diff --git a/demo/datacube.ipynb b/demo/datacube.ipynb
index 490c05f1..ed860b7a 100644
--- a/demo/datacube.ipynb
+++ b/demo/datacube.ipynb
@@ -633,7 +633,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;colortype&#x27; (time: 3, y: 563, x: 576)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;colortype&#x27; (time: 3, y: 563, x: 576)&gt; Size: 8MB\n",
        "array([[[29., 29., 29., ..., 29., 29., 29.],\n",
        "        [29., 29., 29., ..., 29., 29., 29.],\n",
        "        [29., 29., 29., ...,  5., 29., 29.],\n",
@@ -658,19 +658,19 @@
        "        [ 3., 27.,  3., ..., 27., 27., 27.],\n",
        "        [27.,  3.,  7., ..., 27., 27., 27.]]])\n",
        "Coordinates:\n",
-       "    spatial_ref    int32 0\n",
-       "  * x              (x) float64 4.53e+06 4.53e+06 ... 4.536e+06 4.536e+06\n",
-       "  * y              (y) float64 2.697e+06 2.697e+06 ... 2.691e+06 2.691e+06\n",
-       "    temporal_ref   int32 0\n",
-       "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "    spatial_ref    int32 4B 0\n",
+       "  * x              (x) float64 5kB 4.53e+06 4.53e+06 ... 4.536e+06 4.536e+06\n",
+       "  * y              (y) float64 5kB 2.697e+06 2.697e+06 ... 2.691e+06 2.691e+06\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+       "    spatial_feats  (y, x) float64 3MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    AREA_OR_POINT:  Area\n",
        "    scale_factor:   1.0\n",
        "    add_offset:     0.0\n",
        "    _FillValue:     1.7976931348623157e+308\n",
        "    value_type:     ordinal\n",
-       "    value_labels:   {1: &#x27;SVHNIR&#x27;, 2: &#x27;SVLNIR&#x27;, 3: &#x27;AVHNIR&#x27;, 4: &#x27;AVLNIR&#x27;, 5: &#x27;...</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'>'colortype'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>y</span>: 563</li><li><span class='xr-has-index'>x</span>: 576</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5226f911-baa2-4840-a68c-3dd391cb64cd' class='xr-array-in' type='checkbox' checked><label for='section-5226f911-baa2-4840-a68c-3dd391cb64cd' 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>29.0 29.0 29.0 29.0 29.0 29.0 29.0 ... 4.0 3.0 3.0 27.0 27.0 27.0 27.0</span></div><div class='xr-array-data'><pre>array([[[29., 29., 29., ..., 29., 29., 29.],\n",
+       "    value_labels:   {1: &#x27;SVHNIR&#x27;, 2: &#x27;SVLNIR&#x27;, 3: &#x27;AVHNIR&#x27;, 4: &#x27;AVLNIR&#x27;, 5: &#x27;...</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'>'colortype'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>y</span>: 563</li><li><span class='xr-has-index'>x</span>: 576</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e9d06193-b8d8-4216-b726-8dc3d2dca6be' class='xr-array-in' type='checkbox' checked><label for='section-e9d06193-b8d8-4216-b726-8dc3d2dca6be' 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>29.0 29.0 29.0 29.0 29.0 29.0 29.0 ... 4.0 3.0 3.0 27.0 27.0 27.0 27.0</span></div><div class='xr-array-data'><pre>array([[[29., 29., 29., ..., 29., 29., 29.],\n",
        "        [29., 29., 29., ..., 29., 29., 29.],\n",
        "        [29., 29., 29., ...,  5., 29., 29.],\n",
        "        ...,\n",
@@ -692,29 +692,29 @@
        "        ...,\n",
        "        [ 3.,  3.,  3., ...,  3., 27., 27.],\n",
        "        [ 3., 27.,  3., ..., 27., 27., 27.],\n",
-       "        [27.,  3.,  7., ..., 27., 27., 27.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-832ea869-4634-403b-988d-f63f4122feb3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-832ea869-4634-403b-988d-f63f4122feb3' class='xr-section-summary' >Coordinates: <span>(6)</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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-1e0c65a9-8767-4e45-9e6c-8e9178f2ac2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e0c65a9-8767-4e45-9e6c-8e9178f2ac2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67e3ad25-37d5-4110-8959-8ac2e676ebff' class='xr-var-data-in' type='checkbox'><label for='data-67e3ad25-37d5-4110-8959-8ac2e676ebff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530110.0 10.0 0.0 2696630.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.53e+06 4.53e+06 ... 4.536e+06</div><input id='attrs-d675d624-3faa-4d63-8f80-e92e32a2ad7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d675d624-3faa-4d63-8f80-e92e32a2ad7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06e50a70-6220-40f0-93ba-5165e7deed40' class='xr-var-data-in' type='checkbox'><label for='data-06e50a70-6220-40f0-93ba-5165e7deed40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([4530115., 4530125., 4530135., ..., 4535845., 4535855., 4535865.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.697e+06 2.697e+06 ... 2.691e+06</div><input id='attrs-42bd2668-e923-4f3e-be7d-65de0cc7c0c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42bd2668-e923-4f3e-be7d-65de0cc7c0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46b3aaf9-03d0-4b6c-8dd5-4f3fefca82ff' class='xr-var-data-in' type='checkbox'><label for='data-46b3aaf9-03d0-4b6c-8dd5-4f3fefca82ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([2696625., 2696615., 2696605., ..., 2691025., 2691015., 2691005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-da869dab-77e3-4cba-81d5-4c228a21c544' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da869dab-77e3-4cba-81d5-4c228a21c544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b264a71b-bc28-4766-81f9-633899ed6526' class='xr-var-data-in' type='checkbox'><label for='data-b264a71b-bc28-4766-81f9-633899ed6526' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2019-12-15T10:17:33.408715 ... 2...</div><input id='attrs-c8bb9d68-e8b8-49eb-98d6-80f0dfc6577b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8bb9d68-e8b8-49eb-98d6-80f0dfc6577b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0506e428-f6d3-49fc-b73b-33a24bc9e66e' class='xr-var-data-in' type='checkbox'><label for='data-0506e428-f6d3-49fc-b73b-33a24bc9e66e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-15T10:17:33.408715000&#x27;, &#x27;2020-09-05T10:17:43.167942000&#x27;,\n",
-       "       &#x27;2020-12-19T10:17:34.610661000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-fe964e73-2e5f-4f44-b537-110aaab889b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe964e73-2e5f-4f44-b537-110aaab889b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f7743bf-2813-41cc-a4ea-a24dfa827bc6' class='xr-var-data-in' type='checkbox'><label for='data-2f7743bf-2813-41cc-a4ea-a24dfa827bc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., ..., 1., 1., 1.],\n",
+       "        [27.,  3.,  7., ..., 27., 27., 27.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-13b6e179-0319-46f3-89a1-1e5e5e275647' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13b6e179-0319-46f3-89a1-1e5e5e275647' class='xr-section-summary' >Coordinates: <span>(6)</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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-b3578a98-b214-4f53-96dd-5d991176ba27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3578a98-b214-4f53-96dd-5d991176ba27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17707b93-3335-431e-a78b-49a48af6ba11' class='xr-var-data-in' type='checkbox'><label for='data-17707b93-3335-431e-a78b-49a48af6ba11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530110.0 10.0 0.0 2696630.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.53e+06 4.53e+06 ... 4.536e+06</div><input id='attrs-2447ce62-cc1b-40ba-9cb6-e4f60919774b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2447ce62-cc1b-40ba-9cb6-e4f60919774b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b8a8d05-01b0-4eab-a9f1-86f84aade9f0' class='xr-var-data-in' type='checkbox'><label for='data-1b8a8d05-01b0-4eab-a9f1-86f84aade9f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([4530115., 4530125., 4530135., ..., 4535845., 4535855., 4535865.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.697e+06 2.697e+06 ... 2.691e+06</div><input id='attrs-7cf26c81-7cb2-46dd-b25b-8073183566fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cf26c81-7cb2-46dd-b25b-8073183566fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5b71659-c6a3-4432-903b-ff92efa512e8' class='xr-var-data-in' type='checkbox'><label for='data-e5b71659-c6a3-4432-903b-ff92efa512e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([2696625., 2696615., 2696605., ..., 2691025., 2691015., 2691005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-5cb78006-0dae-444b-8337-6083a244eb0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cb78006-0dae-444b-8337-6083a244eb0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61dbb96c-233d-47f9-bf3e-bba447d33b01' class='xr-var-data-in' type='checkbox'><label for='data-61dbb96c-233d-47f9-bf3e-bba447d33b01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2019-12-15T10:17:33.408715 ... 2...</div><input id='attrs-9513e3e1-8226-4ec0-a6a4-cc86f1612e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9513e3e1-8226-4ec0-a6a4-cc86f1612e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5542b4e2-0b76-46b0-9cf3-572a6cfed4f0' class='xr-var-data-in' type='checkbox'><label for='data-5542b4e2-0b76-46b0-9cf3-572a6cfed4f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-15T10:17:33.408715000&#x27;, &#x27;2020-09-05T10:17:43.167942000&#x27;,\n",
+       "       &#x27;2020-12-19T10:17:34.610661000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-0e8fabc1-6ec5-48cb-ac2e-d36271a3dd45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e8fabc1-6ec5-48cb-ac2e-d36271a3dd45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d20642e9-8ee5-481f-a96a-eb50e88cbe45' class='xr-var-data-in' type='checkbox'><label for='data-d20642e9-8ee5-481f-a96a-eb50e88cbe45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., ..., 1., 1., 1.],\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
        "       ...,\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
-       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d44c61a-10e9-4e89-b67c-5ba2ab0e424c' class='xr-section-summary-in' type='checkbox'  ><label for='section-2d44c61a-10e9-4e89-b67c-5ba2ab0e424c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-013f7020-a292-4581-a867-d38e2fd1f62f' class='xr-index-data-in' type='checkbox'/><label for='index-013f7020-a292-4581-a867-d38e2fd1f62f' 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([4530115.0, 4530125.0, 4530135.0, 4530145.0, 4530155.0, 4530165.0,\n",
+       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f0c3e02-24c7-4fe9-9249-f0cf5ccfeccd' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f0c3e02-24c7-4fe9-9249-f0cf5ccfeccd' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9f47368-14e2-4d24-9a79-d7a60f68d42c' class='xr-index-data-in' type='checkbox'/><label for='index-c9f47368-14e2-4d24-9a79-d7a60f68d42c' 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([4530115.0, 4530125.0, 4530135.0, 4530145.0, 4530155.0, 4530165.0,\n",
        "       4530175.0, 4530185.0, 4530195.0, 4530205.0,\n",
        "       ...\n",
        "       4535775.0, 4535785.0, 4535795.0, 4535805.0, 4535815.0, 4535825.0,\n",
        "       4535835.0, 4535845.0, 4535855.0, 4535865.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=576))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da78bc31-bc42-44a2-92de-fd20af00498d' class='xr-index-data-in' type='checkbox'/><label for='index-da78bc31-bc42-44a2-92de-fd20af00498d' 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([2696625.0, 2696615.0, 2696605.0, 2696595.0, 2696585.0, 2696575.0,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=576))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f5153a4a-d43f-4636-8910-d85dffd0a340' class='xr-index-data-in' type='checkbox'/><label for='index-f5153a4a-d43f-4636-8910-d85dffd0a340' 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([2696625.0, 2696615.0, 2696605.0, 2696595.0, 2696585.0, 2696575.0,\n",
        "       2696565.0, 2696555.0, 2696545.0, 2696535.0,\n",
        "       ...\n",
        "       2691095.0, 2691085.0, 2691075.0, 2691065.0, 2691055.0, 2691045.0,\n",
        "       2691035.0, 2691025.0, 2691015.0, 2691005.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=563))</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-db6e00f4-1c29-4da2-996f-4a0fbce465f5' class='xr-index-data-in' type='checkbox'/><label for='index-db6e00f4-1c29-4da2-996f-4a0fbce465f5' 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;2019-12-15 10:17:33.408715&#x27;, &#x27;2020-09-05 10:17:43.167942&#x27;,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=563))</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-d1527d05-0e61-434b-a206-e23695f52150' class='xr-index-data-in' type='checkbox'/><label for='index-d1527d05-0e61-434b-a206-e23695f52150' 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;2019-12-15 10:17:33.408715&#x27;, &#x27;2020-09-05 10:17:43.167942&#x27;,\n",
        "               &#x27;2020-12-19 10:17:34.610661&#x27;],\n",
-       "              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-303f4ccf-bfdb-49ee-a775-4f15abb154a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-303f4ccf-bfdb-49ee-a775-4f15abb154a2' 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>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>1.7976931348623157e+308</dd><dt><span>value_type :</span></dt><dd>ordinal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;SVHNIR&#x27;, 2: &#x27;SVLNIR&#x27;, 3: &#x27;AVHNIR&#x27;, 4: &#x27;AVLNIR&#x27;, 5: &#x27;WV&#x27;, 6: &#x27;SHV&#x27;, 7: &#x27;SHRBRHNIR&#x27;, 8: &#x27;SHRBRLNIR&#x27;, 9: &#x27;HRBCR&#x27;, 10: &#x27;WR&#x27;, 11: &#x27;PB&#x27;, 12: &#x27;GH&#x27;, 13: &#x27;VBBB&#x27;, 14: &#x27;BBB&#x27;, 15: &#x27;SBB&#x27;, 16: &#x27;ABB&#x27;, 17: &#x27;DBB&#x27;, 18: &#x27;WBBorSHB&#x27;, 19: &#x27;NIRPBB&#x27;, 20: &#x27;BA&#x27;, 21: &#x27;DPWASH&#x27;, 22: &#x27;SLWASH&#x27;, 23: &#x27;TWASH&#x27;, 24: &#x27;SASLWA&#x27;, 27: &#x27;TNCLV&#x27;, 28: &#x27;TNCLWA_BB&#x27;, 29: &#x27;SN&#x27;, 30: &#x27;SHSN&#x27;, 31: &#x27;SH&#x27;, 32: &#x27;FLAME&#x27;}</dd></dl></div></li></ul></div></div>"
+       "              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-cdfd557a-7e32-4c05-9d24-1e75990dde26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdfd557a-7e32-4c05-9d24-1e75990dde26' 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>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>1.7976931348623157e+308</dd><dt><span>value_type :</span></dt><dd>ordinal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;SVHNIR&#x27;, 2: &#x27;SVLNIR&#x27;, 3: &#x27;AVHNIR&#x27;, 4: &#x27;AVLNIR&#x27;, 5: &#x27;WV&#x27;, 6: &#x27;SHV&#x27;, 7: &#x27;SHRBRHNIR&#x27;, 8: &#x27;SHRBRLNIR&#x27;, 9: &#x27;HRBCR&#x27;, 10: &#x27;WR&#x27;, 11: &#x27;PB&#x27;, 12: &#x27;GH&#x27;, 13: &#x27;VBBB&#x27;, 14: &#x27;BBB&#x27;, 15: &#x27;SBB&#x27;, 16: &#x27;ABB&#x27;, 17: &#x27;DBB&#x27;, 18: &#x27;WBBorSHB&#x27;, 19: &#x27;NIRPBB&#x27;, 20: &#x27;BA&#x27;, 21: &#x27;DPWASH&#x27;, 22: &#x27;SLWASH&#x27;, 23: &#x27;TWASH&#x27;, 24: &#x27;SASLWA&#x27;, 27: &#x27;TNCLV&#x27;, 28: &#x27;TNCLWA_BB&#x27;, 29: &#x27;SN&#x27;, 30: &#x27;SHSN&#x27;, 31: &#x27;SH&#x27;, 32: &#x27;FLAME&#x27;}</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'colortype' (time: 3, y: 563, x: 576)>\n",
+       "<xarray.DataArray 'colortype' (time: 3, y: 563, x: 576)> Size: 8MB\n",
        "array([[[29., 29., 29., ..., 29., 29., 29.],\n",
        "        [29., 29., 29., ..., 29., 29., 29.],\n",
        "        [29., 29., 29., ...,  5., 29., 29.],\n",
@@ -739,12 +739,12 @@
        "        [ 3., 27.,  3., ..., 27., 27., 27.],\n",
        "        [27.,  3.,  7., ..., 27., 27., 27.]]])\n",
        "Coordinates:\n",
-       "    spatial_ref    int32 0\n",
-       "  * x              (x) float64 4.53e+06 4.53e+06 ... 4.536e+06 4.536e+06\n",
-       "  * y              (y) float64 2.697e+06 2.697e+06 ... 2.691e+06 2.691e+06\n",
-       "    temporal_ref   int32 0\n",
-       "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "    spatial_ref    int32 4B 0\n",
+       "  * x              (x) float64 5kB 4.53e+06 4.53e+06 ... 4.536e+06 4.536e+06\n",
+       "  * y              (y) float64 5kB 2.697e+06 2.697e+06 ... 2.691e+06 2.691e+06\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+       "    spatial_feats  (y, x) float64 3MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    AREA_OR_POINT:  Area\n",
        "    scale_factor:   1.0\n",
@@ -816,7 +816,7 @@
     {
      "data": {
       "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x23a5c0abac0>"
+       "<xarray.plot.facetgrid.FacetGrid at 0x1c3f67f0880>"
       ]
      },
      "execution_count": 13,
@@ -825,7 +825,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 4 Axes>"
       ]
@@ -848,17 +848,22 @@
    "source": [
     "## The STACCube configuration\n",
     "\n",
-    "Semantique contains a third built-in EO data cube configuration called [STACCube](https://zgis.github.io/semantique/semantique.datacube.STACCube.html). This configuration is targeted at ad-hoc data cubes built from the results of a [STAC](https://stacspec.org/en/) metadata search. Contrary to the Opendatacube and the Geotiffarchive, it doesn't require the pre-organising the data (e.g. to ingest the data into a database in case of the Opendatacube or to create a temporally stacked geotiff in case of the Geotiffarchive). Instead, the STACCube contains a retriever that knows how to fetch assets linked in STAC search results into a data cube. If the linked assets are provided in Cloud Optimizes GeoTiff format (CoGs), the STACCube offers a high-performance option for fetching and subsequently processing earth observation data only for the area and timespan of interest. To initialize a representation of such a data cube, you need to provide the STAC search results and valid layout dictionary. \n",
-    "Semantique contains a third built-in EO data cube configuration called [STACCube](https://zgis.github.io/semantique/semantique.datacube.STACCube.html). This configuration is targeted at ad-hoc data cubes built from the results of a [STAC](https://stacspec.org/en/) metadata search. Contrary to the Opendatacube and the Geotiffarchive, it doesn't require pre-organising the data (e.g. to ingest the data into a database in case of the Opendatacube or to create a temporally stacked geotiff in case of the Geotiffarchive). Instead, the STACCube contains a retriever that knows how to fetch assets linked in STAC search results into a data cube. If the linked assets are provided in Cloud Optimizes GeoTiff format (CoGs), the STACCube offers a high-performance option for fetching and subsequently processing earth observation data only for the area and timespan of interest. To initialize a representation of such a data cube, you need to provide the STAC search results and valid layout dictionary. \n",
-    "The STAC search results can be provided as an [pystac.item_collection.ItemCollection](https://pystac.readthedocs.io/en/stable/api/item_collection.html) or as an iterable of [pystac.item.Item](https://pystac.readthedocs.io/en/stable/api/item.html). It is important that the properties object for each item contains a datetime attribute that is non-null. Otherwise, the data can't be indexed temporally. The spatial index will be created based upon the asset-specific proj:epsg, proj:bbox, proj:shape, proj:transform attributes. \n",
+    "Semantique contains a third built-in EO data cube configuration called [STACCube](https://zgis.github.io/semantique/semantique.datacube.STACCube.html). This configuration is targeted at ad-hoc data cubes built from the results of a [STAC](https://stacspec.org/en/) metadata search. Contrary to the Opendatacube and the Geotiffarchive, it doesn't require pre-organising the data (e.g. to ingest the data into a database in case of the Opendatacube or to create a temporally stacked geotiff in case of the Geotiffarchive). Instead, the STACCube contains a retriever that knows how to fetch assets linked in STAC search results into a data cube. If the linked assets are provided in Cloud Optimizes GeoTiff format (CoGs), the STACCube offers a high-performance option for fetching and subsequently processing earth observation data only for the area and timespan of interest. \n",
+    "\n",
+    "To initialize a representation of such a data cube, you need to provide the STAC search results and valid layout dictionary. \n",
+    "The STAC search results can be provided as an [pystac.item_collection.ItemCollection](https://pystac.readthedocs.io/en/stable/api/item_collection.html) or as an iterable of [pystac.item.Item](https://pystac.readthedocs.io/en/stable/api/item.html). It is important that the properties object for each item contains a datetime attribute that is non-null. Otherwise, the data can't be indexed temporally. The spatial index will be created based upon the asset-specific proj:epsg, proj:bbox, proj:shape, proj:transform attributes. Optionally, you can add an extra field \"semantique:key\" on the asset level containing the relevant layout key for this data layer. This allows to resolve data references correctly in cases where multiple data layers with identical asset names should be used.\n",
     "\n",
     "A valid layout for this configuration means that the metadata objects for each data layer should contain at least the following keys and values:\n",
     "\n",
     "- `name`: The name of the data layer, called asset in STAC terminology.\n",
     "- `type`: The value type of the values in the data layer. There are no limits to what the value type categorization should be. If it differs from the [semantique value types](processor.ipynb#Tracking-value-types), this needs to be mapped through a configuration parameter when initializing the instance, see below.\n",
     "- `values`: An overview of the values in the data layer. If the values are numerical, this should be a dictionary containing the keys \"min\", \"max\" and \"precision\". If the values are categorical, this should be a list of dictionaries, with each dictionary referring to single category and containing the keys \"id\", \"label\" and \"description\".\n",
-    " \n",
-    "Additionally, you can provide several configuration parameters that tune the data retrieval process, see the [documentation](https://zgis.github.io/semantique/semantique.datacube.STACCube.html) of the STACCube class for details. Different to the Opendatacube and the Geotiffarchive, STACCube doesn't require to specify the timezone in which the temporal coordinates inside the data cube are stored. By default, the timezone is inferred from the STAC search results and converted to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time). \n",
