GEE_Python_API.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shakyaanice/basics-of-gee/blob/master/GEE_Python_API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "execution_count": 1,
      "metadata": {
        "id": "K7xiNUxHjbOR"
      },
      "outputs": [],
      "source": [
        "import ee\n",
        "ee.Authenticate()\n",
        "ee.Initialize(project='ee-testgee')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "city='Kathmandu'\n",
        "population =300000\n",
        "print(city, population)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YgVVDG7zj9g1",
        "outputId": "3c2627a1-eccc-4ec4-ab52-bde8708674f0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kathmandu 300000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s2=ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "geometry= ee.Geometry.Polygon([[ [82.60642647743225, 27.16350437805251],\n",
        "  [82.60984897613525, 27.1618529901377],\n",
        "  [82.61088967323303, 27.163695288375266],\n",
        "  [82.60757446289062, 27.16517483230927]]])"
      ],
      "metadata": {
        "id": "SQPI3abMkOgh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filtered =s2\\\n",
        ".filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',30))\\\n",
        ".filter(ee.Filter.date('2019-02-01','2019-03-01'))\\\n",
        ".filter(ee.Filter.bounds(geometry))\n",
        "def maskS2clouds(image):\n",
        "  qa = image.select('QA60')\n",
        "  cloudBitMask = 1 << 10\n",
        "  cirrusBitMask = 1 << 11\n",
        "  mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(\n",
        "             qa.bitwiseAnd(cirrusBitMask).eq(0))\n",
        "  return image.updateMask(mask) \\\n",
        "      .select(\"B.*\") \\\n",
        "      .copyProperties(image, [\"system:time_start\"])\n",
        ""
      ],
      "metadata": {
        "id": "F0UhGSGNkg04"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ndvi(image):\n",
        "  ndvi=image.normalizedDifference(['B8','B4']).rename('ndvi')\n",
        "  return image.addBands(ndvi)\n",
        "\n",
        "withNDVI = filtered\\\n",
        ".map(maskS2clouds).map(ndvi)\n"
      ],
      "metadata": {
        "id": "IZVWJpYilsQQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "composite = withNDVI.median()\n",
        "ndvi = composite.select('ndvi')\n",
        "\n",
        "stats = ndvi.reduceRegion(**{\n",
        "  'reducer': ee.Reducer.mean(),\n",
        "  'geometry': geometry,\n",
        "  'scale': 10,\n",
        "  'maxPixels': 1e10\n",
        "  })"
      ],
      "metadata": {
        "id": "dpqxaFjCnEQu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(stats.get('ndvi').getInfo())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "keKKD40knY6G",
        "outputId": "b400c47f-219e-4789-f0ba-331d3775e058"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6795091979989383\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "myList =ee.List.sequence(1,10)\n",
        "newList =myList.map(lambda number: ee.Number(number).pow(2))\n",
        "print(newList.getInfo())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYuV3XNYnxrZ",
        "outputId": "ce7bc162-d0f0-4686-bfdd-4b63ef72de24"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "geometry= ee.Geometry.Point([77.60412933051538, 12.952912912328241])\n",
        "s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
        "  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))\\\n",
        "  .filter(ee.Filter.bounds(geometry))\n",
        "\n",
        "size= filtered.size().getInfo()\n",
        "print(size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PY7UMrMLqHiP",
        "outputId": "0c8cd0f8-5298-4775-adbd-b252f9804f68"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import geemap\n"
      ],
      "metadata": {
        "id": "iIrNf_WPuPeR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Map= geemap.Map(width=800)\n",
        "Map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "23358b2a9c3443bf8fc8c47203d2ed7c",
            "62b5e05ac7a64f11a81b3a84867602de",
            "5e9d02c4a5fa47e88a37e5dffb16e0b5",
            "59dd074729fe4a70ae09698570c19da2",
            "1f59b63ae56d47668c5720c57b3ea835",
            "3755334af8724adda0e76d8ef374be50",
            "c7cdbe96650a4c1eb538fac9252efdce",
            "762552d48f1d481b9273185719b53101",
            "eee9f8d73be24b3aab01b490ce805d9d",
            "08c8a94b21c0462e8ef5ac26373f952a",
            "9596e596aa4343f2a68406b13e6ce6bb",
            "7bea6fe041ea4a8c81a355ab3f4ca89f",
            "b5cf590b147c4a7ea5aa5fc91bbf8f5a",
            "612595b71ef5434cb4ec10216c2b3655",
            "44eb1c64d6744a88aeb362b1164f8b18",
            "5e7e7a317e8844af9a0eefaaac19b268",
            "9946aa50b5b84c45b3187f69c145ccda",
            "01a5e8c136834bd1b2451fcd0a271f86",
            "54272ece30ca4ba888faa80ced28591f",
            "65e32c00211e40dcace1307a530e1bed",
            "d4c7a5b1e98a4c049c3b1a39e8df2b83",
            "166f4875e53f49c0836d0fe4822a82ab",
            "3a7afd780345456a85cbeccd5599ac0b",
            "0739715d5a9046eba4100d734bdc430f",
            "c247c7c34704447c9dba3d8fb7ae1788"
          ]
        },
        "id": "Jo-2ToJZuQw8",
        "outputId": "a71e46de-bccb-4733-cc85-31dc4cebbd59"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[0, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDataGUI(childr…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "23358b2a9c3443bf8fc8c47203d2ed7c"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241])\n",
        "s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "\n",
        "rgbVis = {'min': 0.0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}\n",
        "\n",
        "filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30)) \\\n",
        "  .filter(ee.Filter.date('2019-01-01', '2020-01-01')) \\\n",
        "  .filter(ee.Filter.bounds(geometry))\n",
        "\n",
        "medianComposite = filtered.median()\n",
        "\n",
        "Map.centerObject(geometry, 10)\n",
        "Map.addLayer(medianComposite, rgbVis, 'Median Composite')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "-RZSKAwLuc-T",
        "outputId": "ef0d7e91-0e13-4085-abea-717970a2bc48"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "javascript_code = \"\"\"\n",
        "var geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766]);\n",
        "Map.centerObject(geometry, 12)\n",
        "var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "var rgbVis = {\n",
        "  min: 0.0,\n",
        "  max: 3000,\n",
        "  bands: ['B4', 'B3', 'B2'],\n",
        "};\n",
        "\n",
        "var filtered = s2\n",
        "  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))\n",
        "  .filter(ee.Filter.bounds(geometry))\n",
        "\n",
        "// Load the Cloud Score+ collection\n",
        "var csPlus = ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED');\n",
        "var csPlusBands = csPlus.first().bandNames();\n",
        "\n",
        "// We need to add Cloud Score + bands to each Sentinel-2\n",
        "// image in the collection\n",
        "// This is done using the linkCollection() function\n",
        "var filteredS2WithCs = filtered.linkCollection(csPlus, csPlusBands);\n",
        "\n",
        "// Function to mask pixels with low CS+ QA scores.\n",
        "function maskLowQA(image) {\n",
        "  var qaBand = 'cs';\n",
        "  var clearThreshold = 0.5;\n",
        "  var mask = image.select(qaBand).gte(clearThreshold);\n",
        "  return image.updateMask(mask);\n",
        "}\n",
        "\n",
        "\n",
        "var filteredMasked = filteredS2WithCs\n",
        "  .map(maskLowQA);\n",
        "\n",
        "// Write a function that computes NDVI for an image and adds it as a band\n",
        "function addNDVI(image) {\n",
        "  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi');\n",
        "  return image.addBands(ndvi);\n",
        "}\n",
        "\n",
        "var withNdvi = filteredMasked.map(addNDVI);\n",
        "\n",
        "var composite = withNdvi.median()\n",
        "palette = [\n",
        "  'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',\n",
        "  '74A901', '66A000', '529400', '3E8601', '207401', '056201',\n",
        "  '004C00', '023B01', '012E01', '011D01', '011301'];\n",
        "\n",
        "ndviVis = {min:0, max:0.7, palette: palette }\n",
        "Map.addLayer(withNdvi.select('ndvi'), ndviVis, 'NDVI Composite')\n",
        "\n",
        "\"\"\"\n",
        "lines = geemap.js_snippet_to_py(\n",
        "    javascript_code, add_new_cell=False,\n",
        "    import_ee=True, import_geemap=True, show_map=True)\n",
        "for line in lines:\n",
        "    print(line.rstrip())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "9HgaJa7NuxKj",
        "outputId": "9ed10b39-511b-407f-93e9-5af9b3115f53"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "import ee\n",
            "import geemap\n",
            "\n",
            "m = geemap.Map()\n",
            "\n",
            "geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766])\n",
            "m.centerObject(geometry, 12)\n",
            "s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
            "rgbVis = {\n",
            "  'min': 0.0,\n",
            "  'max': 3000,\n",
            "  'bands': ['B4', 'B3', 'B2'],\n",
            "}\n",
            "\n",
            "filtered = s2 \\\n",
            "  .filter(ee.Filter.date('2019-01-01', '2020-01-01')) \\\n",
            "  .filter(ee.Filter.bounds(geometry))\n",
            "\n",
            "# Load the Cloud Score+ collection\n",
            "csPlus = ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')\n",
            "csPlusBands = csPlus.first().bandNames()\n",
            "\n",
            "# We need to add Cloud Score + bands to each Sentinel-2\n",
            "# image in the collection\n",
            "# This is done using the linkCollection() function\n",
            "filteredS2WithCs = filtered.linkCollection(csPlus, csPlusBands)\n",
            "\n",
            "# Function to mask pixels with low CS+ QA scores.\n",
            "def maskLowQA(image):\n",
            "  qaBand = 'cs'\n",
            "  clearThreshold = 0.5\n",
            "  mask = image.select(qaBand).gte(clearThreshold)\n",
            "  return image.updateMask(mask)\n",
            "\n",
            "filteredMasked = filteredS2WithCs \\\n",
            "  .map(maskLowQA)\n",
            "\n",
            "# Write a function that computes NDVI for an image and adds it as a band\n",
            "def addNDVI(image):\n",
            "  ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi')\n",
            "  return image.addBands(ndvi)\n",
            "\n",
            "withNdvi = filteredMasked.map(addNDVI)\n",
            "\n",
            "composite = withNdvi.median()\n",
            "palette = [\n",
            "  'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',\n",
            "  '74A901', '66A000', '529400', '3E8601', '207401', '056201',\n",
            "  '004C00', '023B01', '012E01', '011D01', '011301']\n",
            "\n",
            "ndviVis = {'min':0, 'max':0.7, 'palette': palette }\n",
            "m.addLayer(withNdvi.select('ndvi'), ndviVis, 'NDVI Composite')\n",
            "\n",
            "m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = geemap.Map()\n",
        "\n",
        "geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766])\n",
        "m.centerObject(geometry, 12)\n",
        "s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "rgbVis = {\n",
        "  'min': 0.0,\n",
        "  'max': 3000,\n",
        "  'bands': ['B4', 'B3', 'B2'],\n",
        "}\n",
        "\n",
        "filtered = s2 \\\n",
        "  .filter(ee.Filter.date('2019-01-01', '2020-01-01')) \\\n",
        "  .filter(ee.Filter.bounds(geometry))\n",
        "\n",
        "# Load the Cloud Score+ collection\n",
        "csPlus = ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')\n",
        "csPlusBands = csPlus.first().bandNames()\n",
        "\n",
        "# We need to add Cloud Score + bands to each Sentinel-2\n",
        "# image in the collection\n",
        "# This is done using the linkCollection() function\n",
        "filteredS2WithCs = filtered.linkCollection(csPlus, csPlusBands)\n",
        "\n",
        "# Function to mask pixels with low CS+ QA scores.\n",
        "def maskLowQA(image):\n",
        "  qaBand = 'cs'\n",
        "  clearThreshold = 0.5\n",
        "  mask = image.select(qaBand).gte(clearThreshold)\n",
        "  return image.updateMask(mask)\n",
        "\n",
        "filteredMasked = filteredS2WithCs \\\n",
        ".map(maskLowQA)\n",
        "\n",
        "# Write a function that computes NDVI for an image and adds it as a band\n",
        "def addNDVI(image):\n",
        "  ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi')\n",
        "  return image.addBands(ndvi)\n",
        "\n",
        "withNdvi = filteredMasked.map(addNDVI)\n",
        "\n",
        "composite = withNdvi.median()\n",
        "palette = [\n",
        "  'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',\n",
        "  '74A901', '66A000', '529400', '3E8601', '207401', '056201',\n",
        "  '004C00', '023B01', '012E01', '011D01', '011301']\n",
        "\n",
        "ndviVis = {'min':0, 'max':0.7, 'palette': palette }\n",
        "m.addLayer(withNdvi.select('ndvi'), ndviVis, 'NDVI Composite')\n",
        "\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "63058e4289b049cb8f66cc5ff95cfd64",
            "f429430df7b1499fa326c5619f172008",
            "2dc4e78181f944889d1658d9fcb21328",
            "8b012ba42aeb44c6bdfe1bfcc06f1495",
            "aba348f692fc4ec1b2b5b8fce95cf0ab",
            "3067a74706f4420a8da5acc48d2b9392",
            "62f54eccd80d43398446db7fa1350e8c",
            "99e890b1f10846488bd6abcb8d618c6c",
            "e64d7324016c4411bfafd1b462f6c188",
            "5f9c960fa0cb4fadbd5c08bbf77cc6b9",
            "735090dd3d9a49cab42b2eaf87bdd36e",
            "d743ef0ae22e405c9750c94257baf5db",
            "766c969a9473439d83601e26b3f0b670",
