Sentinel-2-data-preparation.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20338,"status":"ok","timestamp":1725381323057,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"QwU64-mqfreI","outputId":"9fe02e99-9378-44df-a361-aa9442d74b39"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rQaFXWaRgOHM"},"outputs":[],"source":["#required packages.\n","import numpy as np\n","import pandas as pd\n","from pandas import Timestamp\n","import matplotlib\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt\n","import matplotlib.colors as colors\n","import os\n","import datetime\n","import itertools\n","import argparse\n","import collections"]},{"cell_type":"markdown","source":["Prior to using the Earth Engine Python client library, you need to authenticate (verify your identity) and use the resultant credentials to initialize the Python client. The authentication flows use Cloud Projects to authenticate, and they're used for unpaid (free, noncommercial) use as well as paid use. To authenticate and initialize, run the following codes.\n","\n","https://developers.google.com/earth-engine/guides/auth\n"],"metadata":{"id":"tT_qpPgNwryE"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"tKjQM42ihIqr"},"outputs":[],"source":["import ee\n","# # Trigger the authentication flow.\n","ee.Authenticate()\n","# Initialize the library.\n","ee.Initialize(project='')"]},{"cell_type":"markdown","source":["## utils"],"metadata":{"id":"NyXGWPQxIJBd"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"O-OPGpqKeNPb"},"outputs":[],"source":["#must run these codes\n","def ee_array_to_df(arr, list_of_bands):\n","    \"\"\"Transforms client-side ee.Image.getRegion array to pandas.DataFrame.\"\"\"\n","    df = pd.DataFrame(arr)\n","\n","    # Rearrange the header.\n","    headers = df.iloc[0]\n","    df = pd.DataFrame(df.values[1:], columns=headers)\n","\n","    # Remove rows without data inside.\n","    df = df[['longitude', 'latitude', 'time', *list_of_bands]].dropna()\n","\n","    # Convert the data to numeric values.\n","    for band in list_of_bands:\n","        df[band] = pd.to_numeric(df[band], errors='coerce')\n","\n","    # Convert the time field into a datetime.\n","    df['datetime'] = pd.to_datetime(df['time'], unit='ms')\n","\n","    # Keep the columns of interest.\n","    df = df[['time','datetime',  *list_of_bands]]\n","\n","    return df\n","\n","def ee_array_to_df1(arr, list_of_bands):\n","    \"\"\"Transforms client-side ee.Image.getRegion array to pandas.DataFrame.\"\"\"\n","    df = pd.DataFrame(arr)\n","\n","    # Rearrange the header.\n","    headers = df.iloc[0]\n","    df = pd.DataFrame(df.values[1:], columns=headers)\n","\n","    # Convert the data to numeric values.\n","    for band in list_of_bands:\n","        df[band] = pd.to_numeric(df[band], errors=\"coerce\")\n","\n","    # Convert the time field into a datetime.\n","    df[\"datetime\"] = pd.to_datetime(df[\"time\"], unit=\"ms\")\n","\n","    # Keep the columns of interest.\n","    df = df[[\"time\", \"datetime\", *list_of_bands]]\n","\n","    # The datetime column is defined as index.\n","    df = df.set_index(\"datetime\")\n","\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"uifxTzJDOnJD"},"source":["## Sentinel-2 image downloading"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":33305,"status":"ok","timestamp":1683886533484,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"cKRukBDxXMA1","outputId":"d460e628-b186-4b36-d313-d7787185f2af"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting geopandas\n","  Downloading geopandas-0.13.0-py3-none-any.whl (1.1 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 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sha256=8beef73e0c440d8caaddba0429c88e8934608f462f8cb6f16ff1c722b0b02a19\n","  Stored in directory: /root/.cache/pip/wheels/2f/a1/c8/543df0e8f5e824c3e92a432e32deb9cd89ae686095ee8cfcbe\n","Successfully built utm\n","Installing collected packages: utm, aenum, tomli-w, pillow, mypy-extensions, marshmallow, typing-inspect, marshmallow-enum, dataclasses-json, sentinelhub\n","  Attempting uninstall: pillow\n","    Found existing installation: Pillow 8.4.0\n","    Uninstalling Pillow-8.4.0:\n","      Successfully uninstalled Pillow-8.4.0\n","Successfully installed aenum-3.1.12 dataclasses-json-0.5.7 marshmallow-3.19.0 marshmallow-enum-1.5.1 mypy-extensions-1.0.0 pillow-9.5.0 sentinelhub-3.9.1 tomli-w-1.0.0 typing-inspect-0.8.0 utm-0.7.0\n"]},{"data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["PIL"]}}},"metadata":{},"output_type":"display_data"}],"source":["!pip install geopandas\n","!pip install rasterio"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rHM6rCpEZKoq"},"outputs":[],"source":["import geopandas\n","import rasterio\n","import rasterio.mask"]},{"cell_type":"markdown","metadata":{"id":"03dbWCFAGJM_"},"source":["### utils"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wJyVbIKrOms2"},"outputs":[],"source":["def get_s2_sr_cld_col(aoi, start_date, end_date):\n","    # Import and filter S2 SR.\n","    s2_sr_col = (ee.ImageCollection('COPERNICUS/S2_SR')\n","        .filterBounds(aoi)\n","        .filterDate(start_date, end_date)\n","        .filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', CLOUD_FILTER)))\n","\n","    # Import and filter s2cloudless.\n","    s2_cloudless_col = (ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')\n","        .filterBounds(aoi)\n","        .filterDate(start_date, end_date))\n","\n","    # Join the filtered s2cloudless collection to the SR collection by the 'system:index' property.\n","    return ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply(**{\n","        'primary': s2_sr_col,\n","        'secondary': s2_cloudless_col,\n","        'condition': ee.Filter.equals(**{\n","            'leftField': 'system:index',\n","            'rightField': 'system:index'\n","        })\n","    }))\n","\n","\n","def add_cloud_bands(img):\n","    # Get s2cloudless image, subset the probability band.\n","    cld_prb = ee.Image(img.get('s2cloudless')).select('probability')\n","\n","    # Condition s2cloudless by the probability threshold value.\n","    is_cloud = cld_prb.gt(CLD_PRB_THRESH).rename('clouds')\n","\n","    # Add the cloud probability layer and cloud mask as image bands.\n","    return img.addBands(ee.Image([cld_prb, is_cloud]))\n","\n","def add_shadow_bands(img):\n","    # Identify water pixels from the SCL band.\n","    not_water = img.select('SCL').neq(6)\n","\n","    # Identify dark NIR pixels that are not water (potential cloud shadow pixels).\n","    SR_BAND_SCALE = 1e4\n","    dark_pixels = img.select('B8').lt(NIR_DRK_THRESH*SR_BAND_SCALE).multiply(not_water).rename('dark_pixels')\n","\n","    # Determine the direction to project cloud shadow from clouds (assumes UTM projection).\n","    shadow_azimuth = ee.Number(90).subtract(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE')));\n","\n","    # Project shadows from clouds for the distance specified by the CLD_PRJ_DIST input.\n","    cld_proj = (img.select('clouds').directionalDistanceTransform(shadow_azimuth, CLD_PRJ_DIST*10)\n","        .reproject(**{'crs': img.select(0).projection(), 'scale': 100})\n","        .select('distance')\n","        .mask()\n","        .rename('cloud_transform'))\n","\n","    # Identify the intersection of dark pixels with cloud shadow projection.\n","    shadows = cld_proj.multiply(dark_pixels).rename('shadows')\n","\n","    # Add dark pixels, cloud projection, and identified shadows as image bands.\n","    return img.addBands(ee.Image([dark_pixels, cld_proj, shadows]))\n","\n","def add_cld_shdw_mask(img):\n","    # Add cloud component bands.\n","    img_cloud = add_cloud_bands(img)\n","\n","    # Add cloud shadow component bands.\n","    img_cloud_shadow = add_shadow_bands(img_cloud)\n","\n","    # Combine cloud and shadow mask, set cloud and shadow as value 1, else 0.\n","    is_cld_shdw = img_cloud_shadow.select('clouds').add(img_cloud_shadow.select('shadows')).gt(0)\n","\n","    # Remove small cloud-shadow patches and dilate remaining pixels by BUFFER input.\n","    # 20 m scale is for speed, and assumes clouds don't require 10 m precision.\n","    is_cld_shdw = (is_cld_shdw.focalMin(2).focalMax(BUFFER*2/20)\n","        .reproject(**{'crs': img.select([0]).projection(), 'scale': 20})\n","        .rename('cloudmask'))\n","\n","    # Add the final cloud-shadow mask to the image.\n","    return img_cloud_shadow.addBands(is_cld_shdw)\n","\n","# Define a function to apply the cloud mask to each image in the collection.\n","def apply_cld_shdw_mask(img):\n","    # Subset the cloudmask band and invert it so clouds/shadow are 0, else 1.\n","    not_cld_shdw = img.select('cloudmask').Not()\n","\n","    # Subset reflectance bands and update their masks, return the result.\n","    return img.select('B.*').updateMask(not_cld_shdw)\n","\n","def calculate_bounds_from_geojson(geojson):\n","    \"\"\"\n","    this function calculates the top left and bottom right coordinates of a box around a geojson used to download imagery\n","\n","    Parameters\n","    ----------\n","    geojson : dict\n","        the geojson that describes the spatial information of the farm / area in question.