+    "\n",
+    "Optionally, you can provide the following layout keys to guide how the data values are read in:\n",
+    "- `dtype`: The data type of the data. Setting the `dtype` to a value other than float32 (default) may speed-up the initial data reading, even though the data will be ultimately converted to floating values when calling .retrieve()  \n",
+    "- `na_value`: NA value of the data. This is especially important for categorical layers, where the NA value may be encoded as some integer value. In those cases, setting the NA value ensures that the data will be correctly aggregated when `group_by_solar_day` is set to True.  \n",
+    "\n",
+    "Additionally, several configuration parameters that tune the data retrieval process can be set during the initialisation of STACCube instances, see the [documentation](https://zgis.github.io/semantique/semantique.datacube.STACCube.html) of the STACCube class for details. Different to the Opendatacube and the Geotiffarchive, STACCube doesn't require to specify the timezone in which the temporal coordinates inside the data cube are stored. By default, the timezone is inferred from the STAC search results and converted to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time). \n",
     "\n",
     "We will demonstrate the initialization of an instance of the STACCube class using some Sentinel-2 data fetched from the [registry of open data on AWS](https://registry.opendata.aws/sentinel-2-l2a-cogs/). The layout file we use looks like [this](files/layout_stac.json)."
    ]
@@ -1228,6 +1233,7 @@
     }
    ],
    "source": [
+    "import pystac\n",
     "from pystac_client import Client\n",
     "from shapely.geometry import box\n",
     "\n",
@@ -1256,9 +1262,33 @@
     "gdf"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6cfdde3d",
+   "metadata": {},
+   "source": [
+    "As mentioned above, you can optionally add the asset-specific field \"semantique:key\" to the search results. Below this is demonstrated by associating the asset name \"scl\" with its corresponding layout key (\"appearance\", \"scl\"). Calling the .retrieve() method of the STACCube, it will then be checked if the reference keys of the queried layer matches one of the assets' \"semantique:key\" values. This implies that all data layers that should be retrievable via .retrieve() need to be registered with their layout keys in the \"semantique:key\" extra field of the corresponding assets. Below, only the scl layer is registered meaning that any attempt to retrieve another layer (e.g. the blue band) will fail.\n",
+    "\n",
+    "Note that the check within .retrieve() as described above will only be carried out, if this if the field \"semantique:key\" is specified for at least one of the assets of an item. If the \"semantique:key\" isn't specified at all, the check will be skipped and all items whose asset names are matching the referenced name of the queried layer (i.e. the content of the `name` field of the layout file) will be retrieved. Therefore, specifying \"semantique:key\" is only advisable if you have an ambiguity of asset names that makes it necessary to consider more than the pure asset name to resolve a data layer reference in a correct manner.   "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
+   "id": "16314073",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# optionally - write layout key to items\n",
+    "for item in item_coll:\n",
+    "    asset_dict = item.assets[\"scl\"].to_dict()\n",
+    "    asset_dict[\"semantique:key\"] = (\"appearance\", \"scl\") \n",
+    "    item.assets[\"scl\"] = pystac.asset.Asset.from_dict(asset_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "id": "694275fc",
    "metadata": {},
    "outputs": [
@@ -1628,7 +1658,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;scl&#x27; (time: 4, y: 500, x: 500)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;scl&#x27; (time: 4, y: 500, x: 500)&gt; Size: 4MB\n",
        "array([[[9., 9., 9., ..., 9., 9., 9.],\n",
        "        [9., 9., 9., ..., 9., 9., 9.],\n",
        "        [9., 9., 9., ..., 9., 9., 9.],\n",
@@ -1661,18 +1691,18 @@
        "        [6., 6., 6., ..., 6., 6., 6.],\n",
        "        [6., 6., 6., ..., 6., 6., 6.]]], dtype=float32)\n",
        "Coordinates:\n",
-       "  * x              (x) float64 -2.749 -2.748 -2.748 ... -2.252 -2.252 -2.25\n",
-       "  * y              (y) float64 47.75 47.75 47.75 47.75 ... 47.25 47.25 47.25\n",
-       "    temporal_ref   int32 0\n",
-       "  * time           (time) datetime64[ns] 2020-07-15 2020-07-20 ... 2020-07-30\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "  * x              (x) float64 4kB -2.749 -2.748 -2.748 ... -2.252 -2.252 -2.25\n",
+       "  * y              (y) float64 4kB 47.75 47.75 47.75 47.75 ... 47.25 47.25 47.25\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 32B 2020-07-15 ... 2020-07-30\n",
+       "    spatial_feats  (y, x) float64 2MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    spec:          RasterSpec(epsg=4326, bounds=(-2.75, 47.25, -2.25, 47.75),...\n",
        "    crs:           epsg:4326\n",
        "    transform:     | 0.00, 0.00,-2.75|\\n| 0.00,-0.00, 47.75|\\n| 0.00, 0.00, 1...\n",
        "    resolution:    0.001\n",
        "    value_type:    ordinal\n",
-       "    value_labels:  {0: &#x27;mask&#x27;, 1: &#x27;saturated&#x27;, 2: &#x27;dark&#x27;, 3: &#x27;shadow&#x27;, 4: &#x27;ve...</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'>'scl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>y</span>: 500</li><li><span class='xr-has-index'>x</span>: 500</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c98c72cf-d67a-4366-b7ac-d933066008c2' class='xr-array-in' type='checkbox' checked><label for='section-c98c72cf-d67a-4366-b7ac-d933066008c2' 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>9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 ... 4.0 4.0 6.0 6.0 6.0 6.0 6.0 6.0</span></div><div class='xr-array-data'><pre>array([[[9., 9., 9., ..., 9., 9., 9.],\n",
+       "    value_labels:  {0: &#x27;mask&#x27;, 1: &#x27;saturated&#x27;, 2: &#x27;dark&#x27;, 3: &#x27;shadow&#x27;, 4: &#x27;ve...</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'>'scl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>y</span>: 500</li><li><span class='xr-has-index'>x</span>: 500</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8beea569-5f44-4709-aaea-5dc0cada1f34' class='xr-array-in' type='checkbox' checked><label for='section-8beea569-5f44-4709-aaea-5dc0cada1f34' 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>9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 ... 4.0 4.0 6.0 6.0 6.0 6.0 6.0 6.0</span></div><div class='xr-array-data'><pre>array([[[9., 9., 9., ..., 9., 9., 9.],\n",
        "        [9., 9., 9., ..., 9., 9., 9.],\n",
        "        [9., 9., 9., ..., 9., 9., 9.],\n",
        "        ...,\n",
@@ -1702,15 +1732,15 @@
        "        ...,\n",
        "        [6., 6., 6., ..., 6., 6., 6.],\n",
        "        [6., 6., 6., ..., 6., 6., 6.],\n",
-       "        [6., 6., 6., ..., 6., 6., 6.]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b56636d6-49c0-4191-b5a1-c0ecf064361f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b56636d6-49c0-4191-b5a1-c0ecf064361f' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.749 -2.748 ... -2.252 -2.25</div><input id='attrs-7e58edd1-36e1-445e-b368-44cc7dfa52e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e58edd1-36e1-445e-b368-44cc7dfa52e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2ed0bc6-9e77-4937-bd60-14fcf3d3ec0f' class='xr-var-data-in' type='checkbox'><label for='data-c2ed0bc6-9e77-4937-bd60-14fcf3d3ec0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>resolution :</span></dt><dd>0.001</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([-2.7495, -2.7485, -2.7475, ..., -2.2525, -2.2515, -2.2505])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.75 47.75 47.75 ... 47.25 47.25</div><input id='attrs-eb835b79-681c-4ad9-9677-347a7c862444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb835b79-681c-4ad9-9677-347a7c862444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-462156dc-b557-4bcb-b301-c4c73ca0ebe9' class='xr-var-data-in' type='checkbox'><label for='data-462156dc-b557-4bcb-b301-c4c73ca0ebe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>resolution :</span></dt><dd>-0.001</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([47.7495, 47.7485, 47.7475, ..., 47.2525, 47.2515, 47.2505])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4237682c-df75-4ea5-be32-ef2f6b3c1e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4237682c-df75-4ea5-be32-ef2f6b3c1e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef160803-0803-4fe2-93db-3959fc844ab1' class='xr-var-data-in' type='checkbox'><label for='data-ef160803-0803-4fe2-93db-3959fc844ab1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2020-07-15 ... 2020-07-30</div><input id='attrs-610a6e70-b932-43ab-95dd-a6905410370b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-610a6e70-b932-43ab-95dd-a6905410370b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1eebe93-68a2-40d6-bdd8-cd60b1ae43e8' class='xr-var-data-in' type='checkbox'><label for='data-e1eebe93-68a2-40d6-bdd8-cd60b1ae43e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-15T00:00:00.000000000&#x27;, &#x27;2020-07-20T00:00:00.000000000&#x27;,\n",
+       "        [6., 6., 6., ..., 6., 6., 6.]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-c17161d3-4eb6-43ad-8473-100918aaed69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c17161d3-4eb6-43ad-8473-100918aaed69' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.749 -2.748 ... -2.252 -2.25</div><input id='attrs-23a4cebf-7038-4ed9-a721-96c97c464ca5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23a4cebf-7038-4ed9-a721-96c97c464ca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08f456c5-fac9-4a66-aecb-50ae53472958' class='xr-var-data-in' type='checkbox'><label for='data-08f456c5-fac9-4a66-aecb-50ae53472958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>resolution :</span></dt><dd>0.001</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([-2.7495, -2.7485, -2.7475, ..., -2.2525, -2.2515, -2.2505])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.75 47.75 47.75 ... 47.25 47.25</div><input id='attrs-811edfb1-ce36-4d29-ad34-b8a550f0f5a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-811edfb1-ce36-4d29-ad34-b8a550f0f5a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9b61d46-13f8-413e-8c1a-19ceda413200' class='xr-var-data-in' type='checkbox'><label for='data-d9b61d46-13f8-413e-8c1a-19ceda413200' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>resolution :</span></dt><dd>-0.001</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([47.7495, 47.7485, 47.7475, ..., 47.2525, 47.2515, 47.2505])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c6ae62ee-2814-4a0c-9fb9-bf6b3163763d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6ae62ee-2814-4a0c-9fb9-bf6b3163763d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fc86a37-9599-4ca0-b12c-9f6e067c2fb3' class='xr-var-data-in' type='checkbox'><label for='data-3fc86a37-9599-4ca0-b12c-9f6e067c2fb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2020-07-15 ... 2020-07-30</div><input id='attrs-37976999-3469-448f-8805-eeaf0a7f3e95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37976999-3469-448f-8805-eeaf0a7f3e95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382e71d6-0bcf-4311-a570-bf4f8160d0e4' class='xr-var-data-in' type='checkbox'><label for='data-382e71d6-0bcf-4311-a570-bf4f8160d0e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-15T00:00:00.000000000&#x27;, &#x27;2020-07-20T00:00:00.000000000&#x27;,\n",
        "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-baea2c57-0821-4496-a46a-b87272953108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-baea2c57-0821-4496-a46a-b87272953108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f1f9b81-60a7-44ec-a322-876a90e541c1' class='xr-var-data-in' type='checkbox'><label for='data-8f1f9b81-60a7-44ec-a322-876a90e541c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., ..., 1., 1., 1.],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-5eae0a7e-5fc4-4cb1-bf4e-a0d2adce4802' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eae0a7e-5fc4-4cb1-bf4e-a0d2adce4802' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47b67ec-cd94-4ecf-9ea2-ee21b365cd46' class='xr-var-data-in' type='checkbox'><label for='data-a47b67ec-cd94-4ecf-9ea2-ee21b365cd46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., ..., 1., 1., 1.],\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
        "       ...,\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
        "       [1., 1., 1., ..., 1., 1., 1.],\n",
-       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13be88f8-f1fd-495a-9d6f-38cb1bfb5447' class='xr-section-summary-in' type='checkbox'  ><label for='section-13be88f8-f1fd-495a-9d6f-38cb1bfb5447' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-839d0e48-5a91-44ed-af5e-ecd2e0931767' class='xr-index-data-in' type='checkbox'/><label for='index-839d0e48-5a91-44ed-af5e-ecd2e0931767' 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([            -2.7495,             -2.7485,             -2.7475,\n",
+       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eace18ac-767f-4254-af38-569aff3766ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-eace18ac-767f-4254-af38-569aff3766ed' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02aa67c8-fee9-4924-a02e-59aaf599bcac' class='xr-index-data-in' type='checkbox'/><label for='index-02aa67c8-fee9-4924-a02e-59aaf599bcac' 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([            -2.7495,             -2.7485,             -2.7475,\n",
        "       -2.7464999999999997,             -2.7455,             -2.7445,\n",
        "                   -2.7435, -2.7424999999999997,             -2.7415,\n",
        "                   -2.7405,\n",
@@ -1719,7 +1749,7 @@
        "                   -2.2565, -2.2554999999999996, -2.2544999999999997,\n",
        "                   -2.2535,             -2.2525,             -2.2515,\n",
        "       -2.2504999999999997],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c6f9dc0-1bbf-44a8-82ff-9ebf3d56628f' class='xr-index-data-in' type='checkbox'/><label for='index-9c6f9dc0-1bbf-44a8-82ff-9ebf3d56628f' 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([           47.7495,            47.7485, 47.747499999999995,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff423229-7582-4df3-bec3-f2a946e97a1d' class='xr-index-data-in' type='checkbox'/><label for='index-ff423229-7582-4df3-bec3-f2a946e97a1d' 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([           47.7495,            47.7485, 47.747499999999995,\n",
        "                  47.7465,            47.7455, 47.744499999999995,\n",
        "                  47.7435,            47.7425, 47.741499999999995,\n",
        "                  47.7405,\n",
@@ -1728,12 +1758,12 @@
        "       47.256499999999996,            47.2555,            47.2545,\n",
        "       47.253499999999995,            47.2525,            47.2515,\n",
        "       47.250499999999995],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=500))</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-31ced7d4-0ec8-464d-935a-a2067884d781' class='xr-index-data-in' type='checkbox'/><label for='index-31ced7d4-0ec8-464d-935a-a2067884d781' 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;2020-07-15&#x27;, &#x27;2020-07-20&#x27;, &#x27;2020-07-25&#x27;, &#x27;2020-07-30&#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-80cb7bfe-9c4b-4fa4-ae42-99721277daca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80cb7bfe-9c4b-4fa4-ae42-99721277daca' 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>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-2.75, 47.25, -2.25, 47.75), resolutions_xy=(0.001, 0.001))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-2.75|\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=500))</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-1993c7c4-8ade-417a-a4bb-d415be823cfe' class='xr-index-data-in' type='checkbox'/><label for='index-1993c7c4-8ade-417a-a4bb-d415be823cfe' 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;2020-07-15&#x27;, &#x27;2020-07-20&#x27;, &#x27;2020-07-25&#x27;, &#x27;2020-07-30&#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-0c321057-9507-4445-9221-c4fd1c2b3549' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c321057-9507-4445-9221-c4fd1c2b3549' 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>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-2.75, 47.25, -2.25, 47.75), resolutions_xy=(0.001, 0.001))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-2.75|\n",
        "| 0.00,-0.00, 47.75|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.001</dd><dt><span>value_type :</span></dt><dd>ordinal</dd><dt><span>value_labels :</span></dt><dd>{0: &#x27;mask&#x27;, 1: &#x27;saturated&#x27;, 2: &#x27;dark&#x27;, 3: &#x27;shadow&#x27;, 4: &#x27;vegetation&#x27;, 5: &#x27;bare&#x27;, 6: &#x27;water&#x27;, 7: &#x27;unknown&#x27;, 8: &#x27;cl_med&#x27;, 9: &#x27;cl_high&#x27;, 10: &#x27;cl_cirrus&#x27;, 11: &#x27;snow&#x27;}</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'scl' (time: 4, y: 500, x: 500)>\n",
+       "<xarray.DataArray 'scl' (time: 4, y: 500, x: 500)> Size: 4MB\n",
        "array([[[9., 9., 9., ..., 9., 9., 9.],\n",
        "        [9., 9., 9., ..., 9., 9., 9.],\n",
        "        [9., 9., 9., ..., 9., 9., 9.],\n",
@@ -1766,11 +1796,11 @@
        "        [6., 6., 6., ..., 6., 6., 6.],\n",
        "        [6., 6., 6., ..., 6., 6., 6.]]], dtype=float32)\n",
        "Coordinates:\n",
-       "  * x              (x) float64 -2.749 -2.748 -2.748 ... -2.252 -2.252 -2.25\n",
-       "  * y              (y) float64 47.75 47.75 47.75 47.75 ... 47.25 47.25 47.25\n",
-       "    temporal_ref   int32 0\n",
-       "  * time           (time) datetime64[ns] 2020-07-15 2020-07-20 ... 2020-07-30\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "  * x              (x) float64 4kB -2.749 -2.748 -2.748 ... -2.252 -2.252 -2.25\n",
+       "  * y              (y) float64 4kB 47.75 47.75 47.75 47.75 ... 47.25 47.25 47.25\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 32B 2020-07-15 ... 2020-07-30\n",
+       "    spatial_feats  (y, x) float64 2MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    spec:          RasterSpec(epsg=4326, bounds=(-2.75, 47.25, -2.25, 47.75),...\n",
        "    crs:           epsg:4326\n",
@@ -1780,7 +1810,7 @@
        "    value_labels:  {0: 'mask', 1: 'saturated', 2: 'dark', 3: 'shadow', 4: 've..."
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1809,13 +1839,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "f861e35a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x300 with 5 Axes>"
       ]
diff --git a/demo/extras/cache_tests.ipynb b/demo/extras/cache_tests.ipynb
index bf790f9b..517e255e 100644
--- a/demo/extras/cache_tests.ipynb
+++ b/demo/extras/cache_tests.ipynb
@@ -21,24 +21,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\felix\\AppData\\Roaming\\Python\\Python310\\site-packages\\geopandas\\_compat.py:124: UserWarning: The Shapely GEOS version (3.11.2-CAPI-1.17.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\felix\\AppData\\Local\\Temp/ipykernel_7388/2560623581.py:1: DeprecationWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas still uses PyGEOS by default. However, starting with version 0.14, the default will switch to Shapely. To force to use Shapely 2.0 now, you can either uninstall PyGEOS or set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n",
-      "\n",
-      "import os\n",
-      "os.environ['USE_PYGEOS'] = '0'\n",
-      "import geopandas\n",
-      "\n",
-      "In the next release, GeoPandas will switch to using Shapely by default, even if PyGEOS is installed. If you only have PyGEOS installed to get speed-ups, this switch should be smooth. However, if you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).\n",
-      "  import geopandas as gpd\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import geopandas as gpd\n",
     "import json\n",
@@ -55,15 +38,15 @@
    "outputs": [],
    "source": [
     "# Load a mapping.\n",
-    "with open(\"files/mapping.json\", \"r\") as file:\n",
+    "with open(\"../files/mapping.json\", \"r\") as file:\n",
     "    mapping = sq.mapping.Semantique(json.load(file))\n",
     "\n",
     "# Represent an EO data cube.\n",
-    "with open(\"files/layout.json\", \"r\") as file:\n",
-    "    dc = sq.datacube.GeotiffArchive(json.load(file), src = \"files/layers.zip\")\n",
+    "with open(\"../files/layout_gtiff.json\", \"r\") as file:\n",
+    "    dc = sq.datacube.GeotiffArchive(json.load(file), src = \"../files/layers_gtiff.zip\")\n",
     "\n",
     "# Set the spatio-temporal extent.\n",
-    "space = sq.SpatialExtent(gpd.read_file(\"files/footprint.geojson\"))\n",
+    "space = sq.SpatialExtent(gpd.read_file(\"../files/footprint.geojson\"))\n",
     "time = sq.TemporalExtent(\"2019-01-01\", \"2020-12-31\")"
    ]
   },
@@ -78,9 +61,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "RAM memory requirements are proportional to the number of data layers that are stored as intermediate results. Caching data layers in RAM should only be done for those that are needed again when evaluating downstream parts of the recipe. This requires foresight about the evaluation order of the recipe, which accordingly requires a preview run preceding the actual evaluation. This preview run is performed by loading the data with drastically reduced spatial resolution (5x5 pixel grid). It resolves the data references and fills a cache by creating a list of the data references in the order in which they are evaluated. This list is then used dynamically during the actual evaluation of the recipe as a basis for keeping data layers in the cache and reading them from there if they are needed again.\n",
-    "\n",
-    "Below the result of the preview run is shown first to demonstrate what the resolved data references look like. The resulting initialised cache can then be fed as a context element to the QueryProcessor in a second step for the actual recipe execution."
+    "Caching data layers in RAM should only be done for those that are needed again when evaluating downstream parts of the recipe. This requires foresight about the execution order of the recipe, which accordingly requires a simulated run preceding the actual execution. This simulated run is performed by the FakeProcessor. It resolves the data references and fills a cache by creating a list of the data references in the order in which they are evaluated. This list is then used dynamically during the actual execution of the recipe as a basis for keeping data layers in the cache and reading them from there if they are needed again."