            "4a5b2f650dde4d03a367ff633d93e36f",
            "d0ff52cf09384976af4896cfc52c3e15",
            "6186ecc8b6bb4590bf9fa99cee0eb1e8",
            "72a1bcc058294e7a90fa011f005117fc",
            "c2bc4b72c1c348518f7e21733c59c351",
            "d3857cf6a6be452dabf93e9d85a45084",
            "89eb2af479f54ab4a0f8f59fea9fd522",
            "c03c6dd952b74d309355fefea1994223",
            "60b0e9289cae454c84ec8b05149e0a3e",
            "fe005aac5d444c8daf357de078e59eaa",
            "6e40873192854e068a2788e9e5c5dc27",
            "27508ae3150249d9b70f481edc2460e1"
          ]
        },
        "id": "h1V0oxQ8vb7L",
        "outputId": "56e3eb39-225e-497c-aff2-603e51811b1a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[12.130969369851766, 107.61303468448625], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63058e4289b049cb8f66cc5ff95cfd64"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "javascript = '''\n",
        "var admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\");\n",
        "\n",
        "var karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))\n",
        "\n",
        "var visParams = {color: 'red'}\n",
        "Map.centerObject(karnataka)\n",
        "Map.addLayer(karnataka, visParams, 'Karnataka Districts')\n",
        "\n",
        "'''\n",
        "lines = geemap.js_snippet_to_py(\n",
        "    javascript, add_new_cell=False,\n",
        "    import_ee=True, import_geemap=True, show_map=True)\n",
        "for line in lines:\n",
        "    print(line.rstrip())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "z5HTe3VdwLSP",
        "outputId": "0982b0ce-e17f-4e20-9598-bdc8cbd77138"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "import ee\n",
            "import geemap\n",
            "\n",
            "m = geemap.Map()\n",
            "\n",
            "admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\")\n",
            "\n",
            "karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))\n",
            "\n",
            "visParams = {'color': 'red'}\n",
            "m.centerObject(karnataka)\n",
            "m.addLayer(karnataka, visParams, 'Karnataka Districts')\n",
            "\n",
            "m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = geemap.Map()\n",
        "\n",
        "admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\")\n",
        "\n",
        "karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))\n",
        "\n",
        "visParams = {'color': 'red'}\n",
        "m.centerObject(karnataka)\n",
        "m.addLayer(karnataka, visParams, 'Karnataka Districts')\n",
        "\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "6bedeabccc794f66b3372b1752f74916",
            "77d9ec103ea94480acbcc7845069a3d7",
            "90d1cfe5efe448f98ecf384a000d92ed",
            "ae92e716245d4a65afc399eae23c3de0",
            "b413e3be240f49249b4a8fd935f0ac03",
            "956a9cebeb5a40a0af06e9e9d685e88d",
            "f32e34ac16de42ee8e4b1539916b9ec7",
            "832ec5fdc7b44aeabf1c0009887580ac",
            "d3e28085807742f28f559bfed76ecfdf",
            "eff088274b314df8a7d64f5894ba4978",
            "60d4746b39d045c79cd68814b3499196",
            "96957db393374a1f8f2743a223e36eae",
            "49a95ff9c9714748b001c1b3d45e718f",
            "7ead61df462043cdaa2924d63efa8b63",
            "a3e433570e3e42578308d64d29c9c36d",
            "8bf6fb83a10947879c915245659d7681",
            "52785389eef743a4b80a18744b6b21bd",
            "e7829e5031d34f639770450a49497ed3",
            "511264858cc14665bb55e8cc8e6313d3",
            "4d9a7c1cded64b6d9e557bf6f6abbdfb",
            "b832f1979ffe45959accf9395b8112ae",
            "e66d71c1f5f54ba29d26df21c9be5969",
            "0cff7b6904a74611aaa5d7b8f5466d37",
            "f11a1beba7c244d8a0a15ecc5fdca9b5",
            "70450a350e1f46638a3067ac5bfac631"
          ]
        },
        "id": "yMdjXPcxwgH_",
        "outputId": "36bba8bf-2ee0-49e3-adb9-c1e4684b3a2c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[0, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDataGUI(childr…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6bedeabccc794f66b3372b1752f74916"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766])\n",
        "s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "rgbVis = {\n",
        "  'min': 0.0,\n",
        "  'max': 3000,\n",
        "  'bands': ['B4', 'B3', 'B2'],\n",
        "}\n",
        "\n",
        "filtered = s2 \\\n",
        "  .filter(ee.Filter.date('2019-01-01', '2019-01-31')) \\\n",
        "  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30)) \\\n",
        "  .filter(ee.Filter.bounds(geometry)) \\\n",
        "\n",
        "# Load the Cloud Score+ collection\n",
        "csPlus = ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')\n",
        "csPlusBands = csPlus.first().bandNames()\n",
        "\n",
        "# We need to add Cloud Score + bands to each Sentinel-2\n",
        "# image in the collection\n",
        "# This is done using the linkCollection() function\n",
        "filteredS2WithCs = filtered.linkCollection(csPlus, csPlusBands)\n",
        "\n",
        "# Function to mask pixels with low CS+ QA scores.\n",
        "def maskLowQA(image):\n",
        "  qaBand = 'cs'\n",
        "  clearThreshold = 0.5\n",
        "  mask = image.select(qaBand).gte(clearThreshold)\n",
        "  return image.updateMask(mask)\n",
        "\n",
        "filteredMasked = filteredS2WithCs \\\n",
        "  .map(maskLowQA)\n",
        "\n",
        "# Write a function that computes NDVI for an image and adds it as a band\n",
        "def addNDVI(image):\n",
        "  ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi')\n",
        "  return image.addBands(ndvi)\n",
        "\n",
        "withNdvi = filteredMasked.map(addNDVI)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Qd6agNMIxKiD",
        "outputId": "c746905a-71c2-468e-83cd-091bc0f03d56"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Export all images\n",
        "image_ids = withNdvi.aggregate_array('system:index').getInfo()\n",
        "print('Total images: ', len(image_ids))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "jfM5xY_UxRxo",
        "outputId": "b350c311-6b10-4093-9185-39795ee131dc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total images:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export with 100m resolution for this demo\n",
        "for i, image_id in enumerate(image_ids):\n",
        "  image = ee.Image(withNdvi.filter(ee.Filter.eq('system:index', image_id)).first())\n",
        "  task = ee.batch.Export.image.toDrive(**{\n",
        "    'image': image.select('ndvi'),\n",
        "    'description': 'Image Export {}'.format(i+1),\n",
        "    'fileNamePrefix': image_id,\n",
        "    'folder':'earthengine',\n",
        "    'scale': 100,\n",
        "    'region': image.geometry(),\n",
        "    'maxPixels': 1e10\n",
        "  })\n",
        "  task.start()\n",
        "  print('Started Task: ', i+1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "dkjucFlexuII",
        "outputId": "13d45ef4-d561-4cd1-d301-cc5da063830b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Task:  1\n",
            "Started Task:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "lsib = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')\n",
        "australia = lsib.filter(ee.Filter.eq('country_na', 'Australia'))\n",
        "geometry = australia.geometry()\n",
        "\n",
        "terraclimate = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')\n",
        "tmax = terraclimate.select('tmmx')\n",
        "\n",
        "def scale(image):\n",
        "  return image.multiply(0.1) \\\n",
        "    .copyProperties(image,['system:time_start'])\n",
        "\n",
        "tmaxScaled = tmax.map(scale)\n",
        "\n",
        "filtered = tmaxScaled \\\n",
        "  .filter(ee.Filter.date('2020-01-01', '2020-03-01')) \\\n",
        "  .filter(ee.Filter.bounds(geometry))\n",
        "\n",
        "image_ids = filtered.aggregate_array('system:index').getInfo()\n",
        "print('Total images: ', len(image_ids))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "S1fLTgxKzOpQ",
        "outputId": "2216530e-ea8d-4d32-b4b2-10ea651d7b4e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total images:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, image_id in enumerate(image_ids):\n",
        "    exportImage = ee.Image(filtered.filter(ee.Filter.eq('system:index', image_id)).first())\n",
        "    # Clip the image to the region geometry\n",
        "    clippedImage = exportImage.clip(geometry)\n",
        "\n",
        "    ## Create the export task using ee.batch.Export.image.toDrive()\n",
        "    task = ee.batch.Export.image.toDrive(**{\n",
        "    'image': image.select('tmmx'),\n",
        "    'description': 'Image Export {}'.format(i+1),\n",
        "    'fileNamePrefix': image_id,\n",
        "    'folder':'earthengine',\n",
        "    'scale': 100,\n",
        "    'region': image.geometry(),\n",
        "    'maxPixels': 1e10\n",
        "  })\n",
        "  ## Start the task\n",
        "    task.start()\n",
        "    print('Started Task: ', i+1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "Gjxu3dfkzUWS",
        "outputId": "fc8fb5f5-a389-4270-b0d1-957ee82faaa0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Task:  1\n",
            "Started Task:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    !pip install --upgrade xee\n",
        "!pip install xarray[complete]\n",
        "import ee\n",
        "import xarray\n",
        "\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ARZBPqBV1T5z",
        "outputId": "a3d99bbc-04b3-40be-9462-b73ef78e003b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "geometry = ee.Geometry.Point([82.60759592318209, 27.163481733946846])\n",
        "s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n",
        "\n",
        "filtered = s2 \\\n",
        "  .filter(ee.Filter.date('2017-01-01', '2018-01-01')) \\\n",
        "  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30)) \\\n",
        "  .filter(ee.Filter.bounds(geometry))\n",
        "\n",
        "# Load the Cloud Score+ collection\n",
        "csPlus = ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')\n",
        "csPlusBands = csPlus.first().bandNames()\n",
        "\n",
        "# We need to add Cloud Score + bands to each Sentinel-2\n",
        "# image in the collection\n",
        "# This is done using the linkCollection() function\n",
        "filteredS2WithCs = filtered.linkCollection(csPlus, csPlusBands)\n",
        "\n",
        "# Function to mask pixels with low CS+ QA scores.\n",
        "def maskLowQA(image):\n",
        "  qaBand = 'cs'\n",
        "  clearThreshold = 0.5\n",
        "  mask = image.select(qaBand).gte(clearThreshold)\n",
        "  return image.updateMask(mask)\n",
        "\n",
        "filteredMasked = filteredS2WithCs \\\n",
        "  .map(maskLowQA)\n",
        "\n",
        "# Write a function that computes NDVI for an image and adds it as a band\n",
        "# Create a new image to overcome https://github.com/google/Xee/issues/88\n",
        "def addNDVI(image):\n",
        "  ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi')\n",
        "  return image.multiply(0.0001).addBands(ndvi)\\\n",
        "    .copyProperties(image, ['system:time_start'])\n",
        "\n",
        "# Map the function over the collection\n",
        "withNdvi = filteredMasked.map(addNDVI)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "rd1A1XTP1ZPJ",
        "outputId": "bd4a197b-f9a1-441f-de92-b64ae9b606b9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
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              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
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              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
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              "\n",
              "                .geemap-colab .jupyter-button {\n",
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    {
      "cell_type": "code",
      "source": [
        "#Load ImageCollection as XArray Dataset\n",
        "ds = xarray.open_dataset(\n",
        "    withNdvi,\n",
        "    engine='ee',\n",
        "    crs='EPSG:3857',\n",
        "    scale=10,\n",
        "    geometry=geometry,\n",
        "    ee_mask_value=-9999,\n",
        ")\n",
        "\n",
        "ds"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "vNqIYCz915CV",
        "outputId": "9bc75bf3-b341-4a5d-ae05-98d3ecd6723f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
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              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
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          "metadata": {}
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              "    visualization_0_min:    0.0\n",
              "    visualization_0_name:   RGB\n",
              "    crs:                    EPSG:3857</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6f5220e1-f374-45f1-846a-c4d11663b4c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f5220e1-f374-45f1-846a-c4d11663b4c5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li><li><span class='xr-has-index'>X</span>: 1</li><li><span class='xr-has-index'>Y</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1755e41a-156a-4070-8b75-d41318208c0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1755e41a-156a-4070-8b75-d41318208c0c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2017-01-14T05:13:36.911000 ... 2...</div><input id='attrs-9574b64d-51b5-48d0-8f41-f718911ddb06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9574b64d-51b5-48d0-8f41-f718911ddb06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4286d133-bc8e-40b0-b52b-f393ee5c0af0' class='xr-var-data-in' type='checkbox'><label for='data-4286d133-bc8e-40b0-b52b-f393ee5c0af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-14T05:13:36.911000000&#x27;, &#x27;2017-01-24T05:12:45.473000000&#x27;,\n",