\n","\n","    Returns\n","    -------\n","    bounds : list\n","        list of tuples that corresponds to lat and long of top left and bottom right coordinates.\n","\n","    \"\"\"\n","    geojson_as_dataframe = geopandas.GeoDataFrame.from_features(geojson)\n","    bounds = [(geojson_as_dataframe.total_bounds[0], geojson_as_dataframe.total_bounds[1]), (geojson_as_dataframe.total_bounds[2], geojson_as_dataframe.total_bounds[3])]\n","\n","    return bounds\n","\n","def convert_bands_2_numpy(s2_sr_median, aoi_vineyard):\n","    #using the median value of multi bands\n","    s2_sr_mediana = s2_sr_median.reproject(crs = ee.Projection('EPSG:4326'), scale=10)\n","\n","    #collect all the available bands over the aoi\n","    selected_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']\n","    l8sr_bands = s2_sr_mediana.select(selected_bands).sampleRectangle(aoi_vineyard)\n","\n","    band_arr = l8sr_bands.get('B1')\n","    s2_arr = np.array(band_arr.getInfo())\n","\n","    #stack all bands together\n","    for idx, band in enumerate(selected_bands):\n","        if band == 'B1': continue\n","        band_arr = l8sr_bands.get(band)\n","        band_arr = np.array(band_arr.getInfo())\n","        # Stack the individual bands to make a 3-D array.\n","        s2_arr = np.dstack((s2_arr, band_arr))\n","\n","    #apply the scale of the image\n","    s2_array = (s2_arr * 0.0001).astype(np.float32)\n","\n","    return s2_array\n","\n","def plot_rgb_image(s2_arr):\n","    #apply the scale of the image\n","    s2_array = (s2_arr / 0.0001).astype(np.float32)\n","\n","    #show RGB image: B4, B3, B2\n","    rgb_img = s2_array[:,:,1:4]\n","    rgb_img = rgb_img[:,:,::-1]\n","\n","    # Scale the data to [0, 255] to show as an RGB image.\n","    rgb_img_test = (255*((rgb_img - 100)/3500)).astype('uint8')\n","    plt.imshow(rgb_img_test)\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9Aqpt82eY604"},"outputs":[],"source":["#setting filtering parameters\n","CLOUD_FILTER = 60 #integer, Maximum image cloud cover percent allowed in image collection\n","CLD_PRB_THRESH = 20 #integer, Cloud probability (%); values greater than are considered cloud\n","NIR_DRK_THRESH = 0.15 #float, Near-infrared reflectance; values less than are considered potential cloud shadow\n","CLD_PRJ_DIST = 2 #float, Maximum distance (km) to search for cloud shadows from cloud edges\n","BUFFER = 90 #integer, Distance (m) to dilate the edge of cloud-identified objects"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1vEwliYiXD3I"},"outputs":[],"source":["def get_s2_features_main(geoJSON, vineyard_name, START_DATE, END_DATE):\n","\n","    #compute the center location of the AOI\n","    bounds = calculate_bounds_from_geojson(geoJSON)\n","    #aoi of vineyard\n","    aoi_vineyard = ee.Geometry.Polygon([[[bounds[0][0], bounds[0][1]],\n","                                        [bounds[0][0], bounds[1][1]],\n","                                        [bounds[1][0], bounds[1][1]],\n","                                        [bounds[1][0], bounds[0][1]]]])\n","\n","    longitude, latitude = (bounds[0][0] + bounds[1][0])/2, (bounds[0][1] + bounds[1][1])/2\n","    AOI = ee.Geometry.Point(longitude, latitude)\n","\n","    # Reassemble the S2-cloudless collection since the collection filter parameters have changed.\n","    s2_sr_cld_col = get_s2_sr_cld_col(AOI, START_DATE, END_DATE)\n","    # Reduce the collection by median\n","    s2_sr_median = (s2_sr_cld_col.map(add_cld_shdw_mask)\n","                                .map(apply_cld_shdw_mask)\n","                                .median())\n","\n","    #creating downloading file name for single image\n","    file_name = s2_sr_cld_col.getInfo()['features'][-1]['properties']['DATASTRIP_ID']\n","    #convert image format\n","    s2_array = convert_bands_2_numpy(s2_sr_median, aoi_vineyard)\n","\n","    #compute vegetation indices for each block\n","    data1 = generate_block_wise_vegetation_indices(s2_array, bounds, geoJSON, vineyard_name, START_DATE, END_DATE)\n","\n","    return data1, file_name"]},{"cell_type":"markdown","metadata":{"id":"rAd3dVCwitRi"},"source":["### s2 noisy time series"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NAR30Lq7iszt"},"outputs":[],"source":["#test parameters\n","i_date, f_date = '2018-01-01', '2018-12-31'\n","# r_lon, r_lat = 142.4377389809555, -30.95203025969217\n","# r_lon, r_lat = 1.0757467083137495, 48.02695618370076\n","# r_lon, r_lat = 15.291507673637822, 61.554831262246616\n","# r_lon, r_lat = 11.424320173637822, 44.07288922099584\n","r_lon, r_lat = -2.022945451362177, 53.160855383911525\n","scale = 10\n","selected_features = ['B1','B2','B3','B4','B5','B6','B7','B8','B8A','B9','B11','B12']\n","poi = ee.Geometry.Point(r_lon, r_lat)\n","CloudCoverMax = 20"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1811,"status":"ok","timestamp":1683897194084,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"iKSbPUj7jJYW","outputId":"596ce686-edd7-42bf-980d-e19ca974bcce"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-c9c379a8-fa36-47d8-ba6e-5cf436b2f50d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>B2</th>\n","      <th>B3</th>\n","      <th>B4</th>\n","      <th>B5</th>\n","      <th>B6</th>\n","      <th>datetime</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0356</td>\n","      <td>0.0335</td>\n","      <td>0.0323</td>\n","      <td>0.0648</td>\n","      <td>0.1220</td>\n","      <td>2018-01-07 11:14:31.457</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0357</td>\n","      <td>0.0338</td>\n","      <td>0.0322</td>\n","      <td>0.0641</td>\n","      <td>0.1205</td>\n","      <td>2018-01-07 11:14:31.457</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.0130</td>\n","      <td>0.0141</td>\n","      <td>0.0167</td>\n","      <td>0.0669</td>\n","      <td>0.1344</td>\n","      <td>2018-02-09 11:22:17.460</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0190</td>\n","      <td>0.0377</td>\n","      <td>0.0344</td>\n","      <td>0.0739</td>\n","      <td>0.1266</td>\n","      <td>2018-02-24 11:22:28.439</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0067</td>\n","      <td>0.0266</td>\n","      <td>0.0248</td>\n","      <td>0.0657</td>\n","      <td>0.1188</td>\n","      <td>2018-02-24 11:22:28.439</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.0230</td>\n","      <td>0.0266</td>\n","      <td>0.0320</td>\n","      <td>0.0749</td>\n","      <td>0.1218</td>\n","      <td>2018-04-05 11:21:10.000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.1897</td>\n","      <td>0.2031</td>\n","      <td>0.1931</td>\n","      <td>0.2656</td>\n","      <td>0.3928</td>\n","      <td>2018-04-20 11:26:11.000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.0330</td>\n","      <td>0.0509</td>\n","      <td>0.0415</td>\n","      <td>0.1036</td>\n","      <td>0.1863</td>\n","      <td>2018-05-05 11:23:04.000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.0322</td>\n","      <td>0.0504</td>\n","      <td>0.0413</td>\n","      <td>0.1032</td>\n","      <td>0.1862</td>\n","      <td>2018-05-05 11:23:04.000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.0352</td>\n","      <td>0.0581</td>\n","      <td>0.0564</td>\n","      <td>0.1130</td>\n","      <td>0.2299</td>\n","      <td>2018-05-07 11:08:35.000</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.0367</td>\n","      <td>0.0591</td>\n","      <td>0.0568</td>\n","      <td>0.1129</td>\n","      <td>0.2303</td>\n","      <td>2018-05-07 11:08:35.000</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.0280</td>\n","      <td>0.0521</td>\n","      <td>0.0432</td>\n","      <td>0.1101</td>\n","      <td>0.2133</td>\n","      <td>2018-05-15 11:21:16.069</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.0257</td>\n","      <td>0.0509</td>\n","      <td>0.0428</td>\n","      <td>0.1099</td>\n","      <td>0.2118</td>\n","      <td>2018-05-15 11:21:16.069</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.0310</td>\n","      <td>0.0558</td>\n","      <td>0.0514</td>\n","      <td>0.1266</td>\n","      <td>0.2205</td>\n","      <td>2018-05-22 11:11:19.000</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.0327</td>\n","      <td>0.0572</td>\n","      <td>0.0521</td>\n","      <td>0.1268</td>\n","      <td>0.2214</td>\n","      <td>2018-05-22 11:11:19.000</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0.0504</td>\n","      <td>0.0780</td>\n","      <td>0.0774</td>\n","      <td>0.1370</td>\n","      <td>0.2710</td>\n","      <td>2018-05-27 11:11:41.082</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.0366</td>\n","      <td>0.0557</td>\n","      <td>0.0444</td>\n","      <td>0.1110</td>\n","      <td>0.2340</td>\n","      <td>2018-06-24 11:24:52.538</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.0369</td>\n","      <td>0.0655</td>\n","      <td>0.0564</td>\n","      <td>0.1121</td>\n","      <td>0.2667</td>\n","      <td>2018-06-26 11:14:13.074</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>0.0314</td>\n","      <td>0.0641</td>\n","      <td>0.0569</td>\n","      <td>0.1126</td>\n","      <td>0.2658</td>\n","      <td>2018-06-26 11:14:13.074</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.0304</td>\n","      <td>0.0473</td>\n","      <td>0.0439</td>\n","      <td>0.1086</td>\n","      <td>0.2396</td>\n","      <td>2018-06-29 11:25:37.824</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>0.0288</td>\n","      <td>0.0484</td>\n","      <td>0.0460</td>\n","      <td>0.1092</td>\n","      <td>0.2383</td>\n","      <td>2018-06-29 11:25:37.824</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>0.0233</td>\n","      <td>0.0404</td>\n","      <td>0.0329</td>\n","      <td>0.1059</td>\n","      <td>0.2304</td>\n","      <td>2018-07-04 11:21:12.459</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>0.0341</td>\n","      <td>0.0490</td>\n","      <td>0.0401</td>\n","      <td>0.1085</td>\n","      <td>0.2285</td>\n","      <td>2018-07-04 11:21:12.459</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>0.0317</td>\n","      <td>0.0574</td>\n","      <td>0.0540</td>\n","      <td>0.1208</td>\n","      <td>0.2638</td>\n","      <td>2018-07-06 11:09:18.241</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>0.0374</td>\n","      <td>0.0608</td>\n","      <td>0.0570</td>\n","      <td>0.1221</td>\n","      <td>0.2631</td>\n","      <td>2018-07-06 11:09:18.241</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>0.2126</td>\n","      <td>0.2504</td>\n","      <td>0.2652</td>\n","      <td>0.3139</td>\n","      <td>0.4067</td>\n","      <td>2018-07-11 11:11:39.550</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>0.2056</td>\n","      <td>0.2436</td>\n","      <td>0.2600</td>\n","      <td>0.3077</td>\n","      <td>0.3997</td>\n","      <td>2018-07-11 11:11:39.550</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>0.9544</td>\n","      <td>0.8984</td>\n","      <td>0.8824</td>\n","      <td>0.9362</td>\n","      <td>0.9041</td>\n","      <td>2018-07-14 11:21:12.461</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>0.3316</td>\n","      <td>0.3272</td>\n","      <td>0.2678</td>\n","      <td>0.3750</td>\n","      <td>0.5478</td>\n","      <td>2018-09-24 11:14:16.000</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>0.0280</td>\n","      <td>0.0423</td>\n","      <td>0.0289</td>\n","      <td>0.0744</td>\n","      <td>0.2407</td>\n","      <td>2018-09-27 11:23:30.164</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>0.0252</td>\n","      <td>0.0410</td>\n","      <td>0.0283</td>\n","      <td>0.0738</td>\n","      <td>0.2401</td>\n","      <td>2018-09-27 11:23:30.164</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>0.0306</td>\n","      <td>0.0468</td>\n","      <td>0.0383</td>\n","      <td>0.1046</td>\n","      <td>0.2532</td>\n","      <td>2018-09-29 11:10:54.000</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>0.0200</td>\n","      <td>0.0347</td>\n","      <td>0.0221</td>\n","      <td>0.0968</td>\n","      <td>0.2393</td>\n","      <td>2018-10-09 11:09:42.000</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>0.1586</td>\n","      <td>0.1852</td>\n","      <td>0.1560</td>\n","      <td>0.2440</td>\n","      <td>0.4341</td>\n","      <td>2018-10-19 11:10:50.000</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>0.1560</td>\n","      <td>0.1840</td>\n","      <td>0.1548</td>\n","      <td>0.2427</td>\n","      <td>0.4330</td>\n","      <td>2018-10-19 11:10:50.000</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>0.0258</td>\n","      <td>0.0375</td>\n","      <td>0.0259</td>\n","      <td>0.1070</td>\n","      <td>0.2303</td>\n","      <td>2018-10-29 11:11:52.000</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>0.0208</td>\n","      <td>0.0343</td>\n","      <td>0.0238</td>\n","      <td>0.1056</td>\n","      <td>0.2285</td>\n","      <td>2018-10-29 11:11:52.000</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>0.2886</td>\n","      <td>0.2956</td>\n","      <td>0.3006</td>\n","      <td>0.3599</td>\n","      <td>0.4760</td>\n","      <td>2018-11-11 11:26:32.000</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>0.0115</td>\n","      <td>0.0383</td>\n","      <td>0.0321</td>\n","      <td>0.0984</td>\n","      <td>0.1921</td>\n","      <td>2018-11-13 11:16:32.341</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>0.0097</td>\n","      <td>0.0368</td>\n","      <td>0.0312</td>\n","      <td>0.0981</td>\n","      <td>0.1916</td>\n","      <td>2018-11-13 11:16:17.485</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>0.0302</td>\n","      <td>0.0305</td>\n","      <td>0.0255</td>\n","      <td>0.0848</td>\n","      <td>0.2062</td>\n","      <td>2018-11-18 11:16:34.000</td>\n","    </tr>\n","  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0.0657  0.1188 2018-02-24 11:22:28.439\n","5   0.0230  0.0266  0.0320  0.0749  0.1218 2018-04-05 11:21:10.000\n","6   0.1897  0.2031  0.1931  0.2656  0.3928 2018-04-20 11:26:11.000\n","7   0.0330  0.0509  0.0415  0.1036  0.1863 2018-05-05 11:23:04.000\n","8   0.0322  0.0504  0.0413  0.1032  0.1862 2018-05-05 11:23:04.000\n","9   0.0352  0.0581  0.0564  0.1130  0.2299 2018-05-07 11:08:35.000\n","10  0.0367  0.0591  0.0568  0.1129  0.2303 2018-05-07 11:08:35.000\n","11  0.0280  0.0521  0.0432  0.1101  0.2133 2018-05-15 11:21:16.069\n","12  0.0257  0.0509  0.0428  0.1099  0.2118 2018-05-15 11:21:16.069\n","13  0.0310  0.0558  0.0514  0.1266  0.2205 2018-05-22 11:11:19.000\n","14  0.0327  0.0572  0.0521  0.1268  0.2214 2018-05-22 11:11:19.000\n","15  0.0504  0.0780  0.0774  0.1370  0.2710 2018-05-27 11:11:41.082\n","16  0.0366  0.0557  0.0444  0.1110  0.2340 2018-06-24 11:24:52.538\n","17  0.0369  0.0655  0.0564  0.1121  0.2667 2018-06-26 11:14:13.074\n","18  0.0314  0.0641  0.0569  0.1126  0.2658 2018-06-26 11:14:13.074\n","19  0.0304  0.0473  0.0439  0.1086  0.2396 2018-06-29 11:25:37.824\n","20  0.0288  0.0484  0.0460  0.1092  0.2383 2018-06-29 11:25:37.824\n","21  0.0233  0.0404  0.0329  0.1059  0.2304 2018-07-04 11:21:12.459\n","22  0.0341  0.0490  0.0401  0.1085  0.2285 2018-07-04 11:21:12.459\n","23  0.0317  0.0574  0.0540  0.1208  0.2638 2018-07-06 11:09:18.241\n","24  0.0374  0.0608  0.0570  0.1221  0.2631 2018-07-06 11:09:18.241\n","25  0.2126  0.2504  0.2652  0.3139  0.4067 2018-07-11 11:11:39.550\n","26  0.2056  0.2436  0.2600  0.3077  0.3997 2018-07-11 11:11:39.550\n","27  0.9544  0.8984  0.8824  0.9362  0.9041 2018-07-14 11:21:12.461\n","28  0.3316  0.3272  0.2678  0.3750  0.5478 2018-09-24 11:14:16.000\n","29  0.0280  0.0423  0.0289  0.0744  0.2407 2018-09-27 11:23:30.164\n","30  0.0252  0.0410  0.0283  0.0738  0.2401 2018-09-27 11:23:30.164\n","31  0.0306  0.0468  0.0383  0.1046  0.2532 2018-09-29 11:10:54.000\n","32  0.0200  0.0347  0.0221  0.0968  0.2393 2018-10-09 11:09:42.000\n","33  0.1586  0.1852  0.1560  0.2440  0.4341 2018-10-19 11:10:50.000\n","34  0.1560  0.1840  0.1548  0.2427  0.4330 2018-10-19 11:10:50.000\n","35  0.0258  0.0375  0.0259  0.1070  0.2303 2018-10-29 11:11:52.000\n","36  0.0208  0.0343  0.0238  0.1056  0.2285 2018-10-29 11:11:52.000\n","37  0.2886  0.2956  0.3006  0.3599  0.4760 2018-11-11 11:26:32.000\n","38  0.0115  0.0383  0.0321  0.0984  0.1921 2018-11-13 11:16:32.341\n","39  0.0097  0.0368  0.0312  0.0981  0.1916 2018-11-13 11:16:17.485\n","40  0.0302  0.0305  0.0255  0.0848  0.2062 2018-11-18 11:16:34.000"]},"execution_count":133,"metadata":{},"output_type":"execute_result"}],"source":["# Define the location of interest with a point.\n","poi = ee.Geometry.Point(r_lon, r_lat)\n","\n","selected_features = ['B2','B3','B4','B5','B6']\n","s2 = (ee.ImageCollection('COPERNICUS/S2_SR')\n","    .filterBounds(poi)\n","    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',CloudCoverMax))\n","    .filter(ee.Filter.lt('CLOUD_COVERAGE_ASSESSMENT',CloudCoverMax))\n","    .select(selected_features)\n","    .filterDate(ee.Date(i_date), ee.Date(f_date)))\n","\n","# Evaluate local precipitation conditions.\n","local_pr = s2.getRegion(poi, scale).getInfo()\n","pr_df = ee_array_to_df(local_pr, selected_features)\n","\n","#sentinel bands are in scale of 0.0001\n","#adjust the scale of the values\n","pr = np.array(pr_df[selected_features])/10000\n","prd = pd.DataFrame(pr, columns=selected_features)\n","prd['datetime'] = pr_df['datetime']\n","prd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":909,"status":"ok","timestamp":1683889285592,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"2Dem7JJmn1qX","outputId":"f9a8d70e-9933-4769-d2d6-cba9527be8de"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-d0ba085c-6972-4bed-bde7-361f7b057b3a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>B2</th>\n","      <th>B3</th>\n","      <th>B4</th>\n","      <th>B5</th>\n","      <th>B6</th>\n","      <th>datetime</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.4629</td>\n","      <td>0.4446</td>\n","      <td>0.4711</td>\n","      <td>0.4864</td>\n","      <td>0.5029</td>\n","      <td>2017-01-04 00:34:10.392</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.1343</td>\n","      <td>0.1479</td>\n","      <td>0.2332</td>\n","      <td>0.2639</td>\n","      <td>0.2918</td>\n","      <td>2017-01-14 00:33:57.570</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.1109</td>\n","      <td>0.1216</td>\n","      <td>0.2135</td>\n","      <td>0.2314</td>\n","      <td>0.2549</td>\n","      <td>2017-01-17 00:43:08.450</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.1066</td>\n","      <td>0.1192</td>\n","      <td>0.2023</td>\n","      <td>0.2237</td>\n","      <td>0.2608</td>\n","      <td>2017-01-27 00:42:43.806</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0968</td>\n","      <td>0.0890</td>\n","      <td>0.1245</td>\n","      <td>0.1316</td>\n","      <td>0.1475</td>\n","      <td>2017-02-06 00:43:02.849</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.1253</td>\n","      <td>0.1373</td>\n","      <td>0.2276</td>\n","      <td>0.2592</td>\n","      <td>0.2886</td>\n","      <td>2017-02-13 00:34:21.612</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.1098</td>\n","      <td>0.1230</td>\n","      <td>0.2149</td>\n","      <td>0.2420</td>\n","      <td>0.2608</td>\n","      <td>2017-02-16 00:42:41.201</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.1245</td>\n","      <td>0.1355</td>\n","      <td>0.2295</td>\n","      <td>0.2571</td>\n","      <td>0.2874</td>\n","      <td>2017-02-23 00:30:59.573</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.1081</td>\n","      <td>0.1191</td>\n","      <td>0.2025</td>\n","      <td>0.2216</td>\n","      <td>0.2438</td>\n","      <td>2017-03-08 00:40:59.178</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.1209</td>\n","      <td>0.1297</td>\n","      <td>0.2145</td>\n","      <td>0.2373</td>\n","      <td>0.2680</td>\n","      <td>2017-03-15 00:31:07.400</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.1140</td>\n","      <td>0.1251</td>\n","      <td>0.2102</td>\n","      <td>0.2309</td>\n","      <td>0.2545</td>\n","      <td>2017-03-28 00:41:01.180</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.1234</td>\n","      <td>0.1337</td>\n","      <td>0.2218</td>\n","      <td>0.2432</td>\n","      <td>0.2630</td>\n","      <td>2017-04-04 00:31:13.390</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.1173</td>\n","      <td>0.1254</td>\n","      <td>0.2106</td>\n","      <td>0.2308</td>\n","      <td>0.2497</td>\n","      <td>2017-04-17 00:41:06.180</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.1614</td>\n","      <td>0.1613</td>\n","      <td>0.2196</td>\n","      <td>0.2331</td>\n","      <td>0.2584</td>\n","      <td>2017-04-24 00:31:18.400</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.1182</td>\n","      <td>0.1205</td>\n","      <td>0.1981</td>\n","      <td>0.2239</td>\n","      <td>0.2470</td>\n","      <td>2017-05-07 00:40:58.350</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0.1262</td>\n","      <td>0.1258</td>\n","      <td>0.1915</td>\n","      <td>0.2220</td>\n","      <td>0.2472</td>\n","      <td>2017-05-14 00:31:19.390</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.1146</td>\n","      <td>0.1178</td>\n","      <td>0.1886</td>\n","      <td>0.2107</td>\n","      <td>0.2277</td>\n","      <td>2017-05-27 00:41:09.180</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.1234</td>\n","      <td>0.1217</td>\n","      <td>0.1898</td>\n","      <td>0.2189</td>\n","      <td>0.2411</td>\n","      <td>2017-06-03 00:31:18.400</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>0.2289</td>\n","      <td>0.1839</td>\n","      <td>0.2479</td>\n","      <td>0.1700</td>\n","      <td>0.3107</td>\n","      <td>2017-06-23 00:31:15.400</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.1182</td>\n","      <td>0.1136</td>\n","      <td>0.1810</td>\n","      <td>0.2017</td>\n","      <td>0.2255</td>\n","      <td>2017-07-01 00:41:31.440</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>0.1180</td>\n","      <td>0.1191</td>\n","      <td>0.1843</td>\n","      <td>0.2087</td>\n","      <td>0.2269</td>\n","      <td>2017-07-06 00:41:06.180</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>0.1283</td>\n","      <td>0.1137</td>\n","      <td>0.1395</td>\n","      <td>0.1509</td>\n","      <td>0.1551</td>\n","      <td>2017-07-08 00:31:16.400</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>0.1251</td>\n","      <td>0.1211</td>\n","      <td>0.1884</td>\n","      <td>0.2172</td>\n","      <td>0.2337</td>\n","      <td>2017-07-13 00:31:17.390</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>0.1153</td>\n","      <td>0.1156</td>\n","      <td>0.1903</td>\n","      <td>0.2111</td>\n","      <td>0.2339</td>\n","      <td>2017-07-21 00:41:06.180</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>0.1215</td>\n","      <td>0.1190</td>\n","      <td>0.1923</td>\n","      <td>0.2192</td>\n","      <td>0.2329</td>\n","      <td>2017-07-26 00:41:08.180</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>0.1240</td>\n","      <td>0.1238</td>\n","      <td>0.1919</td>\n","      <td>0.2134</td>\n","      <td>0.2299</td>\n","      <td>2017-07-28 00:31:05.570</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>0.1219</td>\n","      <td>0.1227</td>\n","      <td>0.1918</td>\n","      <td>0.2178</td>\n","      <td>0.2314</td>\n","      <td>2017-08-02 00:33:31.890</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>0.1142</td>\n","      <td>0.1172</td>\n","      <td>0.1925</td>\n","      <td>0.2228</td>\n","      <td>0.2424</td>\n","      <td>2017-08-10 00:41:05.180</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>0.1151</td>\n","      <td>0.1169</td>\n","      <td>0.1914</td>\n","      <td>0.2124</td>\n","      <td>0.2353</td>\n","      <td>2017-08-15 00:43:18.060</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>0.1198</td>\n","      <td>0.1216</td>\n","      <td>0.1972</td>\n","      <td>0.2269</td>\n","      <td>0.2461</td>\n","      <td>2017-08-17 00:31:13.390</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>0.1202</td>\n","      <td>0.1207</td>\n","      <td>0.1957</td>\n","      <td>0.2250</td>\n","      <td>0.2435</td>\n","      <td>2017-08-22 00:33:30.880</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>0.1109</td>\n","      <td>0.1150</td>\n","      <td>0.1916</td>\n","      <td>0.2263</td>\n","      <td>0.2443</td>\n","      <td>2017-08-30 00:43:11.060</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>0.1228</td>\n","      <td>0.1259</td>\n","      <td>0.2080</td>\n","      <td>0.2335</td>\n","      <td>0.2526</td>\n","      <td>2017-09-06 00:33:22.890</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>0.1133</td>\n","      <td>0.1241</td>\n","      <td>0.2132</td>\n","      <td>0.2316</td>\n","      <td>0.2588</td>\n","      <td>2017-09-19 00:43:05.060</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>0.1158</td>\n","      <td>0.1250</td>\n","      <td>0.2097</td>\n","      <td>0.2306</td>\n","      <td>0.2536</td>\n","      <td>2017-09-24 00:43:12.060</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>0.1223</td>\n","      <td>0.1314</td>\n","      <td>0.2197</td>\n","      <td>0.2347</td>\n","      <td>0.2555</td>\n","      <td>2017-09-26 00:33:15.880</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>0.1226</td>\n","      <td>0.1327</td>\n","      <td>0.2272</td>\n","      <td>0.2530</td>\n","      <td>0.2698</td>\n","      <td>2017-10-01 00:33:26.890</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>0.1108</td>\n","      <td>0.1201</td>\n","      <td>0.2086</td>\n","      <td>0.2249</td>\n","      <td>0.2451</td>\n","      <td>2017-10-09 00:43:02.060</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>0.1116</td>\n","      <td>0.1258</td>\n","      <td>0.2212</td>\n","      <td>0.2373</td>\n","      <td>0.2585</td>\n","      <td>2017-10-14 00:43:15.064</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>0.1227</td>\n","      <td>0.1339</td>\n","      <td>0.2365</td>\n","      <td>0.2564</td>\n","      <td>0.2712</td>\n","      <td>2017-10-16 00:33:15.885</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>0.1218</td>\n","      <td>0.1344</td>\n","      <td>0.2374</td>\n","      <td>0.2677</td>\n","      <td>0.2849</td>\n","      <td>2017-10-21 00:33:33.490</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>0.1117</td>\n","      <td>0.1237</td>\n","      <td>0.2293</td>\n","      <td>0.2557</td>\n","      <td>0.2709</td>\n","      <td>2017-10-29 00:43:28.310</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>0.1162</td>\n","      <td>0.1315</td>\n","      <td>0.2364</td>\n","      <td>0.2533</td>\n","      <td>0.2771</td>\n","      <td>2017-11-03 00:43:41.320</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>0.1899</td>\n","      <td>0.1829</td>\n","      <td>0.2579</td>\n","      <td>0.2598</td>\n","      <td>0.2760</td>\n","      <td>2017-11-05 00:33:42.140</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>0.1134</td>\n","      <td>0.1263</td>\n","      <td>0.2304</td>\n","      <td>0.2600</td>\n","      <td>0.2824</td>\n","      <td>2017-11-08 00:36:51.460</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>0.1107</td>\n","      <td>0.1230</td>\n","      <td>0.2249</td>\n","      <td>0.2431</td>\n","      <td>0.2655</td>\n","      <td>2017-11-18 00:43:29.310</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>0.1214</td>\n","      <td>0.1365</td>\n","      <td>0.2364</td>\n","      <td>0.2607</td>\n","      <td>0.2800</td>\n","      <td>2017-11-25 00:31:29.650</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>0.2496</td>\n","      <td>0.2497</td>\n","      <td>0.3064</td>\n","      <td>0.3086</td>\n","      <td>0.3273</td>\n","      <td>2017-11-30 00:31:36.650</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>0.1330</td>\n","      <td>0.1517</td>\n","      <td>0.2645</td>\n","      <td>0.3024</td>\n","      <td>0.3307</td>\n","      <td>2017-12-05 00:26:56.460</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>0.1130</td>\n","      <td>0.1268</td>\n","      <td>0.2274</td>\n","      <td>0.2611</td>\n","      <td>0.2866</td>\n","      <td>2017-12-08 00:41:20.440</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>0.1148</td>\n","      <td>0.1305</td>\n","      <td>0.2345</td>\n","      <td>0.2555</td>\n","      <td>0.2871</td>\n","      <td>2017-12-13 00:41:26.440</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>0.1247</td>\n","      <td>0.1424</td>\n","      <td>0.2446</td>\n","      <td>0.2653</td>\n","      <td>0.2923</td>\n","      <td>2017-12-15 00:31:29.660</td>\n","    </tr>\n","    <tr>\n","      <th>52</th>\n","      <td>0.1139</td>\n","      <td>0.1279</td>\n","      <td>0.2260</td>\n","      <td>0.2570</td>\n","      <td>0.2812</td>\n","      <td>2017-12-18 00:36:52.460</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>0.3227</td>\n","      <td>0.3115</td>\n","      <td>0.3772</td>\n","      <td>0.3872</td>\n","      <td>0.4091</td>\n","      <td>2017-12-28 00:41:20.440</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0ba085c-6972-4bed-bde7-361f7b057b3a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 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 0.3107 2017-06-23 00:31:15.400\n","19  0.1182  0.1136  0.1810  0.2017  0.2255 2017-07-01 00:41:31.440\n","20  0.1180  0.1191  0.1843  0.2087  0.2269 2017-07-06 00:41:06.180\n","21  0.1283  0.1137  0.1395  0.1509  0.1551 2017-07-08 00:31:16.400\n","22  0.1251  0.1211  0.1884  0.2172  0.2337 2017-07-13 00:31:17.390\n","23  0.1153  0.1156  0.1903  0.2111  0.2339 2017-07-21 00:41:06.180\n","24  0.1215  0.1190  0.1923  0.2192  0.2329 2017-07-26 00:41:08.180\n","25  0.1240  0.1238  0.1919  0.2134  0.2299 2017-07-28 00:31:05.570\n","26  0.1219  0.1227  0.1918  0.2178  0.2314 2017-08-02 00:33:31.890\n","27  0.1142  0.1172  0.1925  0.2228  0.2424 2017-08-10 00:41:05.180\n","28  0.1151  0.1169  0.1914  0.2124  0.2353 2017-08-15 00:43:18.060\n","29  0.1198  0.1216  0.1972  0.2269  0.2461 2017-08-17 00:31:13.390\n","30  0.1202  0.1207  0.1957  0.2250  0.2435 2017-08-22 00:33:30.880\n","31  0.1109  0.1150  0.1916  0.2263  0.2443 2017-08-30 00:43:11.060\n","32  0.1228  0.1259  0.2080  0.2335  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2017-11-25 00:31:29.650\n","47  0.2496  0.2497  0.3064  0.3086  0.3273 2017-11-30 00:31:36.650\n","48  0.1330  0.1517  0.2645  0.3024  0.3307 2017-12-05 00:26:56.460\n","49  0.1130  0.1268  0.2274  0.2611  0.2866 2017-12-08 00:41:20.440\n","50  0.1148  0.1305  0.2345  0.2555  0.2871 2017-12-13 00:41:26.440\n","51  0.1247  0.1424  0.2446  0.2653  0.2923 2017-12-15 00:31:29.660\n","52  0.1139  0.1279  0.2260  0.2570  0.2812 2017-12-18 00:36:52.460\n","53  0.3227  0.3115  0.3772  0.3872  0.4091 2017-12-28 00:41:20.440"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["prd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1683889247390,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"um9Tt2KOnsoC","outputId":"6f79c707-125b-4fda-89ce-9e71c233e3a0"},"outputs":[{"data":{"text/html":["\n","  <div 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<td>0.1192</td>\n","      <td>0.2706</td>\n","      <td>0.3172</td>\n","      <td>0.3274</td>\n","      <td>2018-12-18 00:45:28.634</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.0729</td>\n","      <td>0.1262</td>\n","      <td>0.2814</td>\n","      <td>0.3302</td>\n","      <td>0.3455</td>\n","      <td>2018-12-20 00:35:36.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0621</td>\n","      <td>0.1138</td>\n","      <td>0.2678</td>\n","      <td>0.3151</td>\n","      <td>0.3298</td>\n","      <td>2018-12-23 00:45:32.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0738</td>\n","      <td>0.1300</td>\n","      <td>0.2966</td>\n","      <td>0.3434</td>\n","      <td>0.3572</td>\n","      <td>2018-12-25 00:35:33.000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.0648</td>\n","      <td>0.1220</td>\n","      <td>0.2832</td>\n","      <td>0.3284</td>\n","      <td>0.3357</td>\n","      <td>2018-12-28 00:45:30.000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.0799</td>\n","      <td>0.1338</td>\n","      <td>0.2952</td>\n","      <td>0.3422</td>\n","      <td>0.3567</td>\n","      <td>2018-12-30 00:35:38.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2533317f-9baa-4caf-bdae-8c69d3dc2503')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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0.3572 2018-12-25 00:35:33.000\n","5  0.0648  0.1220  0.2832  0.3284  0.3357 2018-12-28 00:45:30.000\n","6  0.0799  0.1338  0.2952  0.3422  0.3567 2018-12-30 00:35:38.000"]},"execution_count":106,"metadata":{},"output_type":"execute_result"}],"source":["prd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"executionInfo":{"elapsed":848,"status":"ok","timestamp":1683888759453,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"E2Y1FjR8lZju","outputId":"ab5a7f7c-ff54-43de-d59c-b8dfe1e64b6a"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-dd1473c8-ef46-43a2-9f61-2b3988c39f70\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        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<td>3642</td>\n","      <td>4256</td>\n","      <td>4186</td>\n","      <td>4352</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1520641623460</td>\n","      <td>2018-03-10 00:27:03.460</td>\n","      <td>1224</td>\n","      <td>1361</td>\n","      <td>2425</td>\n","      <td>2710</td>\n","      <td>2960</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1520901420460</td>\n","      <td>2018-03-13 00:37:00.460</td>\n","      <td>1556</td>\n","      <td>1662</td>\n","      <td>2648</td>\n","      <td>2972</td>\n","      <td>3161</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1521073619460</td>\n","      <td>2018-03-15 00:26:59.460</td>\n","      <td>1222</td>\n","      <td>1366</td>\n","      <td>2431</td>\n","      <td>2673</td>\n","      <td>2877</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1521333417460</td>\n","      <td>2018-03-18 00:36:57.460</td>\n","      <td>1135</td>\n","      <td>1280</td>\n","      <td>2321</td>\n","      <td>2503</td>\n","      <td>2706</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1521505624460</td>\n","      <td>2018-03-20 00:27:04.460</td>\n","      <td>2565</td>\n","      <td>2532</td>\n","      <td>3288</td>\n","      <td>3435</td>\n","      <td>3667</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1521765422460</td>\n","      <td>2018-03-23 00:37:02.460</td>\n","      <td>1984</td>\n","      <td>2092</td>\n","      <td>2866</td>\n","      <td>2956</td>\n","      <td>3157</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1521937622460</td>\n","      <td>2018-03-25 00:27:02.460</td>\n","      <td>1983</td>\n","      <td>1900</td>\n","      <td>2416</td>\n","      <td>2471</td>\n","      <td>2619</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1522197420460</td>\n","      <td>2018-03-28 00:37:00.460</td>\n","      <td>1070</td>\n","      <td>1172</td>\n","      <td>2211</td>\n","      <td>2493</td>\n","      <td>2710</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1522369626460</td>\n","      <td>2018-03-30 00:27:06.460</td>\n","      <td>1192</td>\n","      <td>1311</td>\n","      <td>2427</td>\n","      <td>2663</td>\n","      <td>2833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd1473c8-ef46-43a2-9f61-2b3988c39f70')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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1520901420460 2018-03-13 00:37:00.460  1556  1662  2648  2972  3161\n","5   1521073619460 2018-03-15 00:26:59.460  1222  1366  2431  2673  2877\n","6   1521333417460 2018-03-18 00:36:57.460  1135  1280  2321  2503  2706\n","7   1521505624460 2018-03-20 00:27:04.460  2565  2532  3288  3435  3667\n","8   1521765422460 2018-03-23 00:37:02.460  1984  2092  2866  2956  3157\n","9   1521937622460 2018-03-25 00:27:02.460  1983  1900  2416  2471  2619\n","10  1522197420460 2018-03-28 00:37:00.460  1070  1172  2211  2493  2710\n","11  1522369626460 2018-03-30 00:27:06.460  1192  1311  2427  2663  2833"]},"execution_count":95,"metadata":{},"output_type":"execute_result"}],"source":["# Evaluate local precipitation conditions.\n","local_pr = s2.getRegion(poi, scale).getInfo()\n","pr_df = ee_array_to_df(local_pr, selected_features)\n","pr_df"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":259,"status":"ok","timestamp":1683888538448,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"XiKOEOxjjLEr","outputId":"819e2d49-9e52-4e22-e3c9-1b71e38e5961"},"outputs":[{"data":{"text/plain":["array([[[0.1241 , 0.1391 , 0.2429 , 0.2668 , 0.28785]]])"]},"execution_count":77,"metadata":{},"output_type":"execute_result"}],"source":["s2_array"]},{"cell_type":"markdown","metadata":{"id":"AjKLKiMxUwdv"},"source":["### s2 point values"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NC7KsW2wU0KI"},"outputs":[],"source":["#test parameters\n","i_date, f_date = '2018-03-01', '2018-03-31'\n","r_lon, r_lat = 142.4377389809555, -30.95203025969217\n","scale = 10\n","selected_features = ['B1','B2','B3','B4','B5','B6','B7','B8','B8A','B9','B11','B12']\n","poi = ee.Geometry.Point(r_lon, r_lat)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TaVLEkAnUwF-"},"outputs":[],"source":["# Reassemble the S2-cloudless collection since the collection filter parameters have changed.