    ]
   },
   {
@@ -89,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from semantique.processor.core import QueryProcessor\n",
+    "from semantique.processor.core import FakeProcessor, QueryProcessor\n",
     "\n",
     "# define a simple recipe for a cloudfree composite\n",
     "recipe = sq.QueryRecipe()\n",
@@ -133,10 +114,10 @@
     }
    ],
    "source": [
-    "# step I: preview run\n",
-    "qp = QueryProcessor.parse(recipe, **{**context, \"preview\": True})\n",
-    "qp.optimize().execute()\n",
-    "qp.cache.seq"
+    "# step I: fake run\n",
+    "fp = FakeProcessor.parse(recipe, **context)\n",
+    "fp.optimize().execute()\n",
+    "fp.cache.seq"
    ]
   },
   {
@@ -157,7 +138,7 @@
    ],
    "source": [
     "# step II: query processor execution\n",
-    "qp = QueryProcessor.parse(recipe, **{**context, \"cache\": qp.cache})\n",
+    "qp = QueryProcessor.parse(recipe, **{**context, \"cache\": fp.cache})\n",
     "result = qp.optimize().execute()\n",
     "result[\"composite\"].shape"
    ]
@@ -166,9 +147,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see the preview run resolves the references to the data layers as they are provided by looking up the entities' references in the mapping.json. Note, that in the current case the result is not that interesting, though, since four different data layers are to be loaded. Therefore, there is nothing to be cached during recipe execution. Therefore the QueryProcessor will load all data layers from the referenced sources without storing any of them in the cache. \n",
+    "As you can see the FakeProcessor run resolves the references to the data layers as they are provided by looking up the entities' references in the mapping.json. Note, that in the current case the result is not that interesting, though, since four different data layers are to be loaded. Therefore, there is nothing to be cached during recipe execution. Therefore the QueryProcessor will load all data layers from the referenced sources without storing any of them in the cache. \n",
     "\n",
-    "As a user, however, you can directly initiate the entire caching workflow (preview & full resolution recipe execution) by setting the context parameter when calling `recipe.execute(..., cache_data = True)`. "
+    "As a user, however, you can directly initiate the entire caching workflow (preview & full resolution recipe execution) by setting the context parameter when calling `recipe.execute(..., cache_data = True)`. This is enabled by default."
    ]
   },
   {
@@ -196,7 +177,7 @@
     "* the redundancy of the data references in the recipe, i.e. if layers are called multiple times loading them from cache will reduce the overall time significantly\n",
     "* the data source (EO data cube) from which they are loaded\n",
     "\n",
-    "Especially for the later it should be noted that in this demo only data loaded from a locally stored geotiff (i.e. the GeoTiffArchive layout) are analysed. This is sort of the worst case for demonstrating the benefits of caching since the data is stored locally and is therfore quickly accessible. Also geotiffs that are not stored in cloud-optimised format (CoGs) require to load the whole data into memory even when running in preview mode just to evaluate the sequence of data layers.\n",
+    "Especially for the later it should be noted that in this demo only data loaded from a locally stored geotiff (i.e. the GeoTiffArchive layout) are analysed. This is sort of the worst case for demonstrating the benefits of caching since the data is stored locally and is therfore quickly accessible.\n",
     "\n",
     "Consequently, you will observe that in almost all of the following cases, caching actually adds a small computational overhead. Keep in mind, however, that caching is designed for and particularly beneficial in case of STACCubes when loading data over the internet."
    ]
@@ -217,7 +198,7 @@
     "        \"crs\": 3035, \n",
     "        \"tz\": \"UTC\", \n",
     "        \"spatial_resolution\": [-10, 10],\n",
-    "        \"caching\": caching\n",
+    "        \"cache_data\": caching\n",
     "    }\n",
     "    res = recipe.execute(**context)"
    ]
@@ -248,7 +229,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "649 ms ± 14.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "640 ms ± 3.41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -267,7 +248,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "998 ms ± 5.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "703 ms ± 18.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -300,7 +281,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.09 s ± 61.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "5.28 s ± 72.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -319,7 +300,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.27 s ± 51.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "5.51 s ± 106 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -352,7 +333,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "499 ms ± 5.31 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "495 ms ± 7.52 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -371,7 +352,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "547 ms ± 4.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "283 ms ± 1.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -385,139 +366,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The more expressive examples for the STACCube are provided below. Note that they can't be executed for now (as STACCube in currently still under dev and not yet merged in the main branch). The question if caching brings significant advantages when loading data from a well-indexed OpenDataCube stored on a quickly accessible hot storage, remains to be assessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pystac_client import Client\n",
-    "from shapely.geometry import box\n",
-    "from semantique.processor.core import QueryProcessor\n",
-    "import warnings\n",
-    "\n",
-    "# define temporal & spatial range to perform STAC query\n",
-    "xmin, ymin, xmax, ymax = 13.25,54.25,13.75,54.75\n",
-    "aoi = box(xmin, ymin, xmax, ymax)\n",
-    "t_range = [\"2020-07-15\", \"2020-09-01\"]\n",
-    "\n",
-    "# STAC-based metadata retrieval\n",
-    "import planetary_computer as pc\n",
-    "platform = \"Planet\"\n",
-    "catalog = Client.open(\n",
-    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
-    "    modifier=pc.sign_inplace,\n",
-    ")\n",
-    "query = catalog.search(\n",
-    "    collections=\"sentinel-2-l2a\", \n",
-    "    datetime=t_range, \n",
-    "    limit=100, \n",
-    "    intersects=aoi\n",
-    ")\n",
-    "item_coll = query.item_collection()\n",
-    "\n",
-    "# define datacube\n",
-    "with open(\"layout_planet.json\", \"r\") as file:\n",
-    "    dc = sq.datacube.STACCube(\n",
-    "        json.load(file), \n",
-    "        src = item_coll,\n",
-    "        dtype=\"int8\",\n",
-    "        na_value=0,\n",
-    "        )\n",
-    "        \n",
-    "# define spatio-temporal context vars \n",
-    "res = 20\n",
-    "epsg = 3035\n",
-    "space = sq.SpatialExtent(gpd.GeoDataFrame(geometry=[aoi], crs = 4326))\n",
-    "time = sq.TemporalExtent(*t_range)\n",
-    "\n",
-    "# load mapping\n",
-    "with open(\"mapping.json\", \"r\") as file:\n",
-    "    rules = json.load(file)\n",
-    "mapping = sq.mapping.Semantique(rules)\n",
-    "\n",
-    "# define recipe\n",
-    "recipe = sq.QueryRecipe()\n",
-    "recipe[\"green_map\"] = (\n",
-    "    sq.entity(\"vegetation\")\n",
-    "    .filter(sq.entity(\"cloud\").evaluate(\"not\"))\n",
-    "    .reduce(\"percentage\", \"time\")\n",
-    ")\n",
-    "recipe[\"all_count\"] = (\n",
-    "    sq.entity(\"all\")\n",
-    "    .reduce(\"count\", \"time\")\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# normal execution (no caching/no preview)\n",
-    "context = {\n",
-    "    \"datacube\": dc,\n",
-    "    \"mapping\": mapping,\n",
-    "    \"space\": space,\n",
-    "    \"time\": time,\n",
-    "    \"crs\": epsg,\n",
-    "    \"tz\": \"UTC\",\n",
-    "    \"spatial_resolution\": [-res, res]\n",
-    "}\n",
-    "\n",
-    "with warnings.catch_warnings():\n",
-    "    warnings.simplefilter(\"ignore\", UserWarning)\n",
-    "    response = recipe.execute(**context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# preview mode\n",
-    "context = {\n",
-    "    \"datacube\": dc,\n",
-    "    \"mapping\": mapping,\n",
-    "    \"space\": space,\n",
-    "    \"time\": time,\n",
-    "    \"crs\": epsg,\n",
-    "    \"tz\": \"UTC\",\n",
-    "    \"spatial_resolution\": [-res, res],\n",
-    "    \"preview\": True\n",
-    "}\n",
-    "\n",
-    "with warnings.catch_warnings():\n",
-    "    warnings.simplefilter(\"ignore\", UserWarning)\n",
-    "    response = recipe.execute(**context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# caching mode\n",
-    "context = {\n",
-    "    \"datacube\": dc,\n",
-    "    \"mapping\": mapping,\n",
-    "    \"space\": space,\n",
-    "    \"time\": time,\n",
-    "    \"crs\": epsg,\n",
-    "    \"tz\": \"UTC\",\n",
-    "    \"spatial_resolution\": [-res, res],\n",
-    "    \"caching\": True\n",
-    "}\n",
-    "\n",
-    "with warnings.catch_warnings():\n",
-    "    warnings.simplefilter(\"ignore\", UserWarning)\n",
-    "    response = recipe.execute(**context)"
+    "The more expressive examples for the STACCube are provided below. The question if caching brings significant advantages when loading data from a well-indexed OpenDataCube stored on a quickly accessible hot storage, remains to be assessed. "
    ]
   }
  ],
@@ -537,7 +386,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,
diff --git a/demo/files/layout_stac.json b/demo/files/layout_stac.json
index 879c78cf..29b3b718 100644
--- a/demo/files/layout_stac.json
+++ b/demo/files/layout_stac.json
@@ -10,7 +10,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band02": {
@@ -23,7 +23,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "role": "blue",
             "copyright": "Contains modified Copernicus data."
         },
@@ -37,7 +37,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "role": "green",
             "copyright": "Contains modified Copernicus data."
         },
@@ -51,7 +51,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "role": "red",
             "copyright": "Contains modified Copernicus data."
         },
@@ -65,7 +65,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band06": {
@@ -78,7 +78,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band07": {
@@ -91,7 +91,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band08": {
@@ -104,7 +104,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band08A": {
@@ -117,7 +117,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band09": {
@@ -130,7 +130,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band11": {
@@ -143,7 +143,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         },
         "s2_band12": {
@@ -156,7 +156,7 @@
                 "precision": 1
             },
             "dtype": "float32",
-            "na_vals": "NA",
+            "na_value": "NA",
             "copyright": "Contains modified Copernicus data."
         }
     },
@@ -228,7 +228,7 @@
                 }
             ],
             "dtype": "int8",
-            "na_vals": 0
+            "na_value": 0
         }
     }
 }
\ No newline at end of file
diff --git a/demo/processor.ipynb b/demo/processor.ipynb
index 1fc712db..2e3112ce 100644
--- a/demo/processor.ipynb
+++ b/demo/processor.ipynb
@@ -31,7 +31,7 @@
    "outputs": [],
    "source": [
     "import semantique as sq\n",
-    "from semantique.processor.core import QueryProcessor"
+    "from semantique.processor.core import QueryProcessor, FakeProcessor"
    ]
   },
   {
@@ -569,7 +569,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;index&#x27; (time: 2, y: 4, x: 4)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;index&#x27; (time: 2, y: 4, x: 4)&gt; Size: 256B\n",
        "array([[[1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
@@ -580,18 +580,18 @@
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.]]])\n",
        "Coordinates:\n",
-       "  * time           (time) datetime64[ns] 2019-01-01 2020-12-31\n",
-       "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "    spatial_ref    int64 0\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
-       "    temporal_ref   int64 0\n",
+       "  * time           (time) datetime64[ns] 16B 2019-01-01 2020-12-31\n",
+       "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "    spatial_ref    int32 4B 0\n",
+       "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
+       "    temporal_ref   int32 4B 0\n",
        "Attributes:\n",
        "    name:          index\n",
        "    long_name:     index\n",
        "    _FillValue:    nan\n",
        "    value_type:    nominal\n",
-       "    value_labels:  {1: &#x27;feature_1&#x27;}</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'>'index'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>y</span>: 4</li><li><span class='xr-has-index'>x</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d638407b-839a-4a2f-acf1-101b13cacc6c' class='xr-array-in' type='checkbox' checked><label for='section-d638407b-839a-4a2f-acf1-101b13cacc6c' 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>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([[[1., 1., 1., 1.],\n",
+       "    value_labels:  {1: &#x27;feature_1&#x27;}</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'>'index'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>y</span>: 4</li><li><span class='xr-has-index'>x</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-872b46d3-3378-4dae-81d4-c4f331178dfe' class='xr-array-in' type='checkbox' checked><label for='section-872b46d3-3378-4dae-81d4-c4f331178dfe' 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>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([[[1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.]],\n",
@@ -599,14 +599,14 @@
        "       [[1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
-       "        [1., 1., 1., 1.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-0672f5e6-fffd-4ffd-a8b5-43f144bea552' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0672f5e6-fffd-4ffd-a8b5-43f144bea552' class='xr-section-summary' >Coordinates: <span>(6)</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'>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'>2019-01-01 2020-12-31</div><input id='attrs-a872e4b4-0d2a-46a8-ae02-3471fafdb5d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a872e4b4-0d2a-46a8-ae02-3471fafdb5d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f30e4b73-fada-4ee6-bf30-60f6a8d1003a' class='xr-var-data-in' type='checkbox'><label for='data-f30e4b73-fada-4ee6-bf30-60f6a8d1003a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.696e+06 2.695e+06 ... 2.692e+06</div><input id='attrs-db87719b-23e9-4c83-b6b8-ef4ffaefbc94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db87719b-23e9-4c83-b6b8-ef4ffaefbc94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cd51834-de2a-4f6e-9c6a-a97b577bc177' class='xr-var-data-in' type='checkbox'><label for='data-2cd51834-de2a-4f6e-9c6a-a97b577bc177' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>value_type :</span></dt><dd>continuous</dd><dt><span>resolution :</span></dt><dd>-1500</dd></dl></div><div class='xr-var-data'><pre>array([2696250., 2694750., 2693250., 2691750.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.531e+06 4.532e+06 ... 4.535e+06</div><input id='attrs-9c388dd8-e1f1-4466-8c7c-16b80e871a0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c388dd8-e1f1-4466-8c7c-16b80e871a0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1121431-c785-404e-9170-10fe004d61d0' class='xr-var-data-in' type='checkbox'><label for='data-e1121431-c785-404e-9170-10fe004d61d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>value_type :</span></dt><dd>continuous</dd><dt><span>resolution :</span></dt><dd>1500</dd></dl></div><div class='xr-var-data'><pre>array([4530750., 4532250., 4533750., 4535250.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ae9b09ce-67eb-4ab5-addf-9b4432a84a84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae9b09ce-67eb-4ab5-addf-9b4432a84a84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fc3d851-10a3-4b0e-8db3-cde461207d6b' class='xr-var-data-in' type='checkbox'><label for='data-4fc3d851-10a3-4b0e-8db3-cde461207d6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530000.0 1500.0 0.0 2697000.0 0.0 -1500.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-a020d98f-4703-4bcf-bde0-615b568847b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a020d98f-4703-4bcf-bde0-615b568847b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57112936-f2a5-453e-a450-0aa93717f496' class='xr-var-data-in' type='checkbox'><label for='data-57112936-f2a5-453e-a450-0aa93717f496' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-b2fc2e41-2570-4a39-afa5-244e7fc46733' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2fc2e41-2570-4a39-afa5-244e7fc46733' class='xr-section-summary' >Coordinates: <span>(6)</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'>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'>2019-01-01 2020-12-31</div><input id='attrs-848b3fa9-6d6c-435c-b9bc-79bcb2248c92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-848b3fa9-6d6c-435c-b9bc-79bcb2248c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d73691ec-c8a0-42dd-b2d2-74b0730e6964' class='xr-var-data-in' type='checkbox'><label for='data-d73691ec-c8a0-42dd-b2d2-74b0730e6964' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.696e+06 2.695e+06 ... 2.692e+06</div><input id='attrs-b15ce65c-af99-406a-a38e-5f4443fde60d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b15ce65c-af99-406a-a38e-5f4443fde60d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9069047-6636-4f20-ab06-70844f5bc774' class='xr-var-data-in' type='checkbox'><label for='data-a9069047-6636-4f20-ab06-70844f5bc774' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>value_type :</span></dt><dd>continuous</dd><dt><span>resolution :</span></dt><dd>-1500</dd></dl></div><div class='xr-var-data'><pre>array([2696250., 2694750., 2693250., 2691750.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.531e+06 4.532e+06 ... 4.535e+06</div><input id='attrs-f1ea9450-6424-4c39-933b-8350ccf423e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1ea9450-6424-4c39-933b-8350ccf423e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf078c5e-d0af-498b-a70c-34ba295ba1b7' class='xr-var-data-in' type='checkbox'><label for='data-bf078c5e-d0af-498b-a70c-34ba295ba1b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>value_type :</span></dt><dd>continuous</dd><dt><span>resolution :</span></dt><dd>1500</dd></dl></div><div class='xr-var-data'><pre>array([4530750., 4532250., 4533750., 4535250.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c3fa82c5-20ce-4a23-baf2-d59df10217d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3fa82c5-20ce-4a23-baf2-d59df10217d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea17274f-732a-404a-827e-0ebbc60d8a1a' class='xr-var-data-in' type='checkbox'><label for='data-ea17274f-732a-404a-827e-0ebbc60d8a1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530000.0 1500.0 0.0 2697000.0 0.0 -1500.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-0940c80a-f412-41ab-b91d-d0ec5c0c38eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0940c80a-f412-41ab-b91d-d0ec5c0c38eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8febc9cb-1521-4ff6-949e-74ef5fe5fd4a' class='xr-var-data-in' type='checkbox'><label for='data-8febc9cb-1521-4ff6-949e-74ef5fe5fd4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., 1.],\n",
        "       [1., 1., 1., 1.],\n",
        "       [1., 1., 1., 1.],\n",
-       "       [1., 1., 1., 1.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ea5083d3-6429-4ddb-a859-11390035f7e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea5083d3-6429-4ddb-a859-11390035f7e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7b2ff05-9aff-40d7-948f-a8a10684ae7d' class='xr-var-data-in' type='checkbox'><label for='data-c7b2ff05-9aff-40d7-948f-a8a10684ae7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0b7c3c4-7b7d-4523-9540-fa070308c17e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e0b7c3c4-7b7d-4523-9540-fa070308c17e' 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>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3e7d11e0-bc6f-4b0d-ad74-7ad669677b87' class='xr-index-data-in' type='checkbox'/><label for='index-3e7d11e0-bc6f-4b0d-ad74-7ad669677b87' 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([2696250.0, 2694750.0, 2693250.0, 2691750.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b71831f6-3be5-4983-bf16-8c0726cb7c50' class='xr-index-data-in' type='checkbox'/><label for='index-b71831f6-3be5-4983-bf16-8c0726cb7c50' 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([4530750.0, 4532250.0, 4533750.0, 4535250.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</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-b9c0e230-80a4-4ad4-a1e1-abde6bb7c65f' class='xr-index-data-in' type='checkbox'/><label for='index-b9c0e230-80a4-4ad4-a1e1-abde6bb7c65f' 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;2019-01-01&#x27;, &#x27;2020-12-31&#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-31bce36a-b755-4fcc-a770-78afd68bc0d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31bce36a-b755-4fcc-a770-78afd68bc0d3' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>index</dd><dt><span>long_name :</span></dt><dd>index</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div></li></ul></div></div>"