              "       &#x27;2017-02-03T05:16:47.710000000&#x27;, &#x27;2017-02-23T05:15:15.782000000&#x27;,\n",
              "       &#x27;2017-03-02T05:02:37.158000000&#x27;, &#x27;2017-04-04T05:17:46.080000000&#x27;,\n",
              "       &#x27;2017-04-11T05:10:23.600000000&#x27;, &#x27;2017-04-24T05:17:51.070000000&#x27;,\n",
              "       &#x27;2017-05-14T05:17:53.070000000&#x27;, &#x27;2017-05-21T05:10:27.600000000&#x27;,\n",
              "       &#x27;2017-06-03T05:17:55.680000000&#x27;, &#x27;2017-06-10T05:10:36.430000000&#x27;,\n",
              "       &#x27;2017-06-23T05:17:49.080000000&#x27;, &#x27;2017-07-13T05:17:50.080000000&#x27;,\n",
              "       &#x27;2017-09-06T05:17:13.220000000&#x27;, &#x27;2017-09-11T05:17:22.820000000&#x27;,\n",
              "       &#x27;2017-09-13T05:03:09.880000000&#x27;, &#x27;2017-09-26T05:13:00.880000000&#x27;,\n",
              "       &#x27;2017-10-08T05:02:47.810000000&#x27;, &#x27;2017-10-16T05:18:20.820000000&#x27;,\n",
              "       &#x27;2017-10-28T05:06:47.040000000&#x27;, &#x27;2017-11-05T05:20:32.640000000&#x27;,\n",
              "       &#x27;2017-11-10T05:14:04.930000000&#x27;, &#x27;2017-11-17T05:10:14.660000000&#x27;,\n",
              "       &#x27;2017-11-22T05:01:07.460000000&#x27;, &#x27;2017-11-25T05:19:31.340000000&#x27;,\n",
              "       &#x27;2017-11-30T05:17:13.760000000&#x27;, &#x27;2017-12-02T05:07:06.160000000&#x27;,\n",
              "       &#x27;2017-12-07T05:10:42.580000000&#x27;, &#x27;2017-12-15T05:14:59.020000000&#x27;,\n",
              "       &#x27;2017-12-22T05:07:59.410000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>9.196e+06</div><input id='attrs-4c94c4cd-3a91-4094-b1e4-8644e5909e68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c94c4cd-3a91-4094-b1e4-8644e5909e68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04902b83-1913-4ed5-bbe8-28c8d2635e2c' class='xr-var-data-in' type='checkbox'><label for='data-04902b83-1913-4ed5-bbe8-28c8d2635e2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([9195840.513825])</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'>3.144e+06</div><input id='attrs-6abe2cc6-6163-40ec-b381-8700d024f18b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6abe2cc6-6163-40ec-b381-8700d024f18b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-deea58df-755b-4b9e-b422-cbcf7bd587cb' class='xr-var-data-in' type='checkbox'><label for='data-deea58df-755b-4b9e-b422-cbcf7bd587cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3143916.52068])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3d172c2-028e-466b-8bae-1979a6060951' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3d172c2-028e-466b-8bae-1979a6060951' class='xr-section-summary' >Data variables: <span>(19)</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>B1</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a476be2-6ce4-4282-af93-f5c6b77697a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a476be2-6ce4-4282-af93-f5c6b77697a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-771ceea9-c8c8-4af9-9e8d-47a59ef3aa95' class='xr-var-data-in' type='checkbox'><label for='data-771ceea9-c8c8-4af9-9e8d-47a59ef3aa95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B1</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[1830, 1830]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[60, 0, 600000, 0, -60, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B2</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48ba06b4-cce2-4573-a60e-2ac1856dae35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48ba06b4-cce2-4573-a60e-2ac1856dae35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2bcc5f8-2701-479b-8e24-8869e0f43784' class='xr-var-data-in' type='checkbox'><label for='data-c2bcc5f8-2701-479b-8e24-8869e0f43784' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B2</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B3</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8bb6f4cb-7397-402d-abf6-e13a7e1f94a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bb6f4cb-7397-402d-abf6-e13a7e1f94a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58f209ba-96e6-43d7-83d7-f345908170f4' class='xr-var-data-in' type='checkbox'><label for='data-58f209ba-96e6-43d7-83d7-f345908170f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B3</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B4</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e1c3b241-511b-436c-9e45-5bdbb87d1c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1c3b241-511b-436c-9e45-5bdbb87d1c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74ece2f5-5344-4a8f-a212-460b183c6016' class='xr-var-data-in' type='checkbox'><label for='data-74ece2f5-5344-4a8f-a212-460b183c6016' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B4</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B5</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1048e04b-8f1c-4f1c-824a-5288cecb61d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1048e04b-8f1c-4f1c-824a-5288cecb61d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0335e73-6d32-4785-bfb4-42934061da81' class='xr-var-data-in' type='checkbox'><label for='data-f0335e73-6d32-4785-bfb4-42934061da81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B5</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B6</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5d2981d-c880-455f-8f86-830bf8601f55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5d2981d-c880-455f-8f86-830bf8601f55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-311762e1-6386-4dd5-81ab-2ff2789e7420' class='xr-var-data-in' type='checkbox'><label for='data-311762e1-6386-4dd5-81ab-2ff2789e7420' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B6</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B7</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b15ad76e-6153-4ef4-b47a-c02643fcc8b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b15ad76e-6153-4ef4-b47a-c02643fcc8b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfa00897-79b0-4ca2-9474-67223ccdb381' class='xr-var-data-in' type='checkbox'><label for='data-dfa00897-79b0-4ca2-9474-67223ccdb381' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B7</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B8</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d026f692-aea4-44b7-a3d9-225c9716b6a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d026f692-aea4-44b7-a3d9-225c9716b6a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7037864e-4893-485e-a995-9621b6ac53e2' class='xr-var-data-in' type='checkbox'><label for='data-7037864e-4893-485e-a995-9621b6ac53e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B8</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B8A</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf458ace-f73e-4997-bbcf-ae4c82cb77a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf458ace-f73e-4997-bbcf-ae4c82cb77a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ffb7183-da91-47bd-852d-6f58c2dbb246' class='xr-var-data-in' type='checkbox'><label for='data-4ffb7183-da91-47bd-852d-6f58c2dbb246' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B8A</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B9</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-afd930aa-d8b5-471b-bc40-7c01513e357c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afd930aa-d8b5-471b-bc40-7c01513e357c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83104679-ca7c-4e7a-b027-4df7ecff03b7' class='xr-var-data-in' type='checkbox'><label for='data-83104679-ca7c-4e7a-b027-4df7ecff03b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B9</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[1830, 1830]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[60, 0, 600000, 0, -60, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B10</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d66afbb-9c73-42d6-aed8-8099a993019d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d66afbb-9c73-42d6-aed8-8099a993019d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7b02fde-3094-4cab-8686-f25399a2f904' class='xr-var-data-in' type='checkbox'><label for='data-f7b02fde-3094-4cab-8686-f25399a2f904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B10</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[1830, 1830]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[60, 0, 600000, 0, -60, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B11</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a1d2cd4-138b-4b18-a573-4473b1960014' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a1d2cd4-138b-4b18-a573-4473b1960014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b697173-a8ab-4dcc-b059-8b57e6d4c286' class='xr-var-data-in' type='checkbox'><label for='data-4b697173-a8ab-4dcc-b059-8b57e6d4c286' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B11</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B12</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b965780-5fd3-4e38-8f2f-f9d52410debf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b965780-5fd3-4e38-8f2f-f9d52410debf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d4f1d30-498f-4d5b-8fdd-0baac7cfbd73' class='xr-var-data-in' type='checkbox'><label for='data-1d4f1d30-498f-4d5b-8fdd-0baac7cfbd73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>B12</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QA10</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5af366c-0df2-4dbb-9249-1999f9305325' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5af366c-0df2-4dbb-9249-1999f9305325' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5643e40c-ef4c-4205-951f-7a8e93dd6885' class='xr-var-data-in' type='checkbox'><label for='data-5643e40c-ef4c-4205-951f-7a8e93dd6885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>QA10</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QA20</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9c995bb-bc34-4acd-b104-0eee1b505f31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9c995bb-bc34-4acd-b104-0eee1b505f31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57032ad9-abdb-47e7-91b0-beaf9f156335' class='xr-var-data-in' type='checkbox'><label for='data-57032ad9-abdb-47e7-91b0-beaf9f156335' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>QA20</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 429496.7295}</dd><dt><span>dimensions :</span></dt><dd>[5490, 5490]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[20, 0, 600000, 0, -20, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QA60</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6886574-6205-4265-af14-9a91f0ef5c3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6886574-6205-4265-af14-9a91f0ef5c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e32ea8-d36f-43da-85a9-eb379ebf6b42' class='xr-var-data-in' type='checkbox'><label for='data-27e32ea8-d36f-43da-85a9-eb379ebf6b42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>QA60</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 6.5535000000000005}</dd><dt><span>dimensions :</span></dt><dd>[1830, 1830]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[60, 0, 600000, 0, -60, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cs</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-91c4e0da-024f-498b-a380-05b337ddf0c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91c4e0da-024f-498b-a380-05b337ddf0c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e4482fe-14ca-4fbe-8335-24e6285b838b' class='xr-var-data-in' type='checkbox'><label for='data-1e4482fe-14ca-4fbe-8335-24e6285b838b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>cs</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cs_cdf</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-883791d0-6313-475a-a666-7549d33d3f43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-883791d0-6313-475a-a666-7549d33d3f43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-250d3ca0-900e-4fec-a9fb-5d2d49ab73d2' class='xr-var-data-in' type='checkbox'><label for='data-250d3ca0-900e-4fec-a9fb-5d2d49ab73d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>cs_cdf</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndvi</span></div><div class='xr-var-dims'>(time, X, Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a35f7d3-4579-4351-a1ad-febdf7b0fa3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a35f7d3-4579-4351-a1ad-febdf7b0fa3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ad40f80-1ae6-493a-a8aa-fdbf1dda0a51' class='xr-var-data-in' type='checkbox'><label for='data-7ad40f80-1ae6-493a-a8aa-fdbf1dda0a51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>ndvi</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;max&#x27;: 1}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div><div class='xr-var-data'><pre>[31 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-601bcc88-d8b6-4187-a747-f92899c8d3b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-601bcc88-d8b6-4187-a747-f92899c8d3b7' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c59c93e-210e-4e37-9a17-454ceef9d518' class='xr-index-data-in' type='checkbox'/><label for='index-5c59c93e-210e-4e37-9a17-454ceef9d518' 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;2017-01-14 05:13:36.911000&#x27;, &#x27;2017-01-24 05:12:45.473000&#x27;,\n",
              "               &#x27;2017-02-03 05:16:47.710000&#x27;, &#x27;2017-02-23 05:15:15.782000&#x27;,\n",
              "               &#x27;2017-03-02 05:02:37.158000&#x27;, &#x27;2017-04-04 05:17:46.080000&#x27;,\n",
              "               &#x27;2017-04-11 05:10:23.600000&#x27;, &#x27;2017-04-24 05:17:51.070000&#x27;,\n",
              "               &#x27;2017-05-14 05:17:53.070000&#x27;, &#x27;2017-05-21 05:10:27.600000&#x27;,\n",
              "               &#x27;2017-06-03 05:17:55.680000&#x27;, &#x27;2017-06-10 05:10:36.430000&#x27;,\n",
              "               &#x27;2017-06-23 05:17:49.080000&#x27;, &#x27;2017-07-13 05:17:50.080000&#x27;,\n",
              "               &#x27;2017-09-06 05:17:13.220000&#x27;, &#x27;2017-09-11 05:17:22.820000&#x27;,\n",
              "               &#x27;2017-09-13 05:03:09.880000&#x27;, &#x27;2017-09-26 05:13:00.880000&#x27;,\n",
              "               &#x27;2017-10-08 05:02:47.810000&#x27;, &#x27;2017-10-16 05:18:20.820000&#x27;,\n",
