' title='Show/Hide data repr'><svg 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>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-06-01T09:00:00.000000000&#x27;, &#x27;2002-06-02T09:00:00.000000000&#x27;,\n",
        "       &#x27;2002-06-03T09:00:00.000000000&#x27;, ..., &#x27;2020-01-18T09:00:00.000000000&#x27;,\n",
        "       &#x27;2020-01-19T09:00:00.000000000&#x27;, &#x27;2020-01-20T09:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4601ff2c-4322-4ac2-87cb-22fc1ed758cf' class='xr-section-summary-in' type='checkbox'  ><label for='section-4601ff2c-4322-4ac2-87cb-22fc1ed758cf' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-115307a6-c62f-4790-a332-89bc4f1ebb52' class='xr-index-data-in' type='checkbox'/><label for='index-115307a6-c62f-4790-a332-89bc4f1ebb52' 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([-89.98999786376953,  -89.9800033569336, -89.97000122070312,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1de61402-443d-4379-9dcb-e9fb6cc1f793' class='xr-section-summary-in' type='checkbox'  ><label for='section-1de61402-443d-4379-9dcb-e9fb6cc1f793' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-20e21362-300c-4e1a-8008-05c7b3d2d0af' class='xr-index-data-in' type='checkbox'/><label for='index-20e21362-300c-4e1a-8008-05c7b3d2d0af' 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([-89.98999786376953,  -89.9800033569336, -89.97000122070312,\n",
        "       -89.95999908447266, -89.94999694824219, -89.94000244140625,\n",
        "       -89.93000030517578, -89.91999816894531, -89.91000366210938,\n",
        "        -89.9000015258789,\n",
@@ -892,7 +894,7 @@
        "        89.93000030517578,  89.94000244140625,  89.94999694824219,\n",
        "        89.95999908447266,  89.97000122070312,   89.9800033569336,\n",
        "        89.98999786376953],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=17999))</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-6cb27acd-ff96-437a-87bc-84f688c95b7b' class='xr-index-data-in' type='checkbox'/><label for='index-6cb27acd-ff96-437a-87bc-84f688c95b7b' 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([-179.99000549316406, -179.97999572753906, -179.97000122070312,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=17999))</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-6249f900-0884-41c0-8b65-54cf583c9ae0' class='xr-index-data-in' type='checkbox'/><label for='index-6249f900-0884-41c0-8b65-54cf583c9ae0' 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([-179.99000549316406, -179.97999572753906, -179.97000122070312,\n",
        "        -179.9600067138672,  -179.9499969482422, -179.94000244140625,\n",
        "       -179.92999267578125,  -179.9199981689453, -179.91000366210938,\n",
        "       -179.89999389648438,\n",
@@ -901,7 +903,7 @@
        "        179.94000244140625,   179.9499969482422,   179.9600067138672,\n",
        "        179.97000122070312,  179.97999572753906,  179.99000549316406,\n",
        "                     180.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=36000))</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-b4517dfc-a55f-4d66-ab15-bc26be8373ad' class='xr-index-data-in' type='checkbox'/><label for='index-b4517dfc-a55f-4d66-ab15-bc26be8373ad' 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;2002-06-01 09:00:00&#x27;, &#x27;2002-06-02 09:00:00&#x27;,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=36000))</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-9a7ec8f4-52dd-457e-8b2d-8eda3c67af72' class='xr-index-data-in' type='checkbox'/><label for='index-9a7ec8f4-52dd-457e-8b2d-8eda3c67af72' 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;2002-06-01 09:00:00&#x27;, &#x27;2002-06-02 09:00:00&#x27;,\n",
        "               &#x27;2002-06-03 09:00:00&#x27;, &#x27;2002-06-04 09:00:00&#x27;,\n",
        "               &#x27;2002-06-05 09:00:00&#x27;, &#x27;2002-06-06 09:00:00&#x27;,\n",
        "               &#x27;2002-06-07 09:00:00&#x27;, &#x27;2002-06-08 09:00:00&#x27;,\n",
@@ -912,7 +914,7 @@
        "               &#x27;2020-01-15 09:00:00&#x27;, &#x27;2020-01-16 09:00:00&#x27;,\n",
        "               &#x27;2020-01-17 09:00:00&#x27;, &#x27;2020-01-18 09:00:00&#x27;,\n",