+       "       [1., 1., 1., 1.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-87ffcb2d-3d3d-41fc-ae58-effe0b067f8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87ffcb2d-3d3d-41fc-ae58-effe0b067f8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acff1814-3794-4015-8186-4f079d6b91d7' class='xr-var-data-in' type='checkbox'><label for='data-acff1814-3794-4015-8186-4f079d6b91d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c50c373-bda9-458f-8c66-fdcbe58f309b' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c50c373-bda9-458f-8c66-fdcbe58f309b' 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>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a019496a-d38f-4ecf-a95f-03a5fae453a4' class='xr-index-data-in' type='checkbox'/><label for='index-a019496a-d38f-4ecf-a95f-03a5fae453a4' 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([2696250.0, 2694750.0, 2693250.0, 2691750.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4b5d01d-525d-4a4c-a18d-0f82f58ea3d6' class='xr-index-data-in' type='checkbox'/><label for='index-c4b5d01d-525d-4a4c-a18d-0f82f58ea3d6' 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([4530750.0, 4532250.0, 4533750.0, 4535250.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</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-710e35d1-799b-4939-b7e2-bb99923a1197' class='xr-index-data-in' type='checkbox'/><label for='index-710e35d1-799b-4939-b7e2-bb99923a1197' 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;2019-01-01&#x27;, &#x27;2020-12-31&#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-2b5560db-0eab-448d-83e1-944130c25ab0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b5560db-0eab-448d-83e1-944130c25ab0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>index</dd><dt><span>long_name :</span></dt><dd>index</dd><dt><span>_FillValue :</span></dt><dd>nan</dd><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'index' (time: 2, y: 4, x: 4)>\n",
+       "<xarray.DataArray 'index' (time: 2, y: 4, x: 4)> Size: 256B\n",
        "array([[[1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.],\n",
@@ -617,12 +617,12 @@
        "        [1., 1., 1., 1.],\n",
        "        [1., 1., 1., 1.]]])\n",
        "Coordinates:\n",
-       "  * time           (time) datetime64[ns] 2019-01-01 2020-12-31\n",
-       "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "    spatial_ref    int64 0\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
-       "    temporal_ref   int64 0\n",
+       "  * time           (time) datetime64[ns] 16B 2019-01-01 2020-12-31\n",
+       "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "    spatial_ref    int32 4B 0\n",
+       "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
+       "    temporal_ref   int32 4B 0\n",
        "Attributes:\n",
        "    name:          index\n",
        "    long_name:     index\n",
@@ -712,60 +712,60 @@
     {
      "data": {
       "text/plain": [
-       "{'blue_map': <xarray.DataArray 'blue_map' (y: 4, x: 4)>\n",
+       "{'blue_map': <xarray.DataArray 'blue_map' (y: 4, x: 4)> Size: 128B\n",
        " array([[1., 0., 0., 0.],\n",
        "        [0., 0., 0., 1.],\n",
        "        [1., 0., 1., 0.],\n",
        "        [0., 0., 0., 0.]])\n",
        " Coordinates:\n",
-       "   * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "   * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "     spatial_ref    int64 0\n",
-       "     temporal_ref   int64 0\n",
-       "     spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "     spatial_ref    int32 4B 0\n",
+       "   * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "   * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "     temporal_ref   int32 4B 0\n",
+       "     spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        " Attributes:\n",
        "     value_type:  discrete,\n",
-       " 'green_map': <xarray.DataArray 'green_map' (y: 4, x: 4)>\n",
+       " 'green_map': <xarray.DataArray 'green_map' (y: 4, x: 4)> Size: 128B\n",
        " array([[1., 2., 1., 2.],\n",
        "        [2., 2., 0., 1.],\n",
        "        [1., 1., 0., 1.],\n",
        "        [2., 0., 1., 2.]])\n",
        " Coordinates:\n",
-       "   * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "   * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "     spatial_ref    int64 0\n",
-       "     temporal_ref   int64 0\n",
-       "     spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "     spatial_ref    int32 4B 0\n",
+       "   * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "   * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "     temporal_ref   int32 4B 0\n",
+       "     spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        " Attributes:\n",
        "     value_type:  discrete,\n",
-       " 'blue_curve': <xarray.DataArray 'blue_curve' (time: 3)>\n",
+       " 'blue_curve': <xarray.DataArray 'blue_curve' (time: 3)> Size: 24B\n",
        " array([0., 1., 3.])\n",
        " Coordinates:\n",
-       "     spatial_ref   int64 0\n",
-       "   * time          (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-1...\n",
-       "     temporal_ref  int64 0\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
+       "   * time          (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 20...\n",
        " Attributes:\n",
        "     value_type:  discrete,\n",
-       " 'green_curve': <xarray.DataArray 'green_curve' (time: 3)>\n",
+       " 'green_curve': <xarray.DataArray 'green_curve' (time: 3)> Size: 24B\n",
        " array([ 0., 13.,  6.])\n",
        " Coordinates:\n",
-       "     spatial_ref   int64 0\n",
-       "   * time          (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-1...\n",
-       "     temporal_ref  int64 0\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
+       "   * time          (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 20...\n",
        " Attributes:\n",
        "     value_type:  discrete,\n",
-       " 'blue_stat': <xarray.DataArray 'blue_stat' ()>\n",
+       " 'blue_stat': <xarray.DataArray 'blue_stat' ()> Size: 8B\n",
        " array(4.)\n",
        " Coordinates:\n",
-       "     spatial_ref   int64 0\n",
-       "     temporal_ref  int64 0\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
        " Attributes:\n",
        "     value_type:  discrete,\n",
-       " 'green_stat': <xarray.DataArray 'green_stat' ()>\n",
+       " 'green_stat': <xarray.DataArray 'green_stat' ()> Size: 8B\n",
        " array(19.)\n",
        " Coordinates:\n",
-       "     spatial_ref   int64 0\n",
-       "     temporal_ref  int64 0\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
        " Attributes:\n",
        "     value_type:  discrete}"
       ]
@@ -1161,7 +1161,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vegetation&#x27; (time: 3, y: 4, x: 4)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vegetation&#x27; (time: 3, y: 4, x: 4)&gt; Size: 384B\n",
        "array([[[0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
@@ -1177,18 +1177,18 @@
        "        [0., 0., 0., 0.],\n",
        "        [1., 0., 0., 1.]]])\n",
        "Coordinates:\n",
-       "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "    spatial_ref    int64 0\n",
-       "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-       "    temporal_ref   int64 0\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "    spatial_ref    int32 4B 0\n",
+       "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+       "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    AREA_OR_POINT:  Area\n",
        "    scale_factor:   1.0\n",
        "    add_offset:     0.0\n",
        "    _FillValue:     1.7976931348623157e+308\n",
-       "    value_type:     binary</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'>'vegetation'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>y</span>: 4</li><li><span class='xr-has-index'>x</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bda30ebb-83e8-4c59-83d1-12c91d3d0d53' class='xr-array-in' type='checkbox' checked><label for='section-bda30ebb-83e8-4c59-83d1-12c91d3d0d53' 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>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0</span></div><div class='xr-array-data'><pre>array([[[0., 0., 0., 0.],\n",
+       "    value_type:     binary</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'>'vegetation'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>y</span>: 4</li><li><span class='xr-has-index'>x</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3dd61814-b205-4f24-9bfc-023737ca0a48' class='xr-array-in' type='checkbox' checked><label for='section-3dd61814-b205-4f24-9bfc-023737ca0a48' 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>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0</span></div><div class='xr-array-data'><pre>array([[[0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.]],\n",
@@ -1201,16 +1201,16 @@
        "       [[0., 1., 0., 1.],\n",
        "        [1., 1., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
-       "        [1., 0., 0., 1.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ef62eab4-0e48-4841-b11a-03fcdf94290f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef62eab4-0e48-4841-b11a-03fcdf94290f' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.531e+06 4.532e+06 ... 4.535e+06</div><input id='attrs-cc7f0922-9782-40de-911b-3adf1b9620d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc7f0922-9782-40de-911b-3adf1b9620d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72b75240-6b94-432c-934d-1f00f5575686' class='xr-var-data-in' type='checkbox'><label for='data-72b75240-6b94-432c-934d-1f00f5575686' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([4530750., 4532250., 4533750., 4535250.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.696e+06 2.695e+06 ... 2.692e+06</div><input id='attrs-9a3e1278-3b63-4f74-86fc-90129c82dd2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a3e1278-3b63-4f74-86fc-90129c82dd2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b93c3fd-3b55-4bc3-8d52-30fc3fc5697a' class='xr-var-data-in' type='checkbox'><label for='data-1b93c3fd-3b55-4bc3-8d52-30fc3fc5697a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([2696250., 2694750., 2693250., 2691750.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-14ce3921-cbab-40c1-a5c9-5c398681da4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14ce3921-cbab-40c1-a5c9-5c398681da4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f5cecea-635b-4243-89f8-5cf62dcfde60' class='xr-var-data-in' type='checkbox'><label for='data-4f5cecea-635b-4243-89f8-5cf62dcfde60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530000.0 1500.0 0.0 2697000.0 0.0 -1500.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2019-12-15T10:17:33.408715 ... 2...</div><input id='attrs-9db8e9a8-059e-448c-a512-9b677a684ce6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9db8e9a8-059e-448c-a512-9b677a684ce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a26c700-c6c2-483a-9c86-7e9fba52b630' class='xr-var-data-in' type='checkbox'><label for='data-2a26c700-c6c2-483a-9c86-7e9fba52b630' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-15T10:17:33.408715000&#x27;, &#x27;2020-09-05T10:17:43.167942000&#x27;,\n",
-       "       &#x27;2020-12-19T10:17:34.610661000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4c11e305-c328-4bc5-9a2d-e55ec53c948f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c11e305-c328-4bc5-9a2d-e55ec53c948f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e0431c4-e9c1-4321-873d-0da28bd8eb44' class='xr-var-data-in' type='checkbox'><label for='data-5e0431c4-e9c1-4321-873d-0da28bd8eb44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-ec75cd63-74ed-4c66-8d70-d3a79afe4f8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec75cd63-74ed-4c66-8d70-d3a79afe4f8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcdcb252-702e-494d-aaec-6af204215278' class='xr-var-data-in' type='checkbox'><label for='data-dcdcb252-702e-494d-aaec-6af204215278' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., 1.],\n",
+       "        [1., 0., 0., 1.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-56023a38-2e51-41f6-810b-350f2329e49d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-56023a38-2e51-41f6-810b-350f2329e49d' class='xr-section-summary' >Coordinates: <span>(6)</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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a29074dd-d492-4ae8-9f94-08d7702cfd24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a29074dd-d492-4ae8-9f94-08d7702cfd24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de1a00e8-535a-4c13-a312-fd3160aae6da' class='xr-var-data-in' type='checkbox'><label for='data-de1a00e8-535a-4c13-a312-fd3160aae6da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530000.0 1500.0 0.0 2697000.0 0.0 -1500.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.531e+06 4.532e+06 ... 4.535e+06</div><input id='attrs-551a35aa-31da-44ad-a54b-74cd4a8b6ad5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-551a35aa-31da-44ad-a54b-74cd4a8b6ad5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bf073e0-2a51-4d7a-b8f3-f2b504154243' class='xr-var-data-in' type='checkbox'><label for='data-0bf073e0-2a51-4d7a-b8f3-f2b504154243' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([4530750., 4532250., 4533750., 4535250.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.696e+06 2.695e+06 ... 2.692e+06</div><input id='attrs-70f18786-bd32-401f-b283-1938be265313' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70f18786-bd32-401f-b283-1938be265313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cfb9a1e-8c97-400c-9160-a4a2b51d5eb7' class='xr-var-data-in' type='checkbox'><label for='data-0cfb9a1e-8c97-400c-9160-a4a2b51d5eb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([2696250., 2694750., 2693250., 2691750.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-0e3670d8-1b64-4592-9505-30e054f80122' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e3670d8-1b64-4592-9505-30e054f80122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b04076b-0ae1-4e26-9264-017e81747d0e' class='xr-var-data-in' type='checkbox'><label for='data-9b04076b-0ae1-4e26-9264-017e81747d0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2019-12-15T10:17:33.408715 ... 2...</div><input id='attrs-99a12dbb-e202-4303-bc6b-9151354c4d9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99a12dbb-e202-4303-bc6b-9151354c4d9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a439ec3-f9bd-4be9-9d8b-645130a1542c' class='xr-var-data-in' type='checkbox'><label for='data-4a439ec3-f9bd-4be9-9d8b-645130a1542c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-15T10:17:33.408715000&#x27;, &#x27;2020-09-05T10:17:43.167942000&#x27;,\n",
+       "       &#x27;2020-12-19T10:17:34.610661000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-b17eadb3-7456-41e2-acc5-3dabcd9e509d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b17eadb3-7456-41e2-acc5-3dabcd9e509d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f019605-33fc-498f-ba23-20cc06dc9419' class='xr-var-data-in' type='checkbox'><label for='data-6f019605-33fc-498f-ba23-20cc06dc9419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., 1.],\n",
        "       [1., 1., 1., 1.],\n",
        "       [1., 1., 1., 1.],\n",
-       "       [1., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37411e27-74e0-4f09-ad69-28cbf0b7f615' class='xr-section-summary-in' type='checkbox'  ><label for='section-37411e27-74e0-4f09-ad69-28cbf0b7f615' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3f2685f3-3fac-42c9-8793-483c13170351' class='xr-index-data-in' type='checkbox'/><label for='index-3f2685f3-3fac-42c9-8793-483c13170351' 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([4530750.0, 4532250.0, 4533750.0, 4535250.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf2734f9-7710-42cc-ac0a-74ee94d428af' class='xr-index-data-in' type='checkbox'/><label for='index-bf2734f9-7710-42cc-ac0a-74ee94d428af' 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([2696250.0, 2694750.0, 2693250.0, 2691750.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</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-a5817081-2a7f-47e5-a4f1-2c0906cecb5b' class='xr-index-data-in' type='checkbox'/><label for='index-a5817081-2a7f-47e5-a4f1-2c0906cecb5b' 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;2019-12-15 10:17:33.408715&#x27;, &#x27;2020-09-05 10:17:43.167942&#x27;,\n",
+       "       [1., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e8db64c-826f-4558-9ef6-d495745f2c36' class='xr-section-summary-in' type='checkbox'  ><label for='section-0e8db64c-826f-4558-9ef6-d495745f2c36' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-09c9a779-bb32-4d13-bd7e-7afa2b6d7600' class='xr-index-data-in' type='checkbox'/><label for='index-09c9a779-bb32-4d13-bd7e-7afa2b6d7600' 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([4530750.0, 4532250.0, 4533750.0, 4535250.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1ba99f0-391e-482f-81f1-56ea548f2ef7' class='xr-index-data-in' type='checkbox'/><label for='index-a1ba99f0-391e-482f-81f1-56ea548f2ef7' 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([2696250.0, 2694750.0, 2693250.0, 2691750.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</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-12002aef-8e2e-4fda-91fc-7cab182343b3' class='xr-index-data-in' type='checkbox'/><label for='index-12002aef-8e2e-4fda-91fc-7cab182343b3' 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;2019-12-15 10:17:33.408715&#x27;, &#x27;2020-09-05 10:17:43.167942&#x27;,\n",
        "               &#x27;2020-12-19 10:17:34.610661&#x27;],\n",
-       "              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-6d15cc6e-8402-4569-ba67-3bdafb8f868f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d15cc6e-8402-4569-ba67-3bdafb8f868f' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>1.7976931348623157e+308</dd><dt><span>value_type :</span></dt><dd>binary</dd></dl></div></li></ul></div></div>"
+       "              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-4255e36e-842c-4f22-bc4d-d30ab8a8eac1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4255e36e-842c-4f22-bc4d-d30ab8a8eac1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>1.7976931348623157e+308</dd><dt><span>value_type :</span></dt><dd>binary</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'vegetation' (time: 3, y: 4, x: 4)>\n",
+       "<xarray.DataArray 'vegetation' (time: 3, y: 4, x: 4)> Size: 384B\n",
        "array([[[0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
@@ -1226,12 +1226,12 @@
        "        [0., 0., 0., 0.],\n",
        "        [1., 0., 0., 1.]]])\n",
        "Coordinates:\n",
-       "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "    spatial_ref    int64 0\n",
-       "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-       "    temporal_ref   int64 0\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "    spatial_ref    int32 4B 0\n",
+       "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+       "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    AREA_OR_POINT:  Area\n",
        "    scale_factor:   1.0\n",
@@ -2165,7 +2165,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;water&#x27; (time: 3, y: 4, x: 4)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;water&#x27; (time: 3, y: 4, x: 4)&gt; Size: 384B\n",
        "array([[[0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
@@ -2181,18 +2181,18 @@
        "        [1., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.]]])\n",
        "Coordinates:\n",
-       "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "    spatial_ref    int64 0\n",
-       "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-       "    temporal_ref   int64 0\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "    spatial_ref    int32 4B 0\n",
+       "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+       "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    AREA_OR_POINT:  Area\n",
        "    scale_factor:   1.0\n",
        "    add_offset:     0.0\n",
        "    _FillValue:     1.7976931348623157e+308\n",
-       "    value_type:     binary</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'>'water'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>y</span>: 4</li><li><span class='xr-has-index'>x</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-94e88d46-81cb-4631-9f20-f0d9a9c35d99' class='xr-array-in' type='checkbox' checked><label for='section-94e88d46-81cb-4631-9f20-f0d9a9c35d99' 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>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[0., 0., 0., 0.],\n",
+       "    value_type:     binary</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'>'water'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>y</span>: 4</li><li><span class='xr-has-index'>x</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e4a63dc0-a51e-485b-8dd3-c19f33dcfb24' class='xr-array-in' type='checkbox' checked><label for='section-e4a63dc0-a51e-485b-8dd3-c19f33dcfb24' 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>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.]],\n",
@@ -2205,16 +2205,16 @@
        "       [[1., 0., 0., 0.],\n",
        "        [0., 0., 0., 1.],\n",
        "        [1., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-3261700d-d660-425c-a62b-d0355d4e0bba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3261700d-d660-425c-a62b-d0355d4e0bba' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.531e+06 4.532e+06 ... 4.535e+06</div><input id='attrs-a7f48b52-0997-4833-a17b-f9da21be6cb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7f48b52-0997-4833-a17b-f9da21be6cb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-197fd38f-508c-4f94-85cb-a154ca28ce3f' class='xr-var-data-in' type='checkbox'><label for='data-197fd38f-508c-4f94-85cb-a154ca28ce3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([4530750., 4532250., 4533750., 4535250.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.696e+06 2.695e+06 ... 2.692e+06</div><input id='attrs-5071bf5e-8422-43af-957d-22fd673a2896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5071bf5e-8422-43af-957d-22fd673a2896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-085dfb6c-a0eb-47d8-96bb-ac1d3ea33433' class='xr-var-data-in' type='checkbox'><label for='data-085dfb6c-a0eb-47d8-96bb-ac1d3ea33433' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([2696250., 2694750., 2693250., 2691750.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-bb441299-1bc0-4adb-a2d8-f04f59601002' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb441299-1bc0-4adb-a2d8-f04f59601002' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07508f92-2f86-452b-9a6f-dc399a841a1a' class='xr-var-data-in' type='checkbox'><label for='data-07508f92-2f86-452b-9a6f-dc399a841a1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530000.0 1500.0 0.0 2697000.0 0.0 -1500.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2019-12-15T10:17:33.408715 ... 2...</div><input id='attrs-03f629af-92cc-4431-9551-fb87e46cbf4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03f629af-92cc-4431-9551-fb87e46cbf4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58f80662-4fcb-4c75-9c0e-24f7f215c4f5' class='xr-var-data-in' type='checkbox'><label for='data-58f80662-4fcb-4c75-9c0e-24f7f215c4f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-15T10:17:33.408715000&#x27;, &#x27;2020-09-05T10:17:43.167942000&#x27;,\n",