              "               &#x27;2017-10-28 05:06:47.040000&#x27;, &#x27;2017-11-05 05:20:32.640000&#x27;,\n",
              "               &#x27;2017-11-10 05:14:04.930000&#x27;, &#x27;2017-11-17 05:10:14.660000&#x27;,\n",
              "               &#x27;2017-11-22 05:01:07.460000&#x27;, &#x27;2017-11-25 05:19:31.340000&#x27;,\n",
              "               &#x27;2017-11-30 05:17:13.760000&#x27;, &#x27;2017-12-02 05:07:06.160000&#x27;,\n",
              "               &#x27;2017-12-07 05:10:42.580000&#x27;, &#x27;2017-12-15 05:14:59.020000&#x27;,\n",
              "               &#x27;2017-12-22 05:07:59.410000&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</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-b9d20e7a-a7e7-45ff-83a9-7903e660e059' class='xr-index-data-in' type='checkbox'/><label for='index-b9d20e7a-a7e7-45ff-83a9-7903e660e059' 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([9195840.513825132], 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-0709f4de-0781-4a01-8163-5049d6d37b41' class='xr-index-data-in' type='checkbox'/><label for='index-0709f4de-0781-4a01-8163-5049d6d37b41' 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([3143916.520680158], dtype=&#x27;float64&#x27;, name=&#x27;Y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80d3970a-84bf-4ceb-a8cf-13fa80927128' class='xr-section-summary-in' type='checkbox'  ><label for='section-80d3970a-84bf-4ceb-a8cf-13fa80927128' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>date_range :</span></dt><dd>[1435017600000, 1647993600000]</dd><dt><span>description :</span></dt><dd>&lt;p&gt;Sentinel-2 is a wide-swath, high-resolution, multi-spectral\n",
              "imaging mission supporting Copernicus Land Monitoring studies,\n",
              "including the monitoring of vegetation, soil and water cover,\n",
              "as well as observation of inland waterways and coastal areas.&lt;/p&gt;&lt;p&gt;The Sentinel-2 data contain 13 UINT16 spectral bands representing\n",
              "TOA reflectance scaled by 10000. See the &lt;a href=&quot;https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook&quot;&gt;Sentinel-2 User Handbook&lt;/a&gt;\n",
              "for details. In addition, three QA bands are present where one\n",
              "(QA60) is a bitmask band with cloud mask information. For more\n",
              "details, &lt;a href=&quot;https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks&quot;&gt;see the full explanation of how cloud masks are computed.&lt;/a&gt;&lt;/p&gt;&lt;p&gt;Each Sentinel-2 product (zip archive) may contain multiple\n",
              "granules. Each granule becomes a separate Earth Engine asset.\n",
              "EE asset ids for Sentinel-2 assets have the following format:\n",
              "COPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the\n",
              "first numeric part represents the sensing date and time, the\n",
              "second numeric part represents the product generation date and\n",
              "time, and the final 6-character string is a unique granule identifier\n",
              "indicating its UTM grid reference (see &lt;a href=&quot;https://en.wikipedia.org/wiki/Military_Grid_Reference_System&quot;&gt;MGRS&lt;/a&gt;).&lt;/p&gt;&lt;p&gt;The Level-2 data produced by ESA can be found in the collection\n",
              "&lt;a href=&quot;COPERNICUS_S2_SR&quot;&gt;COPERNICUS/S2_SR&lt;/a&gt;.&lt;/p&gt;&lt;p&gt;Clouds can be mostly removed by using\n",
              "&lt;a href=&quot;COPERNICUS_S2_CLOUD_PROBABILITY&quot;&gt;COPERNICUS/S2_CLOUD_PROBABILITY&lt;/a&gt;.\n",
              "See\n",
              "&lt;a href=&quot;https://developers.google.com/earth-engine/tutorials/community/sentinel-2-s2cloudless&quot;&gt;this tutorial&lt;/a&gt;\n",
              "explaining how to apply the cloud mask.&lt;/p&gt;&lt;p&gt;For more details on Sentinel-2 radiometric resolution, &lt;a href=&quot;https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric&quot;&gt;see this page&lt;/a&gt;.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Provider: &lt;a href=&quot;https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-1c&quot;&gt;European Union/ESA/Copernicus&lt;/a&gt;&lt;/b&gt;&lt;br&gt;&lt;p&gt;&lt;b&gt;Revisit Interval&lt;/b&gt;&lt;br&gt;\n",
              "  5 days\n",
              "&lt;/p&gt;&lt;p&gt;&lt;b&gt;Bands&lt;/b&gt;&lt;table class=&quot;eecat&quot;&gt;&lt;tr&gt;&lt;th scope=&quot;col&quot;&gt;Name&lt;/th&gt;&lt;th scope=&quot;col&quot;&gt;Description&lt;/th&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B1&lt;/td&gt;&lt;td&gt;&lt;p&gt;Aerosols&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B2&lt;/td&gt;&lt;td&gt;&lt;p&gt;Blue&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B3&lt;/td&gt;&lt;td&gt;&lt;p&gt;Green&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B4&lt;/td&gt;&lt;td&gt;&lt;p&gt;Red&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B5&lt;/td&gt;&lt;td&gt;&lt;p&gt;Red Edge 1&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B6&lt;/td&gt;&lt;td&gt;&lt;p&gt;Red Edge 2&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B7&lt;/td&gt;&lt;td&gt;&lt;p&gt;Red Edge 3&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B8&lt;/td&gt;&lt;td&gt;&lt;p&gt;NIR&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B8A&lt;/td&gt;&lt;td&gt;&lt;p&gt;Red Edge 4&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B9&lt;/td&gt;&lt;td&gt;&lt;p&gt;Water vapor&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B10&lt;/td&gt;&lt;td&gt;&lt;p&gt;Cirrus&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B11&lt;/td&gt;&lt;td&gt;&lt;p&gt;SWIR 1&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;B12&lt;/td&gt;&lt;td&gt;&lt;p&gt;SWIR 2&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;QA10&lt;/td&gt;&lt;td&gt;&lt;p&gt;Always empty&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;QA20&lt;/td&gt;&lt;td&gt;&lt;p&gt;Always empty&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;QA60&lt;/td&gt;&lt;td&gt;&lt;p&gt;Cloud mask&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=100&gt;\n",
              "      Bitmask for QA60\n",
              "&lt;ul&gt;&lt;li&gt;\n",
              "          Bit 10: Opaque clouds\n",
              "&lt;ul&gt;&lt;li&gt;0: No opaque clouds&lt;/li&gt;&lt;li&gt;1: Opaque clouds present&lt;/li&gt;&lt;/ul&gt;&lt;/li&gt;&lt;li&gt;\n",
              "          Bit 11: Cirrus clouds\n",
              "&lt;ul&gt;&lt;li&gt;0: No cirrus clouds&lt;/li&gt;&lt;li&gt;1: Cirrus clouds present&lt;/li&gt;&lt;/ul&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;p&gt;&lt;b&gt;Image Properties&lt;/b&gt;&lt;table class=&quot;eecat&quot;&gt;&lt;tr&gt;&lt;th scope=&quot;col&quot;&gt;Name&lt;/th&gt;&lt;th scope=&quot;col&quot;&gt;Type&lt;/th&gt;&lt;th scope=&quot;col&quot;&gt;Description&lt;/th&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;CLOUDY_PIXEL_PERCENTAGE&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Granule-specific cloudy pixel percentage taken from the original metadata&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;CLOUD_COVERAGE_ASSESSMENT&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Cloudy pixel percentage for the whole archive that\n",
              "contains this granule. Taken from the original metadata&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;DATASTRIP_ID&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Unique identifier of the datastrip Product Data Item (PDI)&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;DATATAKE_IDENTIFIER&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Uniquely identifies a given Datatake. The ID contains\n",
              "the Sentinel-2 satellite, start date and time, absolute orbit\n",
              "number, and processing baseline.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;DATATAKE_TYPE&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;MSI operation mode&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;DEGRADED_MSI_DATA_PERCENTAGE&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Percentage of degraded MSI and ancillary data&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;FORMAT_CORRECTNESS&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Synthesis of the On-Line Quality Control (OLQC) checks\n",
              "performed at granule (Product_Syntax) and datastrip (Product\n",
              "Syntax and DS_Consistency) levels&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;GENERAL_QUALITY&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Synthesis of the OLQC checks performed at the datastrip level (Relative_Orbit_Number)&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;GENERATION_TIME&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Product generation time&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;GEOMETRIC_QUALITY&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Synthesis of the OLQC checks performed at the datastrip level (Attitude_Quality_Indicator)&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;GRANULE_ID&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Unique identifier of the granule PDI (PDI_ID)&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B1&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B1 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B2&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B2 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B3&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B3 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B4&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B4 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B5&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B5 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B6&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B6 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B7&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B7 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B8&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B8 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B8A&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B8a and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B9&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B9 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B10&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B10 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B11&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B11 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_AZIMUTH_ANGLE_B12&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence azimuth angle average for band B12 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B1&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B1 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B2&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B2 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B3&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B3 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B4&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B4 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B5&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B5 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B6&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B6 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B7&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B7 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B8&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B8 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B8A&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B8a and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B9&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B9 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B10&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B10 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B11&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B11 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_INCIDENCE_ZENITH_ANGLE_B12&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing viewing incidence zenith angle average for band B12 and for all detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_SOLAR_AZIMUTH_ANGLE&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing sun azimuth angle average for all bands and detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MEAN_SOLAR_ZENITH_ANGLE&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean value containing sun zenith angle average for all bands and detectors&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;MGRS_TILE&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;US-Military Grid Reference System (MGRS) tile&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;PROCESSING_BASELINE&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Configuration baseline used at the time of the product\n",
              "generation in terms of processor software version and major Ground\n",
              "Image Processing Parameters (GIPP) version&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;PRODUCT_ID&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;The full id of the original Sentinel-2 product&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;RADIOMETRIC_QUALITY&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Based on the OLQC reports contained in the Datastrips/QI_DATA with RADIOMETRIC_QUALITY checklist name&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;REFLECTANCE_CONVERSION_CORRECTION&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Earth-Sun distance correction factor&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SENSING_ORBIT_DIRECTION&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Imaging orbit direction&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SENSING_ORBIT_NUMBER&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Imaging orbit number&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SENSOR_QUALITY&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Synthesis of the OLQC checks performed at granule\n",