        "               &#x27;2020-01-19 09:00:00&#x27;, &#x27;2020-01-20 09:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6443, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0845d4c1-171a-4680-b732-a96c51b45edd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0845d4c1-171a-4680-b732-a96c51b45edd' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>&quot;Final&quot; version using Multi-Resolution Variational Analysis (MRVA) method for interpolation</dd><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>-32767</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6443, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a8d0afb-0c5f-433d-8370-0722ce6f566e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a8d0afb-0c5f-433d-8370-0722ce6f566e' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>&quot;Final&quot; version using Multi-Resolution Variational Analysis (MRVA) method for interpolation</dd><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>-32767</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'analysed_sst' (time: 6443, lat: 17999, lon: 36000)> Size: 33TB\n",
@@ -942,7 +944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "d29e0c33",
    "metadata": {
     "scrolled": true
@@ -952,7 +954,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "bounds=BoundingBox(left=-179.99500549324037, bottom=-89.99499786365084, right=180.0050000000763, top=89.99499786365084) crs='http://www.opengis.net/def/crs/EPSG/0/4326' band_metadata=[] band_descriptions=[] dtype='float64' nodata_type='Nodata' colorinterp=None scales=None offsets=None colormap=None name='analysed_sst' count=1 width=36000 height=17999 attrs={'comment': '\"Final\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation', 'long_name': 'analysed sea surface temperature', 'standard_name': 'sea_surface_foundation_temperature', 'units': 'kelvin', 'valid_max': 32767, 'valid_min': -32767}\n",
+      "bounds=(-179.99500549324037, -89.99499786365084, 180.0050000000763, 89.99499786365084) crs='http://www.opengis.net/def/crs/EPSG/0/4326' band_metadata=[] band_descriptions=[] dtype='float64' nodata_type='Nodata' colorinterp=None scales=None offsets=None colormap=None name='analysed_sst' count=1 width=36000 height=17999 attrs={'comment': '\"Final\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation', 'long_name': 'analysed sea surface temperature', 'standard_name': 'sea_surface_foundation_temperature', 'units': 'kelvin', 'valid_max': 32767, 'valid_min': -32767}\n",
       "0 6\n"
      ]
     }
@@ -974,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "93d11d9c",
    "metadata": {},
    "outputs": [
@@ -993,14 +995,88 @@
     "with XarrayReader(da) as dst:\n",
     "    img = dst.tile(31, 22, 6)\n",
     "\n",
-    "plt.imshow(img.data_as_image());"
+    "plt.imshow(img.data_as_image())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "b4c3f03f",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with XarrayReader(da) as dst:\n",
+    "    img = dst.part(\n",
+    "        [-5, 45, 0.0, 49],\n",
+    "        max_size=1024,\n",
+    "    )\n",
+    "\n",
+    "plt.imshow(img.data_as_image())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "20ce537c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "geojson = {\n",
+    "    \"type\": \"Feature\",\n",
+    "    \"properties\": {},\n",
+    "    \"geometry\": {\n",
+    "        \"coordinates\": [\n",
+    "            [\n",
+    "                [-4.262517145703015, 49.58852771128011],\n",
+    "                [-6.121754821536115, 48.07366436953052],\n",
+    "                [-5.679508966604999, 47.26534294147169],\n",
+    "                [-2.890652452854056, 46.8101748147248],\n",
+    "                [-1.8527423498805433, 47.28371740880311],\n",
+    "                [-1.6541805026549241, 49.26565692911578],\n",
+    "                [-3.0440846882388257, 49.565118360747164],\n",
+    "                [-4.262517145703015, 49.58852771128011],\n",
+    "            ]\n",
+    "        ],\n",
+    "        \"type\": \"Polygon\",\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "with XarrayReader(da) as dst:\n",
+    "    img = dst.feature(\n",
+    "        geojson,\n",
+    "        max_size=1024,\n",
+    "    )\n",
+    "\n",
+    "plt.imshow(img.data_as_image())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07310f05",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/docs/src/readers.md b/docs/src/readers.md
index 68963aed..3f6a10e4 100644
--- a/docs/src/readers.md
+++ b/docs/src/readers.md
@@ -998,7 +998,7 @@ For ImageReader we are using a custom `LocalTileMatrixSet` constructed from the
 