-       "       &#x27;2020-12-19T10:17:34.610661000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-de704605-d99d-4a91-adc6-f06a562339b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de704605-d99d-4a91-adc6-f06a562339b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ef33c3f-0c34-4b23-971e-1210fb000992' class='xr-var-data-in' type='checkbox'><label for='data-8ef33c3f-0c34-4b23-971e-1210fb000992' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-9ea687b7-6aba-4294-9615-07cbca97a448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ea687b7-6aba-4294-9615-07cbca97a448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6def988d-0845-4eb5-933c-d7ba441a56ea' class='xr-var-data-in' type='checkbox'><label for='data-6def988d-0845-4eb5-933c-d7ba441a56ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., 1.],\n",
+       "        [0., 0., 0., 0.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-d1c63854-fe88-41a2-b051-e6ee1dc68b2c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1c63854-fe88-41a2-b051-e6ee1dc68b2c' class='xr-section-summary' >Coordinates: <span>(6)</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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-58ce7baf-12ac-4733-9071-9294483bed85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58ce7baf-12ac-4733-9071-9294483bed85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d1465da-5562-428c-a0db-131b4ad13080' class='xr-var-data-in' type='checkbox'><label for='data-1d1465da-5562-428c-a0db-131b4ad13080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>4530000.0 1500.0 0.0 2697000.0 0.0 -1500.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.531e+06 4.532e+06 ... 4.535e+06</div><input id='attrs-3a5adfea-9f12-45c7-a9a1-9871bb50726d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a5adfea-9f12-45c7-a9a1-9871bb50726d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46747cb8-c2a7-42be-85aa-fd027f2e3b78' class='xr-var-data-in' type='checkbox'><label for='data-46747cb8-c2a7-42be-85aa-fd027f2e3b78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([4530750., 4532250., 4533750., 4535250.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.696e+06 2.695e+06 ... 2.692e+06</div><input id='attrs-63dfabfe-7666-4d79-985d-7a07320829ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63dfabfe-7666-4d79-985d-7a07320829ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a846dae-117c-4410-b254-33d18b5703fa' class='xr-var-data-in' type='checkbox'><label for='data-0a846dae-117c-4410-b254-33d18b5703fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-1500</dd><dt><span>value_type :</span></dt><dd>continuous</dd></dl></div><div class='xr-var-data'><pre>array([2696250., 2694750., 2693250., 2691750.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporal_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-94f304da-e131-49f4-9ce3-cad17376bb42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94f304da-e131-49f4-9ce3-cad17376bb42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e51e3f79-ecbd-474f-a9a8-2afc43809247' class='xr-var-data-in' type='checkbox'><label for='data-e51e3f79-ecbd-474f-a9a8-2afc43809247' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>zone :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array(0)</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'>2019-12-15T10:17:33.408715 ... 2...</div><input id='attrs-a8b822dc-a51b-4eba-b8b8-fc4b60b1689f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8b822dc-a51b-4eba-b8b8-fc4b60b1689f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f11aeb2e-d43a-4ce2-9c61-4890f1a00164' class='xr-var-data-in' type='checkbox'><label for='data-f11aeb2e-d43a-4ce2-9c61-4890f1a00164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>datetime</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-15T10:17:33.408715000&#x27;, &#x27;2020-09-05T10:17:43.167942000&#x27;,\n",
+       "       &#x27;2020-12-19T10:17:34.610661000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_feats</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-351e6024-81bd-450f-a4e4-1e9ca5a4fdf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-351e6024-81bd-450f-a4e4-1e9ca5a4fdf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e48eb2f-da2e-4b3f-8179-80287f4eb21f' class='xr-var-data-in' type='checkbox'><label for='data-3e48eb2f-da2e-4b3f-8179-80287f4eb21f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>value_type :</span></dt><dd>nominal</dd><dt><span>value_labels :</span></dt><dd>{1: &#x27;feature_1&#x27;}</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., 1.],\n",
        "       [1., 1., 1., 1.],\n",
        "       [1., 1., 1., 1.],\n",
-       "       [1., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c55b155b-b10a-4fbc-95ad-3a317e2fe23e' class='xr-section-summary-in' type='checkbox'  ><label for='section-c55b155b-b10a-4fbc-95ad-3a317e2fe23e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d484232a-883d-4e5c-a1c3-588f50aaec32' class='xr-index-data-in' type='checkbox'/><label for='index-d484232a-883d-4e5c-a1c3-588f50aaec32' 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([4530750.0, 4532250.0, 4533750.0, 4535250.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc5ffe19-da3b-4e1e-90e0-c9f1e25f5fd6' class='xr-index-data-in' type='checkbox'/><label for='index-dc5ffe19-da3b-4e1e-90e0-c9f1e25f5fd6' 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([2696250.0, 2694750.0, 2693250.0, 2691750.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</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-71e94a3e-5105-489f-ad76-3ed0d971bb78' class='xr-index-data-in' type='checkbox'/><label for='index-71e94a3e-5105-489f-ad76-3ed0d971bb78' 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;2019-12-15 10:17:33.408715&#x27;, &#x27;2020-09-05 10:17:43.167942&#x27;,\n",
+       "       [1., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-116fa2e0-b0cb-4711-8797-2bcf288ad0ad' class='xr-section-summary-in' type='checkbox'  ><label for='section-116fa2e0-b0cb-4711-8797-2bcf288ad0ad' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7220b5c8-d3b3-4815-941b-10a561d2693c' class='xr-index-data-in' type='checkbox'/><label for='index-7220b5c8-d3b3-4815-941b-10a561d2693c' 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([4530750.0, 4532250.0, 4533750.0, 4535250.0], dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54d3152d-062b-4c0c-a886-aea5cdd8b858' class='xr-index-data-in' type='checkbox'/><label for='index-54d3152d-062b-4c0c-a886-aea5cdd8b858' 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([2696250.0, 2694750.0, 2693250.0, 2691750.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</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-a0b823fc-fda3-4a6a-85e2-9436ef9c0af8' class='xr-index-data-in' type='checkbox'/><label for='index-a0b823fc-fda3-4a6a-85e2-9436ef9c0af8' 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;2019-12-15 10:17:33.408715&#x27;, &#x27;2020-09-05 10:17:43.167942&#x27;,\n",
        "               &#x27;2020-12-19 10:17:34.610661&#x27;],\n",
-       "              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-088d1aa2-64cf-4f73-bbdf-a21753da0416' class='xr-section-summary-in' type='checkbox'  checked><label for='section-088d1aa2-64cf-4f73-bbdf-a21753da0416' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>1.7976931348623157e+308</dd><dt><span>value_type :</span></dt><dd>binary</dd></dl></div></li></ul></div></div>"
+       "              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-e469da1a-cb7b-4836-8f29-93b473c06442' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e469da1a-cb7b-4836-8f29-93b473c06442' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>1.7976931348623157e+308</dd><dt><span>value_type :</span></dt><dd>binary</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'water' (time: 3, y: 4, x: 4)>\n",
+       "<xarray.DataArray 'water' (time: 3, y: 4, x: 4)> Size: 384B\n",
        "array([[[0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.],\n",
@@ -2230,12 +2230,12 @@
        "        [1., 0., 0., 0.],\n",
        "        [0., 0., 0., 0.]]])\n",
        "Coordinates:\n",
-       "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-       "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-       "    spatial_ref    int64 0\n",
-       "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-       "    temporal_ref   int64 0\n",
-       "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+       "    spatial_ref    int32 4B 0\n",
+       "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+       "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+       "    temporal_ref   int32 4B 0\n",
+       "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+       "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
        "Attributes:\n",
        "    AREA_OR_POINT:  Area\n",
        "    scale_factor:   1.0\n",
@@ -2366,7 +2366,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<xarray.DataArray 'ones' (time: 3, y: 4, x: 4)>\n",
+      "<xarray.DataArray 'ones' (time: 3, y: 4, x: 4)> Size: 384B\n",
       "array([[[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
@@ -2382,12 +2382,12 @@
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]]])\n",
       "Coordinates:\n",
-      "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-      "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-      "    spatial_ref    int64 0\n",
-      "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-      "    temporal_ref   int64 0\n",
-      "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+      "    spatial_ref    int32 4B 0\n",
+      "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+      "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+      "    temporal_ref   int32 4B 0\n",
+      "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+      "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
@@ -2533,7 +2533,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<xarray.DataArray 'mod' (time: 3, y: 4, x: 4)>\n",
+      "<xarray.DataArray 'mod' (time: 3, y: 4, x: 4)> Size: 384B\n",
       "array([[[ 434.,  530.,  553.,  856.],\n",
       "        [ 499.,  725.,  291.,  378.],\n",
       "        [ 427.,  530., 2690.,  310.],\n",
@@ -2549,12 +2549,12 @@
       "        [ 344.,  724., 1085.,   64.],\n",
       "        [ 387., 1549.,  556.,  369.]]])\n",
       "Coordinates:\n",
-      "  * x              (x) float64 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
-      "  * y              (y) float64 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
-      "    spatial_ref    int64 0\n",
-      "  * time           (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-...\n",
-      "    temporal_ref   int64 0\n",
-      "    spatial_feats  (y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0\n",
+      "    spatial_ref    int32 4B 0\n",
+      "  * x              (x) float64 32B 4.531e+06 4.532e+06 4.534e+06 4.535e+06\n",
+      "  * y              (y) float64 32B 2.696e+06 2.695e+06 2.693e+06 2.692e+06\n",
+      "    temporal_ref   int32 4B 0\n",
+      "  * time           (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 2...\n",
+      "    spatial_feats  (y, x) float64 128B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
       "Attributes:\n",
       "    value_type:  discrete\n"
      ]
@@ -2683,12 +2683,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<xarray.DataArray 'foo' (time: 3)>\n",
+      "<xarray.DataArray 'foo' (time: 3)> Size: 24B\n",
       "array([2.1983427e+08, 5.3802670e+07, 3.9970504e+07])\n",
       "Coordinates:\n",
-      "    spatial_ref   int64 0\n",
-      "  * time          (time) datetime64[ns] 2019-12-15T10:17:33.408715 ... 2020-1...\n",
-      "    temporal_ref  int64 0\n",
+      "    spatial_ref   int32 4B 0\n",
+      "    temporal_ref  int32 4B 0\n",
+      "  * time          (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 20...\n",
       "Attributes:\n",
       "    value_type:  continuous\n"
      ]
@@ -2971,11 +2971,15 @@
    "id": "d3481e84",
    "metadata": {},
    "source": [
-    "## Caching data layers\n",
+    "## Fake & Preview runs\n",
+    "\n",
+    "There are two options to execute a recipe in a simulated manner. \n",
     "\n",
-    "The query processor allows to cache retrieved data layers to reduce RAM memory requirements if the same data layer is referenced multiple times in the query recipe or the mapping. RAM memory requirements are proportional to the number of data layers that are stored as intermediate results. Caching data layers in RAM should only be done for those that are needed again when evaluating downstream parts of the recipe. This requires foresight about the execution order of the recipe, which accordingly requires a preview run preceding the actual execution. This preview run is performed by loading the data with drastically reduced spatial resolution (5x5 pixel grid). It resolves the data references and fills a cache by creating a list of the data references in the order in which they are evaluated. This list is then used dynamically during the actual execution of the recipe as a basis for keeping data layers in the cache and reading them from there if they are needed again.\n",
+    "The first one is a fake run as offered by the FakeProcessor class. Compared to the QueryProcessor all methods that actually process something such as loading data or applying functions to the data are muted by simply passing NaN-filled arrays around. Effectively, the result of calling FakeProcessor.execute() is therefore a recursive traversal of the recipe with resolution of the data references, which are stored in a cache object. This helps to...\n",
+    "* retrieve and inspect the data references in a recipe using a given mapping and layout.\n",
+    "* initialise caching mechanisms since data references are retrieved in the same order in which they will be evaluated using the QueryProcessor.\n",
     "\n",
-    "Below the result of the preview run is shown first to demonstrate what the resolved data references look like. You will see that the same data layer is referenced multiple times. The resulting initialised cache can then be fed as an argument to the QueryProcessor in a second step for the actual recipe execution. "
+    "Below the result of a FakeProcessor call is shown to demonstrate what the resolved data references look like. You will see that the same data layer is referenced multiple times in the recipe."
    ]
   },
   {
@@ -3001,30 +3005,107 @@
     }
    ],
    "source": [
-    "# Step I: preview run.\n",
-    "qp = QueryProcessor.parse(recipe, **{**context, \"preview\": True})\n",
-    "qp.optimize().execute()\n",
-    "qp.cache.seq"
+    "fp = FakeProcessor.parse(recipe, **context)\n",
+    "fp.optimize().execute()\n",
+    "fp.cache.seq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49b0a63a",
+   "metadata": {},
+   "source": [
+    "The FakeProcessor offers an option to rapidly investigate data layer references for a given recipe. However, if one wants to check the executability of a recipe and the validity its results, actual data loading and processing is required. To this end, a preview run can be used, which will execute a recipe in a drastically reduced spatial resolution (5x5 grid). This reduced spatial resolution speeds up the recipe evaluation and thus allows a preliminary inspection of results before running the recipe at full resolution."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "61f3b0dd",
+   "id": "cf86d7ca",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'blue_map': <xarray.DataArray 'blue_map' (y: 5, x: 5)> Size: 200B\n",
+       " array([[0., 0., 0., 0., 0.],\n",
+       "        [0., 1., 2., 0., 0.],\n",
+       "        [1., 1., 1., 1., 0.],\n",
+       "        [0., 0., 2., 1., 1.],\n",
+       "        [0., 0., 0., 0., 0.]])\n",
+       " Coordinates:\n",
+       "     spatial_ref    int32 4B 0\n",
+       "   * x              (x) float64 40B 4.53e+06 4.532e+06 ... 4.534e+06 4.535e+06\n",
+       "   * y              (y) float64 40B 2.696e+06 2.695e+06 ... 2.693e+06 2.692e+06\n",
+       "     temporal_ref   int32 4B 0\n",
+       "     spatial_feats  (y, x) float64 200B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
+       " Attributes:\n",
+       "     value_type:  discrete,\n",
+       " 'green_map': <xarray.DataArray 'green_map' (y: 5, x: 5)> Size: 200B\n",
+       " array([[2., 1., 1., 1., 2.],\n",
+       "        [2., 1., 0., 1., 1.],\n",
+       "        [1., 1., 0., 0., 1.],\n",
+       "        [2., 2., 0., 0., 1.],\n",
+       "        [2., 2., 0., 1., 2.]])\n",
+       " Coordinates:\n",
+       "     spatial_ref    int32 4B 0\n",
+       "   * x              (x) float64 40B 4.53e+06 4.532e+06 ... 4.534e+06 4.535e+06\n",
+       "   * y              (y) float64 40B 2.696e+06 2.695e+06 ... 2.693e+06 2.692e+06\n",
+       "     temporal_ref   int32 4B 0\n",
+       "     spatial_feats  (y, x) float64 200B 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0\n",
+       " Attributes:\n",
+       "     value_type:  discrete,\n",
+       " 'blue_curve': <xarray.DataArray 'blue_curve' (time: 3)> Size: 24B\n",
+       " array([0., 5., 6.])\n",
+       " Coordinates:\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
+       "   * time          (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 20...\n",
+       " Attributes:\n",
+       "     value_type:  discrete,\n",
+       " 'green_curve': <xarray.DataArray 'green_curve' (time: 3)> Size: 24B\n",
+       " array([ 2., 19.,  6.])\n",
+       " Coordinates:\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
+       "   * time          (time) datetime64[ns] 24B 2019-12-15T10:17:33.408715 ... 20...\n",
+       " Attributes:\n",
+       "     value_type:  discrete,\n",
+       " 'blue_stat': <xarray.DataArray 'blue_stat' ()> Size: 8B\n",
+       " array(11.)\n",
+       " Coordinates:\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
+       " Attributes:\n",
+       "     value_type:  discrete,\n",
+       " 'green_stat': <xarray.DataArray 'green_stat' ()> Size: 8B\n",
+       " array(27.)\n",
+       " Coordinates:\n",
+       "     spatial_ref   int32 4B 0\n",
+       "     temporal_ref  int32 4B 0\n",
+       " Attributes:\n",
+       "     value_type:  discrete}"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Step II: query processor execution.\n",
-    "qp = QueryProcessor.parse(recipe, **{**context, \"cache\": qp.cache})\n",
-    "response = qp.optimize().execute()"
+    "qp = QueryProcessor.parse(recipe, **{**context, \"preview\": True})\n",
+    "response = qp.optimize().execute()\n",
+    "response"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "02461c73",
+   "id": "6383e056",
    "metadata": {},
    "source": [
-    "When executing a query recipe you can directly initiate the entire caching workflow (preview & full resolution recipe execution) by setting the \"cache_data\" argument to `True`:"
+    "## Caching data layers\n",
+    "\n",
+    "The query processor allows to cache retrieved data layers to reduce I/O times if the same data layer is referenced multiple times in the query recipe or the mapping. Caching data layers in RAM should only be done for those that are needed again when evaluating downstream parts of the recipe. This requires foresight about the execution order of the recipe, which accordingly requires a simulated run preceding the actual execution. This simulated run is performed by the FakeProcessor. It resolves the data references and fills a cache by creating a list of the data references in the order in which they are evaluated. This list is then used dynamically during the actual execution of the recipe as a basis for keeping data layers in the cache and reading them from there if they are needed again. When executing a query recipe, caching is enabled by default, i.e. the \"cache_data\" argument is set to `True`:"
    ]
   },
   {
@@ -3034,7 +3115,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Same as above in a single step.\n",
     "response = recipe.execute(**{**context, \"cache_data\": True})"
    ]
   },
@@ -3043,13 +3123,12 @@
    "id": "aca485b4",
    "metadata": {},
    "source": [
-    "Caching does not always lead to a increase in performance. The effect depends on:\n",
+    "Note that in some cases caching may lead to a decrease in performance. The effect depends on:\n",
     "\n",
-    "* The resolution in which the query recipe is executed.\n",
     "* The redundancy of the data references in the recipe, i.e. if layers are called multiple times loading them from cache will reduce the overall time significantly.\n",
-    "* The data source (EO data cube) from which they are retrieved.\n",
+    "* The data source (EO data cube) from which they are retrieved, i.e. the I/O times for accessing the data source.\n",
     "\n",
-    "It should be noted that in our demos only data loaded from locally stored GeoTIFF files are analysed. This is sort of the worst case for demonstrating the benefits of caching since the data is stored locally and is therefore quickly accessible. Also, GeoTIFF files that are not stored in cloud-optimised format (CoGs) require to load the whole data into memory even when running in preview mode just to evaluate the sequence of data layers. Keep in mind, however, that caching is designed for and particularly beneficial in case of STACCubes when loading data over the internet."
+    "It should be noted that in our demos only data loaded from locally stored GeoTIFF files are analysed. This is sort of the worst case for demonstrating the benefits of caching since the data is stored locally and is therefore quickly accessible. Keep in mind, however, that caching is designed for and particularly beneficial in case of STACCubes when loading data over the internet."
    ]
   },
   {
diff --git a/semantique/datacube.py b/semantique/datacube.py
index 44bc8cec..3ffbb16e 100644
--- a/semantique/datacube.py
+++ b/semantique/datacube.py
@@ -6,6 +6,7 @@
 import datetime
 import os
 import planetary_computer as pc
+import pyproj
 import pytz
 import pystac
 import pystac_client
@@ -18,6 +19,8 @@
 from datacube.utils import masking
 from pystac_client.stac_api_io import StacApiIO
 from rasterio.errors import RasterioIOError
+from shapely.geometry import box, shape
+from shapely.ops import transform
 from urllib3 import Retry
 