              "(Missing_Lines, Corrupted_ISP, and Sensing_Time) and datastrip\n",
              "(Degraded_SAD and Datation_Model) levels&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B1&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B1&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B2&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B2&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B3&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B3&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B4&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B4&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B5&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B5&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B6&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B6&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B7&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B7&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B8&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B8&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B8A&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B8a&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B9&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B9&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B10&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B10&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B11&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B11&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SOLAR_IRRADIANCE_B12&lt;/td&gt;&lt;td&gt;DOUBLE&lt;/td&gt;&lt;td&gt;&lt;p&gt;Mean solar exoatmospheric irradiance for band B12&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;SPACECRAFT_NAME&lt;/td&gt;&lt;td&gt;STRING&lt;/td&gt;&lt;td&gt;&lt;p&gt;Sentinel-2 spacecraft name: Sentinel-2A, Sentinel-2B&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;p&gt;&lt;b&gt;Terms of Use&lt;/b&gt;&lt;br&gt;&lt;p&gt;The use of Sentinel data is governed by the &lt;a href=&quot;https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/Sentinel_Data_Terms_and_Conditions.pdf&quot;&gt;Copernicus\n",
              "Sentinel Data Terms and Conditions.&lt;/a&gt;&lt;/p&gt;&lt;style&gt;\n",
              "  table.eecat {\n",
              "  border: 1px solid black;\n",
              "  border-collapse: collapse;\n",
              "  font-size: 13px;\n",
              "  }\n",
              "  table.eecat td, tr, th {\n",
              "  text-align: left; vertical-align: top;\n",
              "  border: 1px solid gray; padding: 3px;\n",
              "  }\n",
              "  td.nobreak { white-space: nowrap; }\n",
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              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ndvi&#x27; (time: 31)&gt;\n",
              "array([0.5033776 , 0.63813955, 0.56427604, 0.74168795,        nan,\n",
              "       0.14802729, 0.15883777, 0.13142857, 0.07724426, 0.0973635 ,\n",
              "       0.08064516,        nan, 0.05203938, 0.10798947, 0.6990376 ,\n",
              "       0.5386646 , 0.640806  ,        nan,        nan, 0.40108788,\n",
              "       0.21703963, 0.19086178, 0.11948052, 0.11813843,        nan,\n",
              "       0.11563367, 0.07257407, 0.12698856,        nan, 0.36240703,\n",
              "       0.29904875], dtype=float32)\n",
              "Coordinates:\n",
              "  * time     (time) datetime64[ns] 2017-01-14T05:13:36.911000 ... 2017-12-22T...\n",
              "    X        float64 9.196e+06\n",
              "    Y        float64 3.144e+06\n",
              "Attributes:\n",
              "    id:             ndvi\n",
              "    data_type:      {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;m...\n",
              "    dimensions:     [10980, 10980]\n",
              "    crs:            EPSG:3857\n",
              "    crs_transform:  [10, 0, 600000, 0, -10, 3100020]</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'>'ndvi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-00c841e4-8d60-4d05-8317-71e43c6e36c5' class='xr-array-in' type='checkbox' checked><label for='section-00c841e4-8d60-4d05-8317-71e43c6e36c5' 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>0.5034 0.6381 0.5643 0.7417 nan ... 0.07257 0.127 nan 0.3624 0.299</span></div><div class='xr-array-data'><pre>array([0.5033776 , 0.63813955, 0.56427604, 0.74168795,        nan,\n",
              "       0.14802729, 0.15883777, 0.13142857, 0.07724426, 0.0973635 ,\n",
              "       0.08064516,        nan, 0.05203938, 0.10798947, 0.6990376 ,\n",
              "       0.5386646 , 0.640806  ,        nan,        nan, 0.40108788,\n",
              "       0.21703963, 0.19086178, 0.11948052, 0.11813843,        nan,\n",
              "       0.11563367, 0.07257407, 0.12698856,        nan, 0.36240703,\n",
              "       0.29904875], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-00c2a511-2716-4069-b040-b936d6bf36fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00c2a511-2716-4069-b040-b936d6bf36fa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2017-01-14T05:13:36.911000 ... 2...</div><input id='attrs-da2724a4-4ecb-4230-a14e-f056f893c0f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da2724a4-4ecb-4230-a14e-f056f893c0f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021c950e-4d62-4c74-9840-bad2098f3013' class='xr-var-data-in' type='checkbox'><label for='data-021c950e-4d62-4c74-9840-bad2098f3013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-14T05:13:36.911000000&#x27;, &#x27;2017-01-24T05:12:45.473000000&#x27;,\n",
              "       &#x27;2017-02-03T05:16:47.710000000&#x27;, &#x27;2017-02-23T05:15:15.782000000&#x27;,\n",
              "       &#x27;2017-03-02T05:02:37.158000000&#x27;, &#x27;2017-04-04T05:17:46.080000000&#x27;,\n",
              "       &#x27;2017-04-11T05:10:23.600000000&#x27;, &#x27;2017-04-24T05:17:51.070000000&#x27;,\n",
              "       &#x27;2017-05-14T05:17:53.070000000&#x27;, &#x27;2017-05-21T05:10:27.600000000&#x27;,\n",
              "       &#x27;2017-06-03T05:17:55.680000000&#x27;, &#x27;2017-06-10T05:10:36.430000000&#x27;,\n",
              "       &#x27;2017-06-23T05:17:49.080000000&#x27;, &#x27;2017-07-13T05:17:50.080000000&#x27;,\n",
              "       &#x27;2017-09-06T05:17:13.220000000&#x27;, &#x27;2017-09-11T05:17:22.820000000&#x27;,\n",
              "       &#x27;2017-09-13T05:03:09.880000000&#x27;, &#x27;2017-09-26T05:13:00.880000000&#x27;,\n",
              "       &#x27;2017-10-08T05:02:47.810000000&#x27;, &#x27;2017-10-16T05:18:20.820000000&#x27;,\n",
              "       &#x27;2017-10-28T05:06:47.040000000&#x27;, &#x27;2017-11-05T05:20:32.640000000&#x27;,\n",
              "       &#x27;2017-11-10T05:14:04.930000000&#x27;, &#x27;2017-11-17T05:10:14.660000000&#x27;,\n",
              "       &#x27;2017-11-22T05:01:07.460000000&#x27;, &#x27;2017-11-25T05:19:31.340000000&#x27;,\n",
              "       &#x27;2017-11-30T05:17:13.760000000&#x27;, &#x27;2017-12-02T05:07:06.160000000&#x27;,\n",
              "       &#x27;2017-12-07T05:10:42.580000000&#x27;, &#x27;2017-12-15T05:14:59.020000000&#x27;,\n",
              "       &#x27;2017-12-22T05:07:59.410000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>X</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.196e+06</div><input id='attrs-c0812989-00c3-4d9b-96ba-4609e08f3622' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0812989-00c3-4d9b-96ba-4609e08f3622' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2fa878c-a609-4bc2-964f-ae35e2191619' class='xr-var-data-in' type='checkbox'><label for='data-a2fa878c-a609-4bc2-964f-ae35e2191619' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9195840.51382513)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.144e+06</div><input id='attrs-f2cca82a-9c31-4135-b2a0-43fb7c955a75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2cca82a-9c31-4135-b2a0-43fb7c955a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-402aeb05-583d-4052-aa0c-b006f0b3cc3b' class='xr-var-data-in' type='checkbox'><label for='data-402aeb05-583d-4052-aa0c-b006f0b3cc3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3143916.52068016)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c034102a-5c6d-46e6-a390-47f472d70096' class='xr-section-summary-in' type='checkbox'  ><label for='section-c034102a-5c6d-46e6-a390-47f472d70096' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37a16442-0130-40a1-9cd9-7cec1fa73cf6' class='xr-index-data-in' type='checkbox'/><label for='index-37a16442-0130-40a1-9cd9-7cec1fa73cf6' 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;2017-01-14 05:13:36.911000&#x27;, &#x27;2017-01-24 05:12:45.473000&#x27;,\n",
              "               &#x27;2017-02-03 05:16:47.710000&#x27;, &#x27;2017-02-23 05:15:15.782000&#x27;,\n",
              "               &#x27;2017-03-02 05:02:37.158000&#x27;, &#x27;2017-04-04 05:17:46.080000&#x27;,\n",
              "               &#x27;2017-04-11 05:10:23.600000&#x27;, &#x27;2017-04-24 05:17:51.070000&#x27;,\n",
              "               &#x27;2017-05-14 05:17:53.070000&#x27;, &#x27;2017-05-21 05:10:27.600000&#x27;,\n",
              "               &#x27;2017-06-03 05:17:55.680000&#x27;, &#x27;2017-06-10 05:10:36.430000&#x27;,\n",
              "               &#x27;2017-06-23 05:17:49.080000&#x27;, &#x27;2017-07-13 05:17:50.080000&#x27;,\n",
              "               &#x27;2017-09-06 05:17:13.220000&#x27;, &#x27;2017-09-11 05:17:22.820000&#x27;,\n",
              "               &#x27;2017-09-13 05:03:09.880000&#x27;, &#x27;2017-09-26 05:13:00.880000&#x27;,\n",
              "               &#x27;2017-10-08 05:02:47.810000&#x27;, &#x27;2017-10-16 05:18:20.820000&#x27;,\n",
              "               &#x27;2017-10-28 05:06:47.040000&#x27;, &#x27;2017-11-05 05:20:32.640000&#x27;,\n",
              "               &#x27;2017-11-10 05:14:04.930000&#x27;, &#x27;2017-11-17 05:10:14.660000&#x27;,\n",
              "               &#x27;2017-11-22 05:01:07.460000&#x27;, &#x27;2017-11-25 05:19:31.340000&#x27;,\n",
              "               &#x27;2017-11-30 05:17:13.760000&#x27;, &#x27;2017-12-02 05:07:06.160000&#x27;,\n",
              "               &#x27;2017-12-07 05:10:42.580000&#x27;, &#x27;2017-12-15 05:14:59.020000&#x27;,\n",
              "               &#x27;2017-12-22 05:07:59.410000&#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-76fe3df7-dac5-46d0-9fdb-d139cea5d953' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76fe3df7-dac5-46d0-9fdb-d139cea5d953' 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>id :</span></dt><dd>ndvi</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;max&#x27;: 1}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the time-series\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(10, 5)\n",
        "original_time_series.plot.line(\n",
        "    ax=ax, x='time',\n",
        "    marker='o', color='#66c2a4', linestyle='--', linewidth=1, markersize=4)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "5R3sWOLi7fA4",
        "outputId": "51b778cc-1cac-4bf3-deb2-eecde607f7fe"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Process Time-Series using XArray by creating regularly spaced time series\n",
        "time_series_resampled = original_time_series\\\n",
        "  .resample(time='5d').mean(dim='time')\n",
        "time_series_resampled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "nBfaPCCF9jVX",
        "outputId": "6a75e49a-57df-4e39-944c-2dce0f057ca3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "              nan,        nan, 0.6990376 , 0.58973527,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
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              "Coordinates:\n",
              "    X        float64 9.196e+06\n",
              "    Y        float64 3.144e+06\n",
              "  * time     (time) datetime64[ns] 2017-01-14 2017-01-19 ... 2017-12-20\n",
              "Attributes:\n",
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              "    dimensions:     [10980, 10980]\n",
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            ],
            "text/html": [
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              "\n",
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              "\n",
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              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
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              "  padding-right: 10px;\n",
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              "\n",
              ".xr-attrs dd {\n",
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              "\n",
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              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ndvi&#x27; (time: 69)&gt;\n",
              "array([0.5033776 ,        nan, 0.63813955,        nan, 0.56427604,\n",
              "              nan,        nan,        nan, 0.74168795,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
              "              nan, 0.14802729, 0.15883777,        nan,        nan,\n",
              "       0.13142857,        nan,        nan,        nan, 0.07724426,\n",
              "       0.0973635 ,        nan,        nan, 0.08064516,        nan,\n",
              "              nan,        nan, 0.05203938,        nan,        nan,\n",
              "              nan, 0.10798947,        nan,        nan,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
              "              nan,        nan, 0.6990376 , 0.58973527,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
              "       0.40108788,        nan, 0.21703963,        nan, 0.19086178,\n",
              "       0.11948052, 0.11813843,        nan, 0.11563367, 0.09978132,\n",
              "              nan,        nan, 0.36240703, 0.29904875], dtype=float32)\n",
              "Coordinates:\n",
              "    X        float64 9.196e+06\n",
              "    Y        float64 3.144e+06\n",
              "  * time     (time) datetime64[ns] 2017-01-14 2017-01-19 ... 2017-12-20\n",
              "Attributes:\n",
              "    id:             ndvi\n",