\n","s2_sr_cld_col = get_s2_sr_cld_col(poi, i_date, f_date )\n","# Reduce the collection by median\n","s2_sr_median = (s2_sr_cld_col.map(add_cld_shdw_mask)\n","                            .map(apply_cld_shdw_mask))"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":355,"status":"ok","timestamp":1730817316621,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":0},"id":"kxQ4s2sKdby0"},"outputs":[],"source":["# Evaluate local precipitation conditions.\n","local_pr = s2_sr_median.getRegion(poi, scale).getInfo()\n","pr_df = ee_array_to_df(local_pr, selected_features)\n","\n","#adjust the scale of the values\n","pr = np.array(pr_df[selected_features])/10000\n","prd = pd.DataFrame(pr, columns=selected_features)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1683887846368,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"nWtagj2MiV4g","outputId":"a6a23b58-1113-44ed-8cc5-29454ca3e603"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-61b4ba3d-4aad-4156-9a4c-1fd87c0acc7d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>time</th>\n","      <th>datetime</th>\n","      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<td>3332</td>\n","      <td>3421</td>\n","      <td>3383</td>\n","      <td>5141</td>\n","      <td>4569</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1545266136000</td>\n","      <td>2018-12-20 00:35:36.000</td>\n","      <td>481</td>\n","      <td>729</td>\n","      <td>1262</td>\n","      <td>2814</td>\n","      <td>3302</td>\n","      <td>3455</td>\n","      <td>3546</td>\n","      <td>3494</td>\n","      <td>3577</td>\n","      <td>3628</td>\n","      <td>5474</td>\n","      <td>5119</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1545525932000</td>\n","      <td>2018-12-23 00:45:32.000</td>\n","      <td>401</td>\n","      <td>621</td>\n","      <td>1138</td>\n","      <td>2678</td>\n","      <td>3151</td>\n","      <td>3298</td>\n","      <td>3402</td>\n","      <td>3428</td>\n","      <td>3421</td>\n","      <td>3472</td>\n","      <td>5266</td>\n","      <td>4878</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1545698133000</td>\n","      <td>2018-12-25 00:35:33.000</td>\n","      <td>561</td>\n","      <td>738</td>\n","      <td>1300</td>\n","      <td>2966</td>\n","      <td>3434</td>\n","      <td>3572</td>\n","      <td>3659</td>\n","      <td>3752</td>\n","      <td>3696</td>\n","      <td>3725</td>\n","      <td>5544</td>\n","      <td>4960</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1545957930000</td>\n","      <td>2018-12-28 00:45:30.000</td>\n","      <td>471</td>\n","      <td>648</td>\n","      <td>1220</td>\n","      <td>2832</td>\n","      <td>3284</td>\n","      <td>3357</td>\n","      <td>3475</td>\n","      <td>3432</td>\n","      <td>3545</td>\n","      <td>3534</td>\n","      <td>5262</td>\n","      <td>4678</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1546130138000</td>\n","      <td>2018-12-30 00:35:38.000</td>\n","      <td>550</td>\n","      <td>799</td>\n","      <td>1338</td>\n","      <td>2952</td>\n","      <td>3422</td>\n","      <td>3567</td>\n","      <td>3678</td>\n","      <td>3604</td>\n","      <td>3678</td>\n","      <td>3700</td>\n","      <td>5594</td>\n","      <td>5192</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61b4ba3d-4aad-4156-9a4c-1fd87c0acc7d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 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2678  3151  3298   \n","4  1545698133000 2018-12-25 00:35:33.000  561  738  1300  2966  3434  3572   \n","5  1545957930000 2018-12-28 00:45:30.000  471  648  1220  2832  3284  3357   \n","6  1546130138000 2018-12-30 00:35:38.000  550  799  1338  2952  3422  3567   \n","\n","0    B7    B8   B8A    B9   B11   B12  \n","0  3704  3670  3750  3662  5668  4961  \n","1  3345  3332  3421  3383  5141  4569  \n","2  3546  3494  3577  3628  5474  5119  \n","3  3402  3428  3421  3472  5266  4878  \n","4  3659  3752  3696  3725  5544  4960  \n","5  3475  3432  3545  3534  5262  4678  \n","6  3678  3604  3678  3700  5594  5192  "]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["pr_df"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"executionInfo":{"elapsed":323,"status":"ok","timestamp":1683887017623,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"yzD_WlOzfMKU","outputId":"c5200fd8-53ad-42b3-87d9-42d10d71390b"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-23ca2070-c535-4069-ae42-d60e135530a7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>B1</th>\n","      <th>B2</th>\n","      <th>B3</th>\n","      <th>B4</th>\n","      <th>B5</th>\n","      <th>B6</th>\n","      <th>B7</th>\n","      <th>B8</th>\n","      <th>B8A</th>\n","      <th>B9</th>\n","      <th>B11</th>\n","      <th>B12</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0589</td>\n","      <td>0.0732</td>\n","      <td>0.1362</td>\n","      <td>0.3012</td>\n","      <td>0.3496</td>\n","      <td>0.3630</td>\n","      <td>0.3704</td>\n","      <td>0.3670</td>\n","      <td>0.3750</td>\n","      <td>0.3662</td>\n","      <td>0.5668</td>\n","      <td>0.4961</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0412</td>\n","      <td>0.0600</td>\n","      <td>0.1192</td>\n","      <td>0.2706</td>\n","      <td>0.3172</td>\n","      <td>0.3274</td>\n","      <td>0.3345</td>\n","      <td>0.3332</td>\n","      <td>0.3421</td>\n","      <td>0.3383</td>\n","      <td>0.5141</td>\n","      <td>0.4569</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.0481</td>\n","      <td>0.0729</td>\n","      <td>0.1262</td>\n","      <td>0.2814</td>\n","      <td>0.3302</td>\n","      <td>0.3455</td>\n","      <td>0.3546</td>\n","      <td>0.3494</td>\n","      <td>0.3577</td>\n","      <td>0.3628</td>\n","      <td>0.5474</td>\n","      <td>0.5119</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0401</td>\n","      <td>0.0621</td>\n","      <td>0.1138</td>\n","      <td>0.2678</td>\n","      <td>0.3151</td>\n","      <td>0.3298</td>\n","      <td>0.3402</td>\n","      <td>0.3428</td>\n","      <td>0.3421</td>\n","      <td>0.3472</td>\n","      <td>0.5266</td>\n","      <td>0.4878</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0561</td>\n","      <td>0.0738</td>\n","      <td>0.1300</td>\n","      <td>0.2966</td>\n","      <td>0.3434</td>\n","      <td>0.3572</td>\n","      <td>0.3659</td>\n","      <td>0.3752</td>\n","      <td>0.3696</td>\n","      <td>0.3725</td>\n","      <td>0.5544</td>\n","      <td>0.4960</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.0471</td>\n","      <td>0.0648</td>\n","      <td>0.1220</td>\n","      <td>0.2832</td>\n","      <td>0.3284</td>\n","      <td>0.3357</td>\n","      <td>0.3475</td>\n","      <td>0.3432</td>\n","      <td>0.3545</td>\n","      <td>0.3534</td>\n","      <td>0.5262</td>\n","      <td>0.4678</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.0550</td>\n","      <td>0.0799</td>\n","      <td>0.1338</td>\n","      <td>0.2952</td>\n","      <td>0.3422</td>\n","      <td>0.3567</td>\n","      <td>0.3678</td>\n","      <td>0.3604</td>\n","      <td>0.3678</td>\n","      <td>0.3700</td>\n","      <td>0.5594</td>\n","      <td>0.5192</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23ca2070-c535-4069-ae42-d60e135530a7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path 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generate bounds\n","    bounds = [[r_lon+0.001, r_lat+0.001],[r_lon-0.001, r_lat-0.001]]\n","    #aoi of vineyard\n","    aoi_vineyard = ee.Geometry.Polygon([[[bounds[0][0], bounds[0][1]],\n","                                        [bounds[0][0], bounds[1][1]],\n","                                        [bounds[1][0], bounds[1][1]],\n","                                        [bounds[1][0], bounds[0][1]]]])\n","\n","    longitude, latitude = (bounds[0][0] + bounds[1][0])/2, (bounds[0][1] + bounds[1][1])/2\n","    AOI = ee.Geometry.Point(longitude, latitude)\n","\n","    # Reassemble the S2-cloudless collection since the collection filter parameters have changed.