 from semantique import exceptions
@@ -901,11 +904,36 @@ def _load(self, metadata, extent):
         # retrieve layer specific information
         lyr_dtype, lyr_na = self._get_dtype_na(metadata)
 
-        # subset temporally
-        times = [np.datetime64(x.get_datetime().replace(tzinfo=None)) for x in self.src]
+        # subset temporally and spatially
+        if "spatial_feats" in extent.coords:
+            extent = extent.drop_vars("spatial_feats")
         t_bounds = extent.sq.tz_convert(self.tz).time.values
-        keep = (times >= t_bounds[0]) & (times < t_bounds[1])
-        item_coll = [x for x, k in zip(self.src, keep) if k]
+        item_coll = STACCube.filter_spatio_temporal(
+          self.src,
+          extent.rio.bounds(),
+          epsg,
+          t_bounds[0],
+          t_bounds[1]
+        )
+
+        # subset according to layer key
+        filtered_items = []
+        for item in item_coll:
+            has_no_key = True
+            has_conformant_key = False
+            for asset_key, asset in item.assets.items():
+                if 'semantique:key' in asset.extra_fields:
+                    has_no_key = False
+                    asset_key = asset.extra_fields['semantique:key']
+                    ref_key = metadata['reference']
+                    if "_".join(asset_key) == "_".join(ref_key):
+                        has_conformant_key = True
+                        break
+                else:
+                    continue
+            if has_no_key or has_conformant_key:
+                filtered_items.append(item)
+        item_coll = filtered_items
 