              "    data_type:      {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;m...\n",
              "    dimensions:     [10980, 10980]\n",
              "    crs:            EPSG:3857\n",
              "    crs_transform:  [10, 0, 600000, 0, -10, 3100020]</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'>'ndvi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 69</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e60c89a0-8057-45b7-a18e-d0bfde6c023f' class='xr-array-in' type='checkbox' checked><label for='section-e60c89a0-8057-45b7-a18e-d0bfde6c023f' 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>0.5034 nan 0.6381 nan 0.5643 nan ... 0.09978 nan nan 0.3624 0.299</span></div><div class='xr-array-data'><pre>array([0.5033776 ,        nan, 0.63813955,        nan, 0.56427604,\n",
              "              nan,        nan,        nan, 0.74168795,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
              "              nan, 0.14802729, 0.15883777,        nan,        nan,\n",
              "       0.13142857,        nan,        nan,        nan, 0.07724426,\n",
              "       0.0973635 ,        nan,        nan, 0.08064516,        nan,\n",
              "              nan,        nan, 0.05203938,        nan,        nan,\n",
              "              nan, 0.10798947,        nan,        nan,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
              "              nan,        nan, 0.6990376 , 0.58973527,        nan,\n",
              "              nan,        nan,        nan,        nan,        nan,\n",
              "       0.40108788,        nan, 0.21703963,        nan, 0.19086178,\n",
              "       0.11948052, 0.11813843,        nan, 0.11563367, 0.09978132,\n",
              "              nan,        nan, 0.36240703, 0.29904875], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ddee74f2-c4a8-4e32-89ac-7bd6bcff6eeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddee74f2-c4a8-4e32-89ac-7bd6bcff6eeb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>X</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.196e+06</div><input id='attrs-9bbd2958-4302-406f-96d1-9586df03438b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bbd2958-4302-406f-96d1-9586df03438b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b68eb9-e654-417f-a81b-1ad25a5cb9cb' class='xr-var-data-in' type='checkbox'><label for='data-35b68eb9-e654-417f-a81b-1ad25a5cb9cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9195840.51382513)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.144e+06</div><input id='attrs-e281322d-8be4-4637-b906-47291f8d5033' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e281322d-8be4-4637-b906-47291f8d5033' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f318506-cf49-4163-aecd-40a441bc0d1e' class='xr-var-data-in' type='checkbox'><label for='data-6f318506-cf49-4163-aecd-40a441bc0d1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3143916.52068016)</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'>2017-01-14 ... 2017-12-20</div><input id='attrs-5c01bc6c-1dbc-4f18-996c-27b0425249ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c01bc6c-1dbc-4f18-996c-27b0425249ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61103a95-5db0-4f9b-8f0f-3b24bb54f68c' class='xr-var-data-in' type='checkbox'><label for='data-61103a95-5db0-4f9b-8f0f-3b24bb54f68c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-14T00:00:00.000000000&#x27;, &#x27;2017-01-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-01-24T00:00:00.000000000&#x27;, &#x27;2017-01-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-03T00:00:00.000000000&#x27;, &#x27;2017-02-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-13T00:00:00.000000000&#x27;, &#x27;2017-02-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-23T00:00:00.000000000&#x27;, &#x27;2017-02-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-05T00:00:00.000000000&#x27;, &#x27;2017-03-10T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-15T00:00:00.000000000&#x27;, &#x27;2017-03-20T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-25T00:00:00.000000000&#x27;, &#x27;2017-03-30T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-04T00:00:00.000000000&#x27;, &#x27;2017-04-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-14T00:00:00.000000000&#x27;, &#x27;2017-04-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-24T00:00:00.000000000&#x27;, &#x27;2017-04-29T00:00:00.000000000&#x27;,\n",
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              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;5D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fe7e79b-0692-459d-9667-23c694b2ef7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2fe7e79b-0692-459d-9667-23c694b2ef7f' 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>id :</span></dt><dd>ndvi</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;max&#x27;: 1}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div></li></ul></div></div>"
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              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ndvi&#x27; (time: 69)&gt;\n",
              "array([0.5033776 , 0.5707586 , 0.63813955, 0.6012078 , 0.56427604,\n",
              "       0.608629  , 0.652982  , 0.697335  , 0.74168795, 0.66748035,\n",
              "       0.5932728 , 0.5190652 , 0.44485763, 0.37065002, 0.29644245,\n",
              "       0.22223487, 0.14802729, 0.15883777, 0.14970137, 0.14056496,\n",
              "       0.13142857, 0.11788249, 0.10433641, 0.09079034, 0.07724426,\n",
              "       0.0973635 , 0.09179072, 0.08621794, 0.08064516, 0.07349371,\n",
              "       0.06634227, 0.05919082, 0.05203938, 0.0660269 , 0.08001442,\n",
              "       0.09400195, 0.10798947, 0.16172111, 0.21545276, 0.2691844 ,\n",
              "       0.32291606, 0.3766477 , 0.43037936, 0.484111  , 0.5378427 ,\n",
              "       0.5915743 , 0.645306  , 0.6990376 , 0.58973527, 0.5627856 ,\n",
              "       0.53583604, 0.5088864 , 0.48193675, 0.45498714, 0.4280375 ,\n",
              "       0.40108788, 0.30906376, 0.21703963, 0.2039507 , 0.19086178,\n",
              "       0.11948052, 0.11813843, 0.11688605, 0.11563367, 0.09978132,\n",
              "       0.18732323, 0.27486512, 0.36240703, 0.29904875], dtype=float32)\n",
              "Coordinates:\n",
              "    X        float64 9.196e+06\n",
              "    Y        float64 3.144e+06\n",
              "  * time     (time) datetime64[ns] 2017-01-14 2017-01-19 ... 2017-12-20\n",
              "Attributes:\n",
              "    id:             ndvi\n",
              "    data_type:      {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;m...\n",
              "    dimensions:     [10980, 10980]\n",
              "    crs:            EPSG:3857\n",
              "    crs_transform:  [10, 0, 600000, 0, -10, 3100020]</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'>'ndvi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 69</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a54bfa6f-6add-4ade-93e5-3169b9a3e226' class='xr-array-in' type='checkbox' checked><label for='section-a54bfa6f-6add-4ade-93e5-3169b9a3e226' 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>0.5034 0.5708 0.6381 0.6012 0.5643 ... 0.1873 0.2749 0.3624 0.299</span></div><div class='xr-array-data'><pre>array([0.5033776 , 0.5707586 , 0.63813955, 0.6012078 , 0.56427604,\n",
              "       0.608629  , 0.652982  , 0.697335  , 0.74168795, 0.66748035,\n",
              "       0.5932728 , 0.5190652 , 0.44485763, 0.37065002, 0.29644245,\n",
              "       0.22223487, 0.14802729, 0.15883777, 0.14970137, 0.14056496,\n",
              "       0.13142857, 0.11788249, 0.10433641, 0.09079034, 0.07724426,\n",
              "       0.0973635 , 0.09179072, 0.08621794, 0.08064516, 0.07349371,\n",
              "       0.06634227, 0.05919082, 0.05203938, 0.0660269 , 0.08001442,\n",
              "       0.09400195, 0.10798947, 0.16172111, 0.21545276, 0.2691844 ,\n",
              "       0.32291606, 0.3766477 , 0.43037936, 0.484111  , 0.5378427 ,\n",
              "       0.5915743 , 0.645306  , 0.6990376 , 0.58973527, 0.5627856 ,\n",
              "       0.53583604, 0.5088864 , 0.48193675, 0.45498714, 0.4280375 ,\n",
              "       0.40108788, 0.30906376, 0.21703963, 0.2039507 , 0.19086178,\n",
              "       0.11948052, 0.11813843, 0.11688605, 0.11563367, 0.09978132,\n",
              "       0.18732323, 0.27486512, 0.36240703, 0.29904875], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-630b093c-8f51-455e-ae9f-9dee3ce6458d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-630b093c-8f51-455e-ae9f-9dee3ce6458d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>X</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.196e+06</div><input id='attrs-d516082a-d572-47e3-89f1-9c8cb9bdd025' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d516082a-d572-47e3-89f1-9c8cb9bdd025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79b658c-9894-4767-813c-401985c16c9c' class='xr-var-data-in' type='checkbox'><label for='data-b79b658c-9894-4767-813c-401985c16c9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9195840.51382513)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.144e+06</div><input id='attrs-bdc4299f-196b-461f-a1d1-f8b9c6f683ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bdc4299f-196b-461f-a1d1-f8b9c6f683ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0ddbdb-a615-4a6f-881c-c8ff7fdbdcbb' class='xr-var-data-in' type='checkbox'><label for='data-3c0ddbdb-a615-4a6f-881c-c8ff7fdbdcbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3143916.52068016)</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'>2017-01-14 ... 2017-12-20</div><input id='attrs-f549c609-0a81-4deb-8ff9-e6adb53c9846' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f549c609-0a81-4deb-8ff9-e6adb53c9846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0727647e-3197-4f48-a9c7-eee898c490ea' class='xr-var-data-in' type='checkbox'><label for='data-0727647e-3197-4f48-a9c7-eee898c490ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-14T00:00:00.000000000&#x27;, &#x27;2017-01-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-01-24T00:00:00.000000000&#x27;, &#x27;2017-01-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-03T00:00:00.000000000&#x27;, &#x27;2017-02-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-13T00:00:00.000000000&#x27;, &#x27;2017-02-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-23T00:00:00.000000000&#x27;, &#x27;2017-02-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-05T00:00:00.000000000&#x27;, &#x27;2017-03-10T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-15T00:00:00.000000000&#x27;, &#x27;2017-03-20T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-25T00:00:00.000000000&#x27;, &#x27;2017-03-30T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-04T00:00:00.000000000&#x27;, &#x27;2017-04-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-14T00:00:00.000000000&#x27;, &#x27;2017-04-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-24T00:00:00.000000000&#x27;, &#x27;2017-04-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-04T00:00:00.000000000&#x27;, &#x27;2017-05-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-14T00:00:00.000000000&#x27;, &#x27;2017-05-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-24T00:00:00.000000000&#x27;, &#x27;2017-05-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-03T00:00:00.000000000&#x27;, &#x27;2017-06-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-13T00:00:00.000000000&#x27;, &#x27;2017-06-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-23T00:00:00.000000000&#x27;, &#x27;2017-06-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-03T00:00:00.000000000&#x27;, &#x27;2017-07-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-13T00:00:00.000000000&#x27;, &#x27;2017-07-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-23T00:00:00.000000000&#x27;, &#x27;2017-07-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-02T00:00:00.000000000&#x27;, &#x27;2017-08-07T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-12T00:00:00.000000000&#x27;, &#x27;2017-08-17T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-22T00:00:00.000000000&#x27;, &#x27;2017-08-27T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-01T00:00:00.000000000&#x27;, &#x27;2017-09-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-11T00:00:00.000000000&#x27;, &#x27;2017-09-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-21T00:00:00.000000000&#x27;, &#x27;2017-09-26T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-10-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-11T00:00:00.000000000&#x27;, &#x27;2017-10-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-21T00:00:00.000000000&#x27;, &#x27;2017-10-26T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-31T00:00:00.000000000&#x27;, &#x27;2017-11-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-10T00:00:00.000000000&#x27;, &#x27;2017-11-15T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-20T00:00:00.000000000&#x27;, &#x27;2017-11-25T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-30T00:00:00.000000000&#x27;, &#x27;2017-12-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-12-10T00:00:00.000000000&#x27;, &#x27;2017-12-15T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-12-20T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6d3ecd8-c12e-4e57-9c69-0c3957969a20' class='xr-section-summary-in' type='checkbox'  ><label for='section-c6d3ecd8-c12e-4e57-9c69-0c3957969a20' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9b68745-7696-4c5c-b3e2-d40eebbbc192' class='xr-index-data-in' type='checkbox'/><label for='index-e9b68745-7696-4c5c-b3e2-d40eebbbc192' 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;2017-01-14&#x27;, &#x27;2017-01-19&#x27;, &#x27;2017-01-24&#x27;, &#x27;2017-01-29&#x27;,\n",
              "               &#x27;2017-02-03&#x27;, &#x27;2017-02-08&#x27;, &#x27;2017-02-13&#x27;, &#x27;2017-02-18&#x27;,\n",
              "               &#x27;2017-02-23&#x27;, &#x27;2017-02-28&#x27;, &#x27;2017-03-05&#x27;, &#x27;2017-03-10&#x27;,\n",