 ## rio_tiler.io.xarray.XarrayReader
 
-The `Reader` is designed to work with xarray.DataReader with full geo-reference metadata (CRS) and variables (X,Y)
+The `Reader` is designed to work with `xarray.DataReader` with full geo-reference metadata (CRS) and variables (X,Y)
 
 The class is derived from the `rio_tiler.io.base.BaseReader` class.
 ```python
@@ -1025,6 +1025,7 @@ XarrayReader.__mro__
 - **transform**: dataset's Affine transform
 - **height**: dataset's height
 - **width**: dataset's width
+- **band_names**: list of dataArray dims
 
 ```python
 import numpy
@@ -1071,7 +1072,7 @@ EPSG:4326
     from rio_tiler.models import ImageData
 
     with XarrayReader(data) as src:
-        # src.tile(tile_x, tile_y, tile_z, tilesize, reproject_method)
+        # src.tile(tile_x, tile_y, tile_z, tilesize, reproject_method, auto_expand, nodata)
         img = src.tile(1, 2, 3)
         assert isinstance(img, ImageData)
         assert img.crs == WEB_MERCATOR_CRS
@@ -1084,7 +1085,7 @@ EPSG:4326
     from rio_tiler.models import ImageData
 
     with XarrayReader(data) as src:
-        # src.part((minx, miny, maxx, maxy), dst_crs, bounds_crs, reproject_method)
+        # src.part((minx, miny, maxx, maxy), dst_crs, bounds_crs, reproject_method, auto_expand, nodata, max_size, height, width, resampling_method)
         img = src.part((10, 10, 20, 20))
         assert isinstance(img, ImageData)
         assert img.crs == WGS84_CRS
@@ -1121,7 +1122,7 @@ EPSG:4326
     }
 
     with XarrayReader(data) as src:
-        # src.part(geojson_feature, **kwargs)
+        # src.part(geojson_feature, dst_crs, shape_crs, auto_expand, nodata, max_size, height, width, resampling_method)
         img = src.feature(feat)
         assert isinstance(img, ImageData)
         assert img.crs == WGS84_CRS
@@ -1173,16 +1174,68 @@ EPSG:4326
     }
     ```
 
-- **preview()**:
+- **preview()**: Return low-resolution representation of the DataArray
 
-!!! Important
+    ```python
+    from rio_tiler.io import XarrayReader
+    from rio_tiler.models import ImageData
 
-    Not Implemented
+    with XarrayReader(data) as src:
+        # src.preview(dst_crs, reproject_method, nodata, max_size, height, width, resampling_method)
+        img = src.preview()
+        assert isinstance(img, ImageData)
+        assert img.crs == data.rio.crs
+        assert img.bounds == data.rio.bounds()
+
+    with XarrayReader(data) as src:
+        img = src.preview(WGS84_CRS, max_size=100)
+        assert img.crs == WGS84_CRS
+    ```
 
+    !!! warnings
 
-- **statistics()**:
+        This method will read the whole DataArray into memory
 
-!!! Important
+- **statistics()**: Return DataArray statistics (Min/Max/Stdev)
+
+    ```python
+    from rio_tiler.io import XarrayReader
+    from rio_tiler.models import ImageData
+
+    with XarrayReader(data) as src:
+        stats = src.statistics()
+        assert isinstance(stats, dict)
+
+    # stats will be in form or {"band": BandStatistics(), ...}
+    print(stats)
+    >>> {
+        '2022-01-01T00:00:00.000000000': BandStatistics(...),
+    }
+
+    print(stats["2022-01-01T00:00:00.000000000"].model_dump())
+    >>> {
+        "min": 1,
+        "max": 7872,
+        "mean": 2107.524612053134,
+        "count": 1045504,
+        "sum": 2203425412,
+        "std": 2271.0065537857326,
+        "median": 2800,
+        "majority": 1,
+        "minority": 7072,
+        "unique": 15,
+        "histogram": [
+            [...],
+            [...]
+        ],
+        "valid_percent": 100,
+        "masked_pixels": 0,
+        "valid_pixels": 1045504,
+        "percentile_98": 6896,
+        "percentile_2": 1
+    }
+    ```
 
-    Not Implemented
+    !!! warnings
 
+        This method will read the whole DataArray into memory
diff --git a/rio_tiler/io/xarray.py b/rio_tiler/io/xarray.py
index d8c53a6c..7541458c 100644
--- a/rio_tiler/io/xarray.py
+++ b/rio_tiler/io/xarray.py
@@ -10,9 +10,13 @@
 from morecantile import Tile, TileMatrixSet
 from rasterio.crs import CRS
 from rasterio.enums import Resampling
+from rasterio.errors import NotGeoreferencedWarning
+from rasterio.features import bounds as featureBounds
+from rasterio.features import rasterize
 from rasterio.transform import from_bounds, rowcol
 from rasterio.warp import calculate_default_transform
 from rasterio.warp import transform as transform_coords
+from rasterio.warp import transform_geom
 