         # return extent array as NaN in case of no data
         if not len(item_coll):
@@ -914,7 +942,7 @@ def _load(self, metadata, extent):
 
         # reauth
         if self.config["reauth_individual"]:
-            item_coll = self._sign_metadata(item_coll)
+            item_coll = STACCube._sign_metadata(item_coll)
 
         data = stackstac.stack(
             item_coll,
@@ -936,13 +964,11 @@ def _load(self, metadata, extent):
         # convert datetimes to daily granularity - resample by day
         def _mosaic_ints(x, axis=0, na_value=np.nan):
             max_idx = np.argmax(x != na_value, axis=axis)
-            # handle cases where all values are na_value
+            grid_x, grid_y = np.ogrid[:x.shape[2], :x.shape[3]]
+            chosen = x[max_idx, 0, grid_x, grid_y]
+            na_array = np.full(chosen.shape, na_value, dtype=x.dtype)
             all_na = np.all(x == na_value, axis=axis)
-            chosen = np.choose(max_idx, x)
-            # where all values are na_value, fill with na_value, else use chosen value
-            return np.where(
-                all_na, np.full(chosen.shape, na_value, dtype=x.dtype), chosen
-            )
+            return np.where(all_na, na_array, chosen)
 
         if self.config["group_by_solar_day"]:
             if len(data.time):
@@ -950,11 +976,15 @@ def _mosaic_ints(x, axis=0, na_value=np.nan):
                 if data.dtype.kind == "f":
                     data = data.where(data != lyr_na)
                     data = (
-                        data.groupby(days).first(skipna=True).rename({"floor": "time"})
+                        data
+                        .groupby(days, squeeze=False)
+                        .first(skipna=True)
+                        .rename({"floor": "time"})
                     )
                 else:
                     data = (
-                        data.groupby(days)
+                        data
+                        .groupby(days, squeeze=False)
                         .reduce(_mosaic_ints, na_value=lyr_na)
                         .rename({"floor": "time"})
                     )
@@ -1030,7 +1060,45 @@ def _mask(self, data, metadata):
         data = data.where(data["spatial_feats"].notnull())
         return data
 
-    def _sign_metadata(self, items):
+    @staticmethod
+    def _divide_chunks(lst, k):
+        return [lst[i : i + k] for i in range(0, len(lst), k)]
+
+    @staticmethod
+    def filter_spatio_temporal(item_collection, bbox, bbox_crs, start_datetime, end_datetime):
+        """
+        Filter item collection by spatio-temporal extent.
+
+        Args:
+          item_collection (pystac.ItemCollection): The item collection to filter.
+          bbox (tuple): The bounding box in WGS84 coordinates to filter by.
+          bbox_crs (str): The CRS of the bounding box.
+          start_datetime (np.datetime64): The start datetime to filter by.
+          end_datetime (np.datetime64): The end datetime to filter by.
+        """
+        min_lon, min_lat, max_lon, max_lat = bbox
+        spatial_filter = box(min_lon, min_lat, max_lon, max_lat)
+        source_crs = pyproj.CRS("EPSG:4326")
+        target_crs = pyproj.CRS(bbox_crs)
+        transformer = (
+            pyproj.Transformer
+            .from_crs(source_crs, target_crs, always_xy=True)
+            .transform
+        )
+        filtered_items = []
+        for item in item_collection:
+            item_geom = shape(item.geometry)
+            item_geom = transform(transformer, item_geom)
+            item_datetime = np.datetime64(item.datetime)
+            if not spatial_filter.intersects(item_geom):
+                continue
+            if not (start_datetime <= item_datetime < end_datetime):
+                continue
+            filtered_items.append(item)
+        return filtered_items
+
+    @staticmethod
+    def _sign_metadata(items):
         # retrieve collections root & item ids
         roots = [x.get_root_link().href for x in items]
         # create dictionary grouped by collection
@@ -1067,12 +1135,21 @@ def _sign_metadata(self, items):
                         ids=[x.id for x in chunk],
                         collections=[x.get_collection() for x in chunk],
                     )
-                    updated_items.extend(list(item_search.items()))
+                    for item in item_search.items():
+                        original_item = next(
+                            (i for i in chunk if i.id == item.id), None
+                        )
+                        if original_item is not None:
+                            # create a deep copy of the original item
+                            # aim: keep original attributes and assets
+                            new_item = original_item.clone()
+                            # imprinting of the updated hrefs with new tokens
+                            for asset_key in item.assets:
+                                if asset_key in new_item.assets:
+                                    new_href = item.assets[asset_key].href
+                                    new_item.assets[asset_key].href = new_href
+                            updated_items.append(new_item)
             else:
                 updated_items.extend(curr_colls[coll]["items"])
         # return signed items
-        return pystac.ItemCollection(updated_items)
-
-    @staticmethod
-    def _divide_chunks(lst, k):
-        return [lst[i : i + k] for i in range(0, len(lst), k)]
\ No newline at end of file
+        return pystac.ItemCollection(updated_items)
\ No newline at end of file
diff --git a/semantique/mapping.py b/semantique/mapping.py
index 47681092..cc93ba0c 100644
--- a/semantique/mapping.py
+++ b/semantique/mapping.py
@@ -1,7 +1,7 @@
 from abc import abstractmethod
 
 from semantique import exceptions
-from semantique.processor.core import QueryProcessor
+from semantique.processor.core import QueryProcessor, FakeProcessor
 from semantique.processor.arrays import Collection
 from semantique.processor import reducers
 from semantique.visualiser.visualise import show
@@ -106,7 +106,7 @@ def __init__(self, rules = None):
     super(Semantique, self).__init__(rules)
 
   def translate(self, *reference, property = None, extent, datacube,
-                eval_obj = None, **config):
+                eval_obj = None, processor=QueryProcessor, **config):
     """Translate a semantic concept reference into a semantic array.
 