              "               &#x27;2017-03-15&#x27;, &#x27;2017-03-20&#x27;, &#x27;2017-03-25&#x27;, &#x27;2017-03-30&#x27;,\n",
              "               &#x27;2017-04-04&#x27;, &#x27;2017-04-09&#x27;, &#x27;2017-04-14&#x27;, &#x27;2017-04-19&#x27;,\n",
              "               &#x27;2017-04-24&#x27;, &#x27;2017-04-29&#x27;, &#x27;2017-05-04&#x27;, &#x27;2017-05-09&#x27;,\n",
              "               &#x27;2017-05-14&#x27;, &#x27;2017-05-19&#x27;, &#x27;2017-05-24&#x27;, &#x27;2017-05-29&#x27;,\n",
              "               &#x27;2017-06-03&#x27;, &#x27;2017-06-08&#x27;, &#x27;2017-06-13&#x27;, &#x27;2017-06-18&#x27;,\n",
              "               &#x27;2017-06-23&#x27;, &#x27;2017-06-28&#x27;, &#x27;2017-07-03&#x27;, &#x27;2017-07-08&#x27;,\n",
              "               &#x27;2017-07-13&#x27;, &#x27;2017-07-18&#x27;, &#x27;2017-07-23&#x27;, &#x27;2017-07-28&#x27;,\n",
              "               &#x27;2017-08-02&#x27;, &#x27;2017-08-07&#x27;, &#x27;2017-08-12&#x27;, &#x27;2017-08-17&#x27;,\n",
              "               &#x27;2017-08-22&#x27;, &#x27;2017-08-27&#x27;, &#x27;2017-09-01&#x27;, &#x27;2017-09-06&#x27;,\n",
              "               &#x27;2017-09-11&#x27;, &#x27;2017-09-16&#x27;, &#x27;2017-09-21&#x27;, &#x27;2017-09-26&#x27;,\n",
              "               &#x27;2017-10-01&#x27;, &#x27;2017-10-06&#x27;, &#x27;2017-10-11&#x27;, &#x27;2017-10-16&#x27;,\n",
              "               &#x27;2017-10-21&#x27;, &#x27;2017-10-26&#x27;, &#x27;2017-10-31&#x27;, &#x27;2017-11-05&#x27;,\n",
              "               &#x27;2017-11-10&#x27;, &#x27;2017-11-15&#x27;, &#x27;2017-11-20&#x27;, &#x27;2017-11-25&#x27;,\n",
              "               &#x27;2017-11-30&#x27;, &#x27;2017-12-05&#x27;, &#x27;2017-12-10&#x27;, &#x27;2017-12-15&#x27;,\n",
              "               &#x27;2017-12-20&#x27;],\n",
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              "    crs_transform:  [10, 0, 600000, 0, -10, 3100020]</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'>'ndvi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 69</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b57fe758-7e89-49a3-9b2d-45131c903766' class='xr-array-in' type='checkbox' checked><label for='section-b57fe758-7e89-49a3-9b2d-45131c903766' 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>nan 0.5708 0.6034 0.6012 0.5914 ... 0.1342 0.1873 0.2749 0.3121 nan</span></div><div class='xr-array-data'><pre>array([       nan, 0.5707586 , 0.60336864, 0.60120785, 0.591371  ,\n",
              "       0.6086291 , 0.6529821 , 0.6973351 , 0.70216787, 0.66748047,\n",
              "       0.5932729 , 0.5190653 , 0.44485772, 0.37065014, 0.29644257,\n",
              "       0.22223496, 0.17636673, 0.1521889 , 0.14970146, 0.14056507,\n",
              "       0.12995878, 0.11788259, 0.10433652, 0.09079044, 0.08846613,\n",
              "       0.08879959, 0.09179082, 0.08621804, 0.08011904, 0.07349381,\n",
              "       0.06634237, 0.05919092, 0.0590858 , 0.066027  , 0.08001452,\n",
              "       0.09400204, 0.1212376 , 0.1617212 , 0.21545285, 0.26918453,\n",
              "       0.32291615, 0.3766478 , 0.43037945, 0.48411107, 0.53784275,\n",
              "       0.5915744 , 0.645306  , 0.644693  , 0.6171862 , 0.5627857 ,\n",
              "       0.5358361 , 0.50888646, 0.48193678, 0.45498714, 0.4280375 ,\n",
              "       0.37939638, 0.30906373, 0.24335134, 0.20395069, 0.17143099,\n",
              "       0.1428269 , 0.11816832, 0.11688603, 0.110767  , 0.13424605,\n",
              "       0.18732321, 0.27486512, 0.31210697,        nan], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a06fe994-47d3-472b-b036-dbdde1ae866a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a06fe994-47d3-472b-b036-dbdde1ae866a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>X</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.196e+06</div><input id='attrs-eefe0ec9-25ea-4602-9188-6bdd4c1dd016' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eefe0ec9-25ea-4602-9188-6bdd4c1dd016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-866f3d6b-a743-4a88-a996-474f3fb45db2' class='xr-var-data-in' type='checkbox'><label for='data-866f3d6b-a743-4a88-a996-474f3fb45db2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9195840.51382513)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.144e+06</div><input id='attrs-07c10067-efcf-4828-8ec9-b4d927d3d18d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07c10067-efcf-4828-8ec9-b4d927d3d18d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-408dbd9a-8b6b-4bbd-b5cf-a3e292c1f801' class='xr-var-data-in' type='checkbox'><label for='data-408dbd9a-8b6b-4bbd-b5cf-a3e292c1f801' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3143916.52068016)</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'>2017-01-14 ... 2017-12-20</div><input id='attrs-1892dd43-cf8b-4bbe-bbb8-c28a0233d0a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1892dd43-cf8b-4bbe-bbb8-c28a0233d0a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a632df84-397d-4e30-b78e-82e7c10326ce' class='xr-var-data-in' type='checkbox'><label for='data-a632df84-397d-4e30-b78e-82e7c10326ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-14T00:00:00.000000000&#x27;, &#x27;2017-01-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-01-24T00:00:00.000000000&#x27;, &#x27;2017-01-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-03T00:00:00.000000000&#x27;, &#x27;2017-02-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-13T00:00:00.000000000&#x27;, &#x27;2017-02-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-23T00:00:00.000000000&#x27;, &#x27;2017-02-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-05T00:00:00.000000000&#x27;, &#x27;2017-03-10T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-15T00:00:00.000000000&#x27;, &#x27;2017-03-20T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-25T00:00:00.000000000&#x27;, &#x27;2017-03-30T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-04T00:00:00.000000000&#x27;, &#x27;2017-04-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-14T00:00:00.000000000&#x27;, &#x27;2017-04-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-24T00:00:00.000000000&#x27;, &#x27;2017-04-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-04T00:00:00.000000000&#x27;, &#x27;2017-05-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-14T00:00:00.000000000&#x27;, &#x27;2017-05-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-24T00:00:00.000000000&#x27;, &#x27;2017-05-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-03T00:00:00.000000000&#x27;, &#x27;2017-06-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-13T00:00:00.000000000&#x27;, &#x27;2017-06-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-23T00:00:00.000000000&#x27;, &#x27;2017-06-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-03T00:00:00.000000000&#x27;, &#x27;2017-07-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-13T00:00:00.000000000&#x27;, &#x27;2017-07-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-23T00:00:00.000000000&#x27;, &#x27;2017-07-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-02T00:00:00.000000000&#x27;, &#x27;2017-08-07T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-12T00:00:00.000000000&#x27;, &#x27;2017-08-17T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-22T00:00:00.000000000&#x27;, &#x27;2017-08-27T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-01T00:00:00.000000000&#x27;, &#x27;2017-09-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-11T00:00:00.000000000&#x27;, &#x27;2017-09-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-21T00:00:00.000000000&#x27;, &#x27;2017-09-26T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-10-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-11T00:00:00.000000000&#x27;, &#x27;2017-10-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-21T00:00:00.000000000&#x27;, &#x27;2017-10-26T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-31T00:00:00.000000000&#x27;, &#x27;2017-11-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-10T00:00:00.000000000&#x27;, &#x27;2017-11-15T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-20T00:00:00.000000000&#x27;, &#x27;2017-11-25T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-30T00:00:00.000000000&#x27;, &#x27;2017-12-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-12-10T00:00:00.000000000&#x27;, &#x27;2017-12-15T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-12-20T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b9efcba-878c-40a5-8311-a14e0ca0671b' class='xr-section-summary-in' type='checkbox'  ><label for='section-3b9efcba-878c-40a5-8311-a14e0ca0671b' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b25e6f9e-987f-4dae-b4bc-a5384070cc39' class='xr-index-data-in' type='checkbox'/><label for='index-b25e6f9e-987f-4dae-b4bc-a5384070cc39' 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;2017-01-14&#x27;, &#x27;2017-01-19&#x27;, &#x27;2017-01-24&#x27;, &#x27;2017-01-29&#x27;,\n",
              "               &#x27;2017-02-03&#x27;, &#x27;2017-02-08&#x27;, &#x27;2017-02-13&#x27;, &#x27;2017-02-18&#x27;,\n",
              "               &#x27;2017-02-23&#x27;, &#x27;2017-02-28&#x27;, &#x27;2017-03-05&#x27;, &#x27;2017-03-10&#x27;,\n",
              "               &#x27;2017-03-15&#x27;, &#x27;2017-03-20&#x27;, &#x27;2017-03-25&#x27;, &#x27;2017-03-30&#x27;,\n",
              "               &#x27;2017-04-04&#x27;, &#x27;2017-04-09&#x27;, &#x27;2017-04-14&#x27;, &#x27;2017-04-19&#x27;,\n",
              "               &#x27;2017-04-24&#x27;, &#x27;2017-04-29&#x27;, &#x27;2017-05-04&#x27;, &#x27;2017-05-09&#x27;,\n",
              "               &#x27;2017-05-14&#x27;, &#x27;2017-05-19&#x27;, &#x27;2017-05-24&#x27;, &#x27;2017-05-29&#x27;,\n",
              "               &#x27;2017-06-03&#x27;, &#x27;2017-06-08&#x27;, &#x27;2017-06-13&#x27;, &#x27;2017-06-18&#x27;,\n",
              "               &#x27;2017-06-23&#x27;, &#x27;2017-06-28&#x27;, &#x27;2017-07-03&#x27;, &#x27;2017-07-08&#x27;,\n",
              "               &#x27;2017-07-13&#x27;, &#x27;2017-07-18&#x27;, &#x27;2017-07-23&#x27;, &#x27;2017-07-28&#x27;,\n",
              "               &#x27;2017-08-02&#x27;, &#x27;2017-08-07&#x27;, &#x27;2017-08-12&#x27;, &#x27;2017-08-17&#x27;,\n",
              "               &#x27;2017-08-22&#x27;, &#x27;2017-08-27&#x27;, &#x27;2017-09-01&#x27;, &#x27;2017-09-06&#x27;,\n",
              "               &#x27;2017-09-11&#x27;, &#x27;2017-09-16&#x27;, &#x27;2017-09-21&#x27;, &#x27;2017-09-26&#x27;,\n",
              "               &#x27;2017-10-01&#x27;, &#x27;2017-10-06&#x27;, &#x27;2017-10-11&#x27;, &#x27;2017-10-16&#x27;,\n",
              "               &#x27;2017-10-21&#x27;, &#x27;2017-10-26&#x27;, &#x27;2017-10-31&#x27;, &#x27;2017-11-05&#x27;,\n",
              "               &#x27;2017-11-10&#x27;, &#x27;2017-11-15&#x27;, &#x27;2017-11-20&#x27;, &#x27;2017-11-25&#x27;,\n",
              "               &#x27;2017-11-30&#x27;, &#x27;2017-12-05&#x27;, &#x27;2017-12-10&#x27;, &#x27;2017-12-15&#x27;,\n",
              "               &#x27;2017-12-20&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;5D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b481126-597d-49de-a364-511fc59929f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6b481126-597d-49de-a364-511fc59929f6' 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>id :</span></dt><dd>ndvi</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;max&#x27;: 1}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#A moving-window smoothing removed the first and last values of the time-series.\n",
        "#We anchor the smoothed time-series with the values from the original time-series.\n",
        "time_series_smooth[0] = original_time_series[0]\n",
        "time_series_smooth[-1] = original_time_series[-1]\n",
        "time_series_smooth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Fj-OPPrP92pC",
        "outputId": "c620ed3a-7aee-4228-ee36-d1441afc4c82"
      },
      "execution_count": 43,
      "outputs": [
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              "Coordinates:\n",
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              "    Y        float64 3.144e+06\n",
              "  * time     (time) datetime64[ns] 2017-01-14 2017-01-19 ... 2017-12-20\n",
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              "\n",
              ":root {\n",
              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
              "  --xr-background-color: var(--jp-layout-color0, white);\n",
              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
              "}\n",
              "\n",
              "html[theme=dark],\n",
              "body[data-theme=dark],\n",
              "body.vscode-dark {\n",
              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
              "  --xr-border-color: #1F1F1F;\n",
              "  --xr-disabled-color: #515151;\n",
              "  --xr-background-color: #111111;\n",
              "  --xr-background-color-row-even: #111111;\n",
              "  --xr-background-color-row-odd: #313131;\n",
              "}\n",
              "\n",
              ".xr-wrap {\n",
              "  display: block !important;\n",
              "  min-width: 300px;\n",
              "  max-width: 700px;\n",
              "}\n",
              "\n",
              ".xr-text-repr-fallback {\n",
              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-header {\n",
              "  padding-top: 6px;\n",
              "  padding-bottom: 6px;\n",
              "  margin-bottom: 4px;\n",
              "  border-bottom: solid 1px var(--xr-border-color);\n",
              "}\n",
              "\n",
              ".xr-header > div,\n",
              ".xr-header > ul {\n",
              "  display: inline;\n",
              "  margin-top: 0;\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-obj-type,\n",
              ".xr-array-name {\n",
              "  margin-left: 2px;\n",