 from rio_tiler.constants import WEB_MERCATOR_TMS, WGS84_CRS
 from rio_tiler.errors import (
@@ -463,6 +467,7 @@ def feature(
         dst_crs: Optional[CRS] = None,
         shape_crs: CRS = WGS84_CRS,
         reproject_method: WarpResampling = "nearest",
+        auto_expand: bool = True,
         nodata: Optional[NoData] = None,
         max_size: Optional[int] = None,
         height: Optional[int] = None,
@@ -476,6 +481,7 @@ def feature(
             dst_crs (rasterio.crs.CRS, optional): Overwrite target coordinate reference system.
             shape_crs (rasterio.crs.CRS, optional): Input geojson coordinate reference system. Defaults to `epsg:4326`.
             reproject_method (WarpResampling, optional): WarpKernel resampling algorithm. Defaults to `nearest`.
+            auto_expand (boolean, optional): When True, rioxarray's clip_box will expand clip search if only 1D raster found with clip. When False, will throw `OneDimensionalRaster` error if only 1 x or y data point is found. Defaults to True.
             nodata (int or float, optional): Overwrite dataset internal nodata value.
             max_size (int, optional): Limit the size of the longest dimension of the dataset read, respecting bounds X/Y aspect ratio.
             height (int, optional): Output height of the array.
@@ -486,66 +492,46 @@ def feature(
             rio_tiler.models.ImageData: ImageData instance with data, mask and input spatial info.
 
         """
-        if max_size and width and height:
-            warnings.warn(
-                "'max_size' will be ignored with with 'height' and 'width' set.",
-                UserWarning,
-            )
+        shape = _validate_shape_input(shape)
 
         if not dst_crs:
             dst_crs = shape_crs
 
-        shape = _validate_shape_input(shape)
-
-        ds = self.input
-        if nodata is not None:
-            ds = ds.rio.write_nodata(nodata)
-
-        ds = ds.rio.clip([shape], crs=shape_crs)
+        # Get BBOX of the polygon
+        bbox = featureBounds(shape)
 
-        if dst_crs != self.crs:
-            dst_transform, w, h = calculate_default_transform(
-                self.crs,
-                dst_crs,
-                ds.rio.width,
-                ds.rio.height,
-                *ds.rio.bounds(),
-            )
-            ds = ds.rio.reproject(
-                dst_crs,
-                shape=(h, w),
-                transform=dst_transform,
-                resampling=Resampling[reproject_method],
-                nodata=nodata,
-            )
-
-        # Forward valid_min/valid_max to the ImageData object
-        minv, maxv = ds.attrs.get("valid_min"), ds.attrs.get("valid_max")
-        stats = None
-        if minv is not None and maxv is not None:
-            stats = ((minv, maxv),) * ds.rio.count
-
-        arr = ds.to_masked_array()
-        arr.mask |= arr.data == ds.rio.nodata
-
-        img = ImageData(
-            arr,
-            bounds=ds.rio.bounds(),
-            crs=ds.rio.crs,
-            dataset_statistics=stats,
-            band_names=self.band_names,
+        img = self.part(
+            bbox,
+            dst_crs=dst_crs,
+            bounds_crs=shape_crs,
+            nodata=nodata,
+            max_size=max_size,
+            width=width,
+            height=height,
+            reproject_method=reproject_method,
+            resampling_method=resampling_method,
         )
 
-        output_height = height or img.height
-        output_width = width or img.width
-        if max_size and not (width and height):
-            output_height, output_width = _get_width_height(
-                max_size, img.height, img.width
-            )
+        if dst_crs != shape_crs:
+            shape = transform_geom(shape_crs, dst_crs, shape)
 
-        if output_height != img.height or output_width != img.width:
-            img = img.resize(
-                output_height, output_width, resampling_method=resampling_method
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                category=NotGeoreferencedWarning,
+                module="rasterio",
             )
+            cutline_mask = rasterize(
+                [shape],
+                out_shape=(img.height, img.width),
+                transform=img.transform,
+                all_touched=True,  # Mandatory for matching masks at different resolutions
+                default_value=0,
+                fill=1,
+                dtype="uint8",
+            ).astype("bool")
+
+        img.cutline_mask = cutline_mask
+        img.array.mask = numpy.where(~cutline_mask, img.array.mask, True)
 
         return img
diff --git a/tests/test_io_xarray.py b/tests/test_io_xarray.py
index 9c28f51b..b6b907f4 100644
--- a/tests/test_io_xarray.py
+++ b/tests/test_io_xarray.py
@@ -151,16 +151,16 @@ def test_xarray_reader():
         img = dst.feature(feat)
         assert img.count == 1
         assert img.band_names == ["2022-01-01T00:00:00.000000000"]
-        assert img.array.shape == (1, 25, 30)
+        assert img.array.shape == (1, 25, 32)
 
         img = dst.feature(feat, dst_crs="epsg:3857")
         assert img.count == 1
         assert img.band_names == ["2022-01-01T00:00:00.000000000"]
         assert img.crs == "epsg:3857"
-        assert img.array.shape == (1, 20, 33)
+        assert img.array.shape == (1, 20, 35)
 
         img = dst.feature(feat, max_size=15)
-        assert img.array.shape == (1, 13, 15)
+        assert img.array.shape == (1, 12, 15)
 
         img = dst.feature(feat, width=50, height=45)
         assert img.array.shape == (1, 45, 50)