     Parameters
@@ -129,6 +129,10 @@ def translate(self, *reference, property = None, extent, datacube,
       eval_obj : :obj:`xarray.DataArray`
         The array to refer to when the mapping rules of the semantic concept
         contain processing chains that start with a self reference.
+      processor : :obj:`processor.core.QueryProcessor`
+        The processor class to be used for processing the query. By default
+        :obj:`processor.core.QueryProcessor` is used. Can be set to
+        :obj:`processor.core.FakeProcessor` to skip the processing.
       **config:
         Additional keyword arguments forwarded to the initializer of
         :obj:`processor.core.QueryProcessor`.
@@ -142,7 +146,7 @@ def translate(self, *reference, property = None, extent, datacube,
 
     """
     ruleset = self.lookup(*reference)
-    processor = QueryProcessor({}, datacube, self, extent, **config)
+    processor = processor({}, datacube, self, extent, **config)
     if eval_obj is not None:
       processor._set_eval_obj(eval_obj)
     if property is None:
@@ -150,7 +154,10 @@ def translate(self, *reference, property = None, extent, datacube,
       if len(properties) == 1:
         out = properties[0]
       else:
-        out = Collection(properties).merge(reducers.all_)
+        out = Collection(properties).merge(
+          reducers.all_,
+          track_types=processor.track_types
+        )
     else:
       try:
         property = ruleset[property]
diff --git a/semantique/processor/arrays.py b/semantique/processor/arrays.py
index 651f0dd2..caf703a0 100644
--- a/semantique/processor/arrays.py
+++ b/semantique/processor/arrays.py
@@ -447,7 +447,7 @@ def groupby(self, grouper, labels_as_names = True, **kwargs):
     if is_list:
       idx = pd.MultiIndex.from_arrays([x.data for x in grouper])
       dim = grouper[0].dims
-      partition = list(obj.groupby(xr.IndexVariable(dim, idx)))
+      partition = list(obj.groupby(xr.IndexVariable(dim, idx), squeeze=False))
       # Use value labels as group names if defined.
       if labels_as_names:
         labs = [x.sq.value_labels for x in grouper]
@@ -464,7 +464,7 @@ def groupby(self, grouper, labels_as_names = True, **kwargs):
       else:
         groups = [i[1].rename(i[0]) for i in partition]
     else:
-      partition = list(obj.groupby(grouper[0]))
+      partition = list(obj.groupby(grouper[0], squeeze=False))
       # Use value labels as group names if defined.
       if labels_as_names:
         labs = grouper[0].sq.value_labels
@@ -493,7 +493,14 @@ def fix(x, y):
     out = Collection(groups)
     return out
 
-  def reduce(self, reducer, dimension = None, track_types = True, **kwargs):
+  def reduce(
+      self,
+      reducer,
+      dimension = None,
+      track_types = True,
+      keep_attrs = True,
+      **kwargs
+    ):
     """Apply the reduce verb to the array.
 
     The reduce verb reduces the dimensionality of an array.
@@ -508,6 +515,9 @@ def reduce(self, reducer, dimension = None, track_types = True, **kwargs):
       track_types : :obj:`bool`
         Should the reducer promote the value type of the output object, based
         on the value type of the input object?
+      keep_attrs: :obj:`bool`
+        Should the variable's attributes (attrs) be copied from the
+        original object to the new one?
       **kwargs:
         Additional keyword arguments passed on to the reducer function. These
         should not include a keyword argument "dim", which is reserved for
@@ -539,7 +549,7 @@ def reduce(self, reducer, dimension = None, track_types = True, **kwargs):
           )
       kwargs["dim"] = dimension
     # Reduce.
-    out = reducer(obj, track_types = track_types, **kwargs)
+    out = reducer(obj, track_types = track_types, keep_attrs = keep_attrs, **kwargs)
     return out
 
   def shift(self, dimension, steps, **kwargs):
@@ -1605,7 +1615,7 @@ def _merge_dups(obj):
               return Collection(dups).sq.merge(reducers.first_)
             else:
               return obj
-          groups = list(raw.groupby(dimension))
+          groups = list(raw.groupby(dimension, squeeze=False))
           clean = xr.concat([_merge_dups(x[1]) for x in groups], dimension)
         else:
           clean = raw
diff --git a/semantique/processor/core.py b/semantique/processor/core.py
index 1681f672..10a307e5 100644
--- a/semantique/processor/core.py
+++ b/semantique/processor/core.py
@@ -6,6 +6,7 @@
 import pyproj
 import pytz
 import warnings
+import xarray as xr
 
 from semantique import exceptions
 from semantique.processor import arrays, operators, reducers, values, utils
@@ -427,13 +428,10 @@ def handle_layer(self, block):
       )
     logger.debug(f"Retrieved layer {block['reference']}:\n{out}")
     # Update cache
-    if self._preview:
-      self._cache.build(block["reference"])
-    else:
-      self._cache.update(layer_key, out)
-      logger.debug("Cache updated")
-      logger.debug(f"Sequence of layers: {self._cache._seq}")
-      logger.debug(f"Currently cached layers: {list(self._cache._data.keys())}")
+    self._cache.update(layer_key, out)
+    logger.debug("Cache updated")
+    logger.debug(f"Sequence of layers: {self._cache._seq}")
+    logger.debug(f"Currently cached layers: {list(self._cache._data.keys())}")
     return out
 
   def handle_result(self, block):
@@ -1205,11 +1203,140 @@ def _reset_eval_obj(self):
   def _set_eval_obj(self, obj):
     self._eval_obj.append(obj)
 
+class FakeProcessor(QueryProcessor):
+  """
+  Worker that simulates the processing of a semantic query recipe.
+  It doesn't actually process the query, but can be used to translate concepts
+  and retrieve data layer names.
+
+  Parameters
+  ----------
+    recipe : QueryRecipe
+      The query recipe to be processed.
+    datacube : Datacube
+      The datacube instance to process the query against.
+    mapping : Mapping
+      The mapping instance to process the query against.
+    extent : :obj:`xarray.DataArray`
+      The spatio-temporal extent in which the query should be processed. Should
+      be given as an array with a temporal dimension and two spatial dimensions
+      such as returned by
+      :func:`parse_extent <semantique.processor.utils.parse_extent>`.
+    custom_verbs : :obj:`dict`, optional
+      User-defined verbs that may be used when executing the query recipe in
+      addition to the built-in verbs in semantique.
+    custom_operators : :obj:`dict`, optional
+      User-defined operator functions that may be used when evaluating
+      expressions with the evaluate verb in addition to the built-in operators
+      in semantique. Built-in operators with the same name will be overwritten.
+    custom_reducers : :obj:`dict`, optional
+      User-defined reducer functions that may be used when reducing array
+      dimensions with the reduce verb in addition to the built-in reducers in
+      semantique. Built-in reducers with the same name will be overwritten.
+    track_types : :obj:`bool`
+      Should the query processor keep track of the value type of arrays
+      when applying processes, and promote them if necessary? This option is
+      always disabled for the FakeProcessor since it doesn't evaualte processes
+      and therefore can't check the validity of the types of the arrays.
+    preview : :obj:`bool`
+      Run the query processor with reduced resolution to test the recipe execution.
+      Preview-runs are necessary if cache should be used.
+    cache : :obj:`Cache`
+      The cache object that is used to store data layers.
+  """
+  def __init__(self, recipe, datacube, mapping, extent, custom_verbs = None,
+               custom_operators = None, custom_reducers = None,
+               track_types = True, preview = False, cache = None):
+    super(FakeProcessor, self).__init__(
+      recipe, datacube, mapping, extent, custom_verbs=custom_verbs,
+      custom_operators=custom_operators, custom_reducers=custom_reducers,
+      track_types=track_types, preview=preview, cache=cache
+      )
+    self.track_types = False
+
+  def call_verb(self, name, params):
+    """Apply a verb to the active evaluation object.
+
+    Parameters
+    -----------
+      name : :obj:`str`
+        Name of the verb.
+      params : :obj:`dict`
+        Parameters to be forwarded to the verb.
+
+    Returns
+    -------
+      :obj:`xarray.DataArray` or :obj:`Collection <semantique.processor.arrays.Collection>`
+    """
+    return self._get_eval_obj()
+
+  def handle_concept(self, block):
+    """Handler for semantic concept references.
+
+    Parameters
+    ----------
+      block : :obj:`dict`
+        Textual representation of a building block of type "concept".
+
+    Returns
+    -------
+      :obj:`xarray.DataArray`
+
+    """
+    logger.debug(f"Translating concept {block['reference']}")
+    out = self._mapping.translate(
+      *block["reference"],
+      property = block["property"] if "property" in block else None,
+      extent = self._extent,
+      datacube = self._datacube,
+      eval_obj = self._get_eval_obj(),
+      processor = FakeProcessor,
+      preview = self._preview,
+      cache = self._cache,
+      custom_verbs = self._custom_verbs,
+      custom_operators = self._custom_operators,
+      custom_reducers = self._custom_reducers,
+      track_types = self._track_types,
+
+    )
+    logger.debug(f"Translated concept {block['reference']}:\n{out}")
+    return out
+
+  def handle_label(self, block):
+    """Handler for value labels.
+
+    Parameters
+    ----------
+      block : :obj:`dict`
+        Textual representation of a building block of type "label".
+
+    Returns
+    -------
+      :obj:None
+    """
+    return None
+
+  def handle_layer(self, block):
+    """Handler for data layer references.
+
+    Parameters
+    ----------
+      block : :obj:`dict`
+        Textual representation of a building block of type "layer".
+
+    Returns
+    -------
+      :obj:`xarray.DataArray`
+    """
+    self._cache.build(block["reference"])
+    return xr.full_like(self._extent, np.nan)
+
+
 class Cache:
   """Cache of retrieved data layers.
 
   The cache takes care of tracking the data references in their order of
-  evaluation and retaining data layers in RAM if they are still  needed for
+  evaluation and retaining data layers in RAM if they are still needed for
   the further execution of the semantic query.
   """
 
diff --git a/semantique/recipe.py b/semantique/recipe.py
index 9a4fb4a1..34d1d12a 100644
--- a/semantique/recipe.py
+++ b/semantique/recipe.py
@@ -1,4 +1,4 @@
-from semantique.processor.core import QueryProcessor
+from semantique.processor.core import QueryProcessor, FakeProcessor
 from semantique.visualiser.visualise import show
 
 class QueryRecipe(dict):
@@ -29,7 +29,7 @@ def __init__(self, results = None):
     super(QueryRecipe, self).__init__(obj)
 
   def execute(self, datacube, mapping, space, time, run_preview = False,
-              cache_data = False, **config):
+              cache_data = True, **config):
     """Execute a query recipe.
 
     This function initializes a :obj:`processor.core.QueryProcessor` instance
@@ -47,15 +47,14 @@ def execute(self, datacube, mapping, space, time, run_preview = False,
       time : TemporalExtent
         The temporal extent in which the query should be processed.
       run_preview : :obj:`bool`
-        Should a preview run be performed before executing the query recipe as
-        specified? A preview run calls the query processor with reduced
-        resolution to test if the recipe execution succeeds.
+        Should a preview run with reduced spatial resolution be performed?
+        A preview run enables to test if the recipe execution succeeds
+        and allows to inspect the results.
       cache_data : :obj:`bool`
         Should the query processor cache the data references as provided by the
         mapped concepts? Enabling caching increases the memory footprint while
-        reducing the I/O time to retrieve data. Will be used only if the same
-        data layer is referenced multiple times. Caching requires a preview run
-        and will automatically set the preview parameter to :obj:`True`.
+        reducing the I/O time to retrieve data if the same data layer is
+        referenced multiple times.
       **config:
         Additional configuration parameters forwarded to
         :func:`QueryProcessor.parse <processor.core.QueryProcessor.parse>`.
@@ -84,23 +83,24 @@ def execute(self, datacube, mapping, space, time, run_preview = False,
     >>> recipe.execute(dc, mapping, space, time, **config)
 
     """
-    if run_preview or cache_data:
-      # Preview run.
-      preview_config = config
-      preview_config["preview"] = True
-      preview_config["cache"] = None
-      qp = QueryProcessor.parse(self, datacube, mapping, space, time, **preview_config)
-      _ = qp.optimize().execute()
-      # Main run.
-      main_config = config
-      main_config["preview"] = False
-      main_config["cache"] = qp.cache if cache_data else None
-      qp = QueryProcessor.parse(self, datacube, mapping, space, time, **main_config)
-      return qp.optimize().execute()
+    if cache_data:
+      fp = FakeProcessor.parse(self, datacube, mapping, space, time, **config)
+      _ = fp.optimize().execute()
+      cache = fp.cache
     else:
-      # Execute the query recipe without a preview run.
-      qp = QueryProcessor.parse(self, datacube, mapping, space, time, **config)
-      return qp.optimize().execute()
+      cache = None
+
+    qp = QueryProcessor.parse(
+      self,
+      datacube,
+      mapping,
+      space,
+      time,
+      preview=run_preview,
+      cache=cache,
+      **config
+    )
+    return qp.optimize().execute()
 
   def visualise(self):
     """Visualise the recipe in a web browser.
@@ -110,4 +110,4 @@ def visualise(self):
     editor. The recipe is converted into Blockly XML format and served
     to the browser.
     """
-    show(self)
\ No newline at end of file
+    show(self)