              "  margin-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-obj-type {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-sections {\n",
              "  padding-left: 0 !important;\n",
              "  display: grid;\n",
              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
              "}\n",
              "\n",
              ".xr-section-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-section-item input {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-item input + label {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label {\n",
              "  cursor: pointer;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label:hover {\n",
              "  color: var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-summary {\n",
              "  grid-column: 1;\n",
              "  color: var(--xr-font-color2);\n",
              "  font-weight: 500;\n",
              "}\n",
              "\n",
              ".xr-section-summary > span {\n",
              "  display: inline-block;\n",
              "  padding-left: 0.5em;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in + label:before {\n",
              "  display: inline-block;\n",
              "  content: '►';\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: '▼';\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: '(';\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: ')';\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: ',';\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ndvi&#x27; (time: 69)&gt;\n",
              "array([0.5033776 , 0.5707586 , 0.60336864, 0.60120785, 0.591371  ,\n",
              "       0.6086291 , 0.6529821 , 0.6973351 , 0.70216787, 0.66748047,\n",
              "       0.5932729 , 0.5190653 , 0.44485772, 0.37065014, 0.29644257,\n",
              "       0.22223496, 0.17636673, 0.1521889 , 0.14970146, 0.14056507,\n",
              "       0.12995878, 0.11788259, 0.10433652, 0.09079044, 0.08846613,\n",
              "       0.08879959, 0.09179082, 0.08621804, 0.08011904, 0.07349381,\n",
              "       0.06634237, 0.05919092, 0.0590858 , 0.066027  , 0.08001452,\n",
              "       0.09400204, 0.1212376 , 0.1617212 , 0.21545285, 0.26918453,\n",
              "       0.32291615, 0.3766478 , 0.43037945, 0.48411107, 0.53784275,\n",
              "       0.5915744 , 0.645306  , 0.644693  , 0.6171862 , 0.5627857 ,\n",
              "       0.5358361 , 0.50888646, 0.48193678, 0.45498714, 0.4280375 ,\n",
              "       0.37939638, 0.30906373, 0.24335134, 0.20395069, 0.17143099,\n",
              "       0.1428269 , 0.11816832, 0.11688603, 0.110767  , 0.13424605,\n",
              "       0.18732321, 0.27486512, 0.31210697, 0.29904875], dtype=float32)\n",
              "Coordinates:\n",
              "    X        float64 9.196e+06\n",
              "    Y        float64 3.144e+06\n",
              "  * time     (time) datetime64[ns] 2017-01-14 2017-01-19 ... 2017-12-20\n",
              "Attributes:\n",
              "    id:             ndvi\n",
              "    data_type:      {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;m...\n",
              "    dimensions:     [10980, 10980]\n",
              "    crs:            EPSG:3857\n",
              "    crs_transform:  [10, 0, 600000, 0, -10, 3100020]</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'>'ndvi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 69</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-49cfb24b-c48b-44e9-ae34-e2ad012184a0' class='xr-array-in' type='checkbox' checked><label for='section-49cfb24b-c48b-44e9-ae34-e2ad012184a0' 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>0.5034 0.5708 0.6034 0.6012 0.5914 ... 0.1873 0.2749 0.3121 0.299</span></div><div class='xr-array-data'><pre>array([0.5033776 , 0.5707586 , 0.60336864, 0.60120785, 0.591371  ,\n",
              "       0.6086291 , 0.6529821 , 0.6973351 , 0.70216787, 0.66748047,\n",
              "       0.5932729 , 0.5190653 , 0.44485772, 0.37065014, 0.29644257,\n",
              "       0.22223496, 0.17636673, 0.1521889 , 0.14970146, 0.14056507,\n",
              "       0.12995878, 0.11788259, 0.10433652, 0.09079044, 0.08846613,\n",
              "       0.08879959, 0.09179082, 0.08621804, 0.08011904, 0.07349381,\n",
              "       0.06634237, 0.05919092, 0.0590858 , 0.066027  , 0.08001452,\n",
              "       0.09400204, 0.1212376 , 0.1617212 , 0.21545285, 0.26918453,\n",
              "       0.32291615, 0.3766478 , 0.43037945, 0.48411107, 0.53784275,\n",
              "       0.5915744 , 0.645306  , 0.644693  , 0.6171862 , 0.5627857 ,\n",
              "       0.5358361 , 0.50888646, 0.48193678, 0.45498714, 0.4280375 ,\n",
              "       0.37939638, 0.30906373, 0.24335134, 0.20395069, 0.17143099,\n",
              "       0.1428269 , 0.11816832, 0.11688603, 0.110767  , 0.13424605,\n",
              "       0.18732321, 0.27486512, 0.31210697, 0.29904875], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-d203f7bf-ca97-4e14-a50a-651b478f333f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d203f7bf-ca97-4e14-a50a-651b478f333f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>X</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.196e+06</div><input id='attrs-cbd1a750-46af-4494-8e66-35b5eb457ac3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbd1a750-46af-4494-8e66-35b5eb457ac3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b37a36fc-ab83-4737-84f3-db0057248595' class='xr-var-data-in' type='checkbox'><label for='data-b37a36fc-ab83-4737-84f3-db0057248595' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9195840.51382513)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.144e+06</div><input id='attrs-edc81831-8dbf-40bd-9307-16b3ffe717d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edc81831-8dbf-40bd-9307-16b3ffe717d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a57c9376-c21a-453f-af8f-8c6569e25b10' class='xr-var-data-in' type='checkbox'><label for='data-a57c9376-c21a-453f-af8f-8c6569e25b10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3143916.52068016)</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'>2017-01-14 ... 2017-12-20</div><input id='attrs-d3f02ed1-5baa-4eab-ac10-8695b058cba0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3f02ed1-5baa-4eab-ac10-8695b058cba0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0331b652-66a0-466a-8a90-645bec5b5eec' class='xr-var-data-in' type='checkbox'><label for='data-0331b652-66a0-466a-8a90-645bec5b5eec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-14T00:00:00.000000000&#x27;, &#x27;2017-01-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-01-24T00:00:00.000000000&#x27;, &#x27;2017-01-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-03T00:00:00.000000000&#x27;, &#x27;2017-02-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-13T00:00:00.000000000&#x27;, &#x27;2017-02-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-02-23T00:00:00.000000000&#x27;, &#x27;2017-02-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-05T00:00:00.000000000&#x27;, &#x27;2017-03-10T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-15T00:00:00.000000000&#x27;, &#x27;2017-03-20T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-03-25T00:00:00.000000000&#x27;, &#x27;2017-03-30T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-04T00:00:00.000000000&#x27;, &#x27;2017-04-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-14T00:00:00.000000000&#x27;, &#x27;2017-04-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-04-24T00:00:00.000000000&#x27;, &#x27;2017-04-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-04T00:00:00.000000000&#x27;, &#x27;2017-05-09T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-14T00:00:00.000000000&#x27;, &#x27;2017-05-19T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-05-24T00:00:00.000000000&#x27;, &#x27;2017-05-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-03T00:00:00.000000000&#x27;, &#x27;2017-06-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-13T00:00:00.000000000&#x27;, &#x27;2017-06-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-06-23T00:00:00.000000000&#x27;, &#x27;2017-06-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-03T00:00:00.000000000&#x27;, &#x27;2017-07-08T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-13T00:00:00.000000000&#x27;, &#x27;2017-07-18T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-07-23T00:00:00.000000000&#x27;, &#x27;2017-07-28T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-02T00:00:00.000000000&#x27;, &#x27;2017-08-07T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-12T00:00:00.000000000&#x27;, &#x27;2017-08-17T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-08-22T00:00:00.000000000&#x27;, &#x27;2017-08-27T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-01T00:00:00.000000000&#x27;, &#x27;2017-09-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-11T00:00:00.000000000&#x27;, &#x27;2017-09-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-09-21T00:00:00.000000000&#x27;, &#x27;2017-09-26T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-10-06T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-11T00:00:00.000000000&#x27;, &#x27;2017-10-16T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-21T00:00:00.000000000&#x27;, &#x27;2017-10-26T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-10-31T00:00:00.000000000&#x27;, &#x27;2017-11-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-10T00:00:00.000000000&#x27;, &#x27;2017-11-15T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-20T00:00:00.000000000&#x27;, &#x27;2017-11-25T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-11-30T00:00:00.000000000&#x27;, &#x27;2017-12-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-12-10T00:00:00.000000000&#x27;, &#x27;2017-12-15T00:00:00.000000000&#x27;,\n",
              "       &#x27;2017-12-20T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ec43a11-ad19-4830-b204-78e036a765cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-3ec43a11-ad19-4830-b204-78e036a765cd' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6720971-b253-4f25-a15f-d538e1a8da38' class='xr-index-data-in' type='checkbox'/><label for='index-e6720971-b253-4f25-a15f-d538e1a8da38' 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;2017-01-14&#x27;, &#x27;2017-01-19&#x27;, &#x27;2017-01-24&#x27;, &#x27;2017-01-29&#x27;,\n",
              "               &#x27;2017-02-03&#x27;, &#x27;2017-02-08&#x27;, &#x27;2017-02-13&#x27;, &#x27;2017-02-18&#x27;,\n",
              "               &#x27;2017-02-23&#x27;, &#x27;2017-02-28&#x27;, &#x27;2017-03-05&#x27;, &#x27;2017-03-10&#x27;,\n",
              "               &#x27;2017-03-15&#x27;, &#x27;2017-03-20&#x27;, &#x27;2017-03-25&#x27;, &#x27;2017-03-30&#x27;,\n",
              "               &#x27;2017-04-04&#x27;, &#x27;2017-04-09&#x27;, &#x27;2017-04-14&#x27;, &#x27;2017-04-19&#x27;,\n",
              "               &#x27;2017-04-24&#x27;, &#x27;2017-04-29&#x27;, &#x27;2017-05-04&#x27;, &#x27;2017-05-09&#x27;,\n",
              "               &#x27;2017-05-14&#x27;, &#x27;2017-05-19&#x27;, &#x27;2017-05-24&#x27;, &#x27;2017-05-29&#x27;,\n",
              "               &#x27;2017-06-03&#x27;, &#x27;2017-06-08&#x27;, &#x27;2017-06-13&#x27;, &#x27;2017-06-18&#x27;,\n",
              "               &#x27;2017-06-23&#x27;, &#x27;2017-06-28&#x27;, &#x27;2017-07-03&#x27;, &#x27;2017-07-08&#x27;,\n",
              "               &#x27;2017-07-13&#x27;, &#x27;2017-07-18&#x27;, &#x27;2017-07-23&#x27;, &#x27;2017-07-28&#x27;,\n",
              "               &#x27;2017-08-02&#x27;, &#x27;2017-08-07&#x27;, &#x27;2017-08-12&#x27;, &#x27;2017-08-17&#x27;,\n",
              "               &#x27;2017-08-22&#x27;, &#x27;2017-08-27&#x27;, &#x27;2017-09-01&#x27;, &#x27;2017-09-06&#x27;,\n",
              "               &#x27;2017-09-11&#x27;, &#x27;2017-09-16&#x27;, &#x27;2017-09-21&#x27;, &#x27;2017-09-26&#x27;,\n",
              "               &#x27;2017-10-01&#x27;, &#x27;2017-10-06&#x27;, &#x27;2017-10-11&#x27;, &#x27;2017-10-16&#x27;,\n",
              "               &#x27;2017-10-21&#x27;, &#x27;2017-10-26&#x27;, &#x27;2017-10-31&#x27;, &#x27;2017-11-05&#x27;,\n",
              "               &#x27;2017-11-10&#x27;, &#x27;2017-11-15&#x27;, &#x27;2017-11-20&#x27;, &#x27;2017-11-25&#x27;,\n",
              "               &#x27;2017-11-30&#x27;, &#x27;2017-12-05&#x27;, &#x27;2017-12-10&#x27;, &#x27;2017-12-15&#x27;,\n",
              "               &#x27;2017-12-20&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;5D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc0fdf6d-398a-4e1d-a583-0995ac9970fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc0fdf6d-398a-4e1d-a583-0995ac9970fa' 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>id :</span></dt><dd>ndvi</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;float&#x27;, &#x27;min&#x27;: -1, &#x27;max&#x27;: 1}</dd><dt><span>dimensions :</span></dt><dd>[10980, 10980]</dd><dt><span>crs :</span></dt><dd>EPSG:3857</dd><dt><span>crs_transform :</span></dt><dd>[10, 0, 600000, 0, -10, 3100020]</dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the original and smoothed time-series.\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(10, 5)\n",
        "original_time_series.plot.line(\n",
        "    ax=ax, x='time',\n",
        "    marker='^', color='#66c2a4', linestyle='--', linewidth=1, markersize=2)\n",
        "time_series_smooth.plot.line(\n",
        "    ax=ax, x='time',\n",
        "    marker='o', color='#238b45', linestyle='-', linewidth=1, markersize=4)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "txdCNDZv97wj",
        "outputId": "a9b3a3b4-f212-4771-ac8f-8530bb3e01e8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert the DataArray to a Pandas DataFrame and save it as a CSV file.\n",
        "df = time_series_smooth.to_dataframe('ndvi').reset_index()\n",
        "df\n",
        "output_filename = 'smoothed_time_series.csv'\n",
        "df[['time', 'ndvi']].to_csv(output_filename, index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "4G3o5ndl-BAt",
        "outputId": "b8c0c29a-3fc5-44d7-d9c8-5a42c2ccff7d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}