\n","    s2_sr_cld_col = get_s2_sr_cld_col(AOI, START_DATE, END_DATE)\n","    # Reduce the collection by median\n","    s2_sr_median = (s2_sr_cld_col.map(add_cld_shdw_mask)\n","                                .map(apply_cld_shdw_mask)\n","                                .median())\n","\n","    #creating downloading file name for single image\n","    file_name = s2_sr_cld_col.getInfo()['features'][-1]['properties']['DATASTRIP_ID']\n","    #convert image format\n","    s2_array = convert_bands_2_numpy(s2_sr_median, aoi_vineyard)\n","\n","    return s2_array\n","\n","def get_s2_band_features(s2_array, selected_bands, indexnams):\n","    central_row, central_col = int(s2_array.shape[0]/2), int(s2_array.shape[1]/2)\n","    #find the central point\n","    if s2_array.shape[0]%2 == 0:\n","        central_row = int(s2_array.shape[0]/2)\n","    else:\n","        central_row = int(s2_array.shape[0]/2)+1\n","\n","    if s2_array.shape[1]%2 == 0:\n","        central_col = int(s2_array.shape[1]/2)\n","    else:\n","        central_col = int(s2_array.shape[1]/2)+1\n","    #generate vegetation indices\n","    imdat = VegIndicesarrayL2A(s2_array[central_row:central_row+1,central_col:central_col+1,:])\n","\n","    #generate band values\n","    # selected_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']\n","    s2_band_values = s2_array[central_row:central_row+1,central_col:central_col+1,:]\n","    s2_bands = pd.DataFrame(np.array(s2_band_values.flatten()).reshape([1,-1]), columns = selected_bands)\n","\n","    return s2_bands"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":110},"executionInfo":{"elapsed":436,"status":"ok","timestamp":1683208177672,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"qlr3m_KgBoHy","outputId":"876b45b1-ce76-48dc-a470-a57bf1af6054"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-4112e428-63ec-46e7-811d-4b213069a6b6\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>B1</th>\n","      <th>B2</th>\n","      <th>B3</th>\n","      <th>B4</th>\n","      <th>B5</th>\n","      <th>B6</th>\n","      <th>B7</th>\n","      <th>B8</th>\n","      <th>B8A</th>\n","      <th>B9</th>\n","      <th>...</th>\n","      <th>N2NDVI</th>\n","      <th>RENDVI1</th>\n","      <th>RENDVI2</th>\n","      <th>RDVI1</th>\n","      <th>RDVI2</th>\n","      <th>TDVI</th>\n","      <th>TDVI2</th>\n","      <th>NLI</th>\n","      <th>LAIG</th>\n","      <th>SM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0186</td>\n","      <td>0.0339</td>\n","      <td>0.0607</td>\n","      <td>0.0434</td>\n","      <td>0.0984</td>\n","      <td>0.2559</td>\n","      <td>0.32025</td>\n","      <td>0.3532</td>\n","      <td>0.34195</td>\n","      <td>0.32755</td>\n","      <td>...</td>\n","      <td>0.77475</td>\n","      <td>0.564216</td>\n","      <td>0.553083</td>\n","      <td>0.491932</td>\n","      <td>0.77475</td>\n","      <td>0.568507</td>\n","      <td>0.551097</td>\n","      <td>0.483795</td>\n","      <td>2.875412</td>\n","      <td>0.45424</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 32 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4112e428-63ec-46e7-811d-4b213069a6b6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-4112e428-63ec-46e7-811d-4b213069a6b6 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["       B1      B2      B3      B4      B5      B6       B7      B8      B8A  \\\n","0  0.0186  0.0339  0.0607  0.0434  0.0984  0.2559  0.32025  0.3532  0.34195   \n","\n","        B9  ...   N2NDVI   RENDVI1   RENDVI2     RDVI1    RDVI2      TDVI  \\\n","0  0.32755  ...  0.77475  0.564216  0.553083  0.491932  0.77475  0.568507   \n","\n","      TDVI2       NLI      LAIG       SM  \n","0  0.551097  0.483795  2.875412  0.45424  \n","\n","[1 rows x 32 columns]"]},"execution_count":106,"metadata":{},"output_type":"execute_result"}],"source":["s2_features"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":28,"status":"ok","timestamp":1683201879348,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"Y_K1uKIyW0I5","outputId":"1f0785b1-2403-4c2c-a3dc-f534060de0af"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_rgb_image(s2_array)"]},{"cell_type":"markdown","metadata":{"id":"DB0BtHJq8nYy"},"source":["## s2 time series download"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eNh6-YKLEi4Z"},"outputs":[],"source":["def get_s2_pointwise_band_values(r_lon, r_lat, scale, i_date, f_date, selected_bands):\n","    CloudCoverMax = 20\n","    # selected_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']\n","\n","    # Define the location of interest with a point.\n","    poi = ee.Geometry.Point(r_lon, r_lat)\n","\n","    # selected_features = ['B2','B3','B4','B5','B6']\n","    s2 = (ee.ImageCollection('COPERNICUS/S2_SR')\n","        .filterBounds(poi)\n","        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',CloudCoverMax))\n","        .filter(ee.Filter.lt('CLOUD_COVERAGE_ASSESSMENT',CloudCoverMax))\n","        .select(selected_bands)\n","        .filterDate(i_date, f_date)\n","        .median())\n","\n","    #get the metadata of the band\n","    s2_band_vals = s2.sample(poi, scale).first().getInfo()\n","    #reorder the band values referencing selected_bands\n","    df = pd.DataFrame(s2_band_vals['properties'], index=['i',])\n","\n","    #sentinel bands are in scale of 0.0001\n","    s2_array = np.array(df[selected_bands])/10000\n","    #reshape file\n","    s2_array = s2_array.reshape([1,1,-1])\n","\n","    return s2_array"]},{"cell_type":"markdown","metadata":{"id":"z1OrB-ggHwIv"},"source":["### test example"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":377,"status":"ok","timestamp":1683276686754,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"OdQR84f-FAUx","outputId":"53251826-c6c1-4f2a-b23f-81816673669c"},"outputs":[{"data":{"text/plain":["(array([[[0.0476 , 0.06885, 0.1241 , 0.2823 , 0.3293 , 0.3406 , 0.35105,\n","          0.3463 , 0.3561 , 0.3581 , 0.537  , 0.4919 ]]]),\n"," (1, 1, 12))"]},"execution_count":98,"metadata":{},"output_type":"execute_result"}],"source":["i_date, f_date = '2018-01-01', '2018-12-31'\n","r_lon, r_lat = 142.4377389809555, -30.95203025969217\n","scale = 10\n","CloudCoverMax = 20\n","selected_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']\n","\n","s2_array = get_s2_pointwise_band_values(r_lon, r_lat, scale, i_date, f_date, selected_bands)\n","s2_array, s2_array.shape"]},{"cell_type":"markdown","metadata":{"id":"ksUoNPAfFyyJ"},"source":["## Sentinel-2 time series\n","\n","We also calculated the 10th and\n","90th percentile NDVI composites for each year in an attempt to characterize vegetation phenologies (e.g. annual grasses versus perennial\n","woody plants)."]},{"cell_type":"markdown","metadata":{"id":"fhaZbovnJyx_"},"source":["### point based"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VGCimLJXMt2T"},"outputs":[],"source":["def ee_array_to_df(arr, list_of_bands):\n","    \"\"\"Transforms client-side ee.Image.getRegion array to pandas.DataFrame.\"\"\"\n","    df = pd.DataFrame(arr)\n","\n","    # Rearrange the header.\n","    headers = df.iloc[0]\n","    df = pd.DataFrame(df.values[1:], columns=headers)\n","\n","    # Remove rows without data inside.\n","    df = df[['longitude', 'latitude', 'time', *list_of_bands]].dropna()\n","\n","    # Convert the data to numeric values.\n","    for band in list_of_bands:\n","        df[band] = pd.to_numeric(df[band], errors='coerce')\n","\n","    # Convert the time field into a datetime.\n","    df['datetime'] = pd.to_datetime(df['time'], unit='ms')\n","\n","    # Keep the columns of interest.\n","    df = df[['time','datetime',  *list_of_bands]]\n","\n","    return df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g0cYraYyW2nk"},"outputs":[],"source":["def get_s2_time_series(r_lon, r_lat, scale, selected_features, i_date, f_date):\n","    CloudCoverMax = 20\n","    # Define the location of interest with a point.\n","    poi = ee.Geometry.Point(r_lon, r_lat)\n","    s2 = (ee.ImageCollection('COPERNICUS/S2_SR')\n","        .filterBounds(poi)\n","        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',CloudCoverMax))\n","        .filter(ee.Filter.lt('CLOUD_COVERAGE_ASSESSMENT',CloudCoverMax))\n","        .select(selected_features)\n","        .filterDate(i_date, f_date))\n","\n","    #get the metadata of the band\n","    local_pr = s2.getRegion(poi, scale).getInfo()\n","    pr_df = ee_array_to_df(local_pr, selected_features)\n","\n","    pr = np.array(pr_df[selected_features])/10000\n","    prd = pd.DataFrame(pr, columns=selected_features)\n","\n","    return prd\n","\n","def get_s2_statistic_features(prd):\n","    dfcd = prd.describe()\n","    dfc_mean = dfcd.loc[['mean']].reset_index(drop=True)\n","    dfc_std = dfcd.loc[['std']].reset_index(drop=True)\n","    dfc_25p = dfcd.loc[['25%']].reset_index(drop=True)\n","    dfc_50p = dfcd.loc[['50%']].reset_index(drop=True)\n","    dfc_75p = dfcd.loc[['75%']].reset_index(drop=True)\n","\n","    dfc_mean.rename(update_dictionary_mean, axis=1, inplace=True)\n","    dfc_std.rename(update_dictionary_std, axis=1, inplace=True)\n","    dfc_25p.rename(update_dictionary_25p, axis=1, inplace=True)\n","    dfc_50p.rename(update_dictionary_50p, axis=1, inplace=True)\n","    dfc_75p.rename(update_dictionary_75p, axis=1, inplace=True)\n","\n","    dfs2 = pd.concat([dfc_25p, dfc_50p, dfc_75p, dfc_mean, dfc_std], axis=1)\n","\n","    return dfs2"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iLlU-nwTe8eF"},"outputs":[],"source":["i_date, f_date = '2018-01-01', '2018-12-31'\n","r_lon, r_lat = 15.291507673637822, 61.554831262246616\n","scale = 10\n","CloudCoverMax = 20\n","selected_features = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2046,"status":"ok","timestamp":1683896995610,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"KL-PINHje43X","outputId":"3201fa2b-7999-4254-a93d-cde2ea16e4d3"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-7931c4dd-a53d-48d3-bb72-e34f6894fe77\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>B1</th>\n","      <th>B2</th>\n","      <th>B3</th>\n","      <th>B4</th>\n","      <th>B5</th>\n","      <th>B6</th>\n","      <th>B7</th>\n","      <th>B8</th>\n","      <th>B8A</th>\n","      <th>B9</th>\n","      <th>B11</th>\n","      <th>B12</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.5875</td>\n","      <td>0.4812</td>\n","      <td>0.4173</td>\n","      <td>0.4150</td>\n","      <td>0.4205</td>\n","      <td>0.4902</td>\n","      <td>0.4611</td>\n","      <td>0.5371</td>\n","      <td>0.4738</td>\n","      <td>0.5571</td>\n","      <td>0.0278</td>\n","      <td>0.0237</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.6237</td>\n","      <td>0.5073</td>\n","      <td>0.4763</td>\n","      <td>0.4568</td>\n","      <td>0.5055</td>\n","      <td>0.5483</td>\n","      <td>0.5208</td>\n","      <td>0.5840</td>\n","      <td>0.5424</td>\n","      <td>0.5862</td>\n","      <td>0.0819</td>\n","      <td>0.0775</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.7154</td>\n","      <td>0.7058</td>\n","      <td>0.6989</td>\n","      <td>0.6763</td>\n","      <td>0.6927</td>\n","      <td>0.7181</td>\n","      <td>0.7101</td>\n","      <td>0.6740</td>\n","      <td>0.6801</td>\n","      <td>0.6534</td>\n","      <td>0.0664</td>\n","      <td>0.0545</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6838</td>\n","      <td>0.6832</td>\n","      <td>0.6690</td>\n","      <td>0.6632</td>\n","      <td>0.6458</td>\n","      <td>0.6566</td>\n","      <td>0.6438</td>\n","      <td>0.6738</td>\n","      <td>0.5927</td>\n","      <td>0.6389</td>\n","      <td>0.0719</td>\n","      <td>0.0612</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.7489</td>\n","      <td>0.6655</td>\n","      <td>0.6711</td>\n","      <td>0.6474</td>\n","      <td>0.6725</td>\n","      <td>0.6957</td>\n","      <td>0.6238</td>\n","      <td>0.6380</td>\n","      <td>0.5923</td>\n","      <td>0.6221</td>\n","      <td>0.0406</td>\n","      <td>0.0302</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.7187</td>\n","      <td>0.6521</td>\n","      <td>0.6721</td>\n","      <td>0.6568</td>\n","      <td>0.6590</td>\n","      <td>0.6573</td>\n","      <td>0.6045</td>\n","      <td>0.6421</td>\n","      <td>0.5555</td>\n","      <td>0.5964</td>\n","      <td>0.0331</td>\n","      <td>0.0224</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.7647</td>\n","      <td>0.6335</td>\n","      <td>0.6543</td>\n","      <td>0.6214</td>\n","      <td>0.6718</td>\n","      <td>0.6775</td>\n","      <td>0.6007</td>\n","      <td>0.6110</td>\n","      <td>0.5617</td>\n","      <td>0.6025</td>\n","      <td>0.0275</td>\n","      <td>0.0178</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.0288</td>\n","      <td>0.0243</td>\n","      <td>0.0374</td>\n","      <td>0.0459</td>\n","      <td>0.0976</td>\n","      <td>0.1595</td>\n","      <td>0.1839</td>\n","      <td>0.2057</td>\n","      <td>0.2137</td>\n","      <td>0.2164</td>\n","      <td>0.1696</td>\n","      <td>0.0933</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.0167</td>\n","      <td>0.0297</td>\n","      <td>0.0399</td>\n","      <td>0.0501</td>\n","      <td>0.1021</td>\n","      <td>0.1615</td>\n","      <td>0.1889</td>\n","      <td>0.2163</td>\n","      <td>0.2145</td>\n","      <td>0.2158</td>\n","      <td>0.1857</td>\n","      <td>0.1059</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.0199</td>\n","      <td>0.0266</td>\n","      <td>0.0441</td>\n","      <td>0.0521</td>\n","      <td>0.0999</td>\n","      <td>0.1662</td>\n","      <td>0.1885</td>\n","      <td>0.2171</td>\n","      <td>0.2200</td>\n","      <td>0.2167</td>\n","      <td>0.1882</td>\n","      <td>0.1075</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.8842</td>\n","      <td>0.8134</td>\n","      <td>0.7573</td>\n","      <td>0.7173</td>\n","      <td>0.7434</td>\n","      <td>0.7241</td>\n","      <td>0.7057</td>\n","      <td>0.7080</td>\n","      <td>0.6778</td>\n","      <td>0.8138</td>\n","      <td>0.5358</td>\n","      <td>0.4255</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.0262</td>\n","      <td>0.0255</td>\n","      <td>0.0457</td>\n","      <td>0.0504</td>\n","      <td>0.0993</td>\n","      <td>0.1698</td>\n","      <td>0.1972</td>\n","      <td>0.2189</td>\n","      <td>0.2264</td>\n","      <td>0.2165</td>\n","      <td>0.1812</td>\n","      <td>0.0991</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.0245</td>\n","      <td>0.0228</td>\n","      <td>0.0375</td>\n","      <td>0.0387</td>\n","      <td>0.0779</td>\n","      <td>0.1372</td>\n","      <td>0.1568</td>\n","      <td>0.1706</td>\n","      <td>0.1795</td>\n","      <td>0.2163</td>\n","      <td>0.1233</td>\n","      <td>0.0561</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.0243</td>\n","      <td>0.0332</td>\n","      <td>0.0516</td>\n","      <td>0.0497</td>\n","      <td>0.1000</td>\n","      <td>0.1802</td>\n","      <td>0.2051</td>\n","      <td>0.2268</td>\n","      <td>0.2386</td>\n","      <td>0.2340</td>\n","      <td>0.1830</td>\n","      <td>0.0967</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.0326</td>\n","      <td>0.0336</td>\n","      <td>0.0553</td>\n","      <td>0.0569</td>\n","      <td>0.1119</td>\n","      <td>0.1992</td>\n","      <td>0.2303</td>\n","      <td>0.2512</td>\n","      <td>0.2630</td>\n","      <td>0.2505</td>\n","      <td>0.1938</td>\n","      <td>0.1001</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0.0304</td>\n","      <td>0.0309</td>\n","      <td>0.0487</td>\n","      <td>0.0453</td>\n","      <td>0.0982</td>\n","      <td>0.1910</td>\n","      <td>0.2192</td>\n","      <td>0.2328</td>\n","      <td>0.2501</td>\n","      <td>0.2379</td>\n","      <td>0.1842</td>\n","      <td>0.0921</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.0232</td>\n","      <td>0.0339</td>\n","      <td>0.0521</td>\n","      <td>0.0517</td>\n","      <td>0.1033</td>\n","      <td>0.2007</td>\n","      <td>0.2351</td>\n","      <td>0.2602</td>\n","      <td>0.2588</td>\n","      <td>0.2500</td>\n","      <td>0.2010</td>\n","      <td>0.1046</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.0182</td>\n","      <td>0.0296</td>\n","      <td>0.0479</td>\n","      <td>0.0468</td>\n","      <td>0.0951</td>\n","      <td>0.1917</td>\n","      <td>0.2200</td>\n","      <td>0.2410</td>\n","      <td>0.2533</td>\n","      <td>0.2498</td>\n","      <td>0.1958</td>\n","      <td>0.0967</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>0.0239</td>\n","      <td>0.0266</td>\n","      <td>0.0496</td>\n","      <td>0.0493</td>\n","      <td>0.1037</td>\n","      <td>0.2118</td>\n","      <td>0.2361</td>\n","      <td>0.2586</td>\n","      <td>0.2691</td>\n","      <td>0.2595</td>\n","      <td>0.1987</td>\n","      <td>0.1007</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.0268</td>\n","      <td>0.0349</td>\n","      <td>0.0532</td>\n","      <td>0.0531</td>\n","      <td>0.1080</td>\n","      <td>0.2111</td>\n","      <td>0.2462</td>\n","      <td>0.2658</td>\n","      <td>0.2790</td>\n","      <td>0.2748</td>\n","      <td>0.2098</td>\n","      <td>0.1067</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>0.0186</td>\n","      <td>0.0282</td>\n","      <td>0.0491</td>\n","      <td>0.0433</td>\n","      <td>0.0982</td>\n","      <td>0.2107</td>\n","      <td>0.2467</td>\n","      <td>0.2666</td>\n","      <td>0.2788</td>\n","      <td>0.2693</td>\n","      <td>0.1738</td>\n","      <td>0.0871</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>0.0263</td>\n","      <td>0.0291</td>\n","      <td>0.0543</td>\n","      <td>0.0500</td>\n","      <td>0.1037</td>\n","      <td>0.2236</td>\n","      <td>0.2548</td>\n","      <td>0.2773</td>\n","      <td>0.2854</td>\n","      <td>0.2766</td>\n","      <td>0.2030</td>\n","      <td>0.0978</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>0.0238</td>\n","      <td>0.0296</td>\n","      <td>0.0472</td>\n","      <td>0.0412</td>\n","      <td>0.0908</td>\n","      <td>0.2060</td>\n","      <td>0.2359</td>\n","      <td>0.2646</td>\n","      <td>0.2662</td>\n","      <td>0.2652</td>\n","      <td>0.1927</td>\n","      <td>0.0914</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>0.0323</td>\n","      <td>0.0318</td>\n","      <td>0.0506</td>\n","      <td>0.0489</td>\n","      <td>0.1026</td>\n","      <td>0.2193</td>\n","      <td>0.2487</td>\n","      <td>0.2667</td>\n","      <td>0.2796</td>\n","      <td>0.2838</td>\n","      <td>0.1979</td>\n","      <td>0.0957</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>0.0152</td>\n","      <td>0.0335</td>\n","      <td>0.0585</td>\n","      <td>0.0526</td>\n","      <td>0.1081</td>\n","      <td>0.2200</td>\n","      <td>0.2549</td>\n","      <td>0.2720</td>\n","      <td>0.2848</td>\n","      <td>0.2905</td>\n","      <td>0.2083</td>\n","      <td>0.1031</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>0.0177</td>\n","      <td>0.0352</td>\n","      <td>0.0586</td>\n","      <td>0.0589</td>\n","      <td>0.1086</td>\n","      <td>0.2245</td>\n","      <td>0.2650</td>\n","      <td>0.2840</td>\n","      <td>0.2995</td>\n","      <td>0.2959</td>\n","      <td>0.2154</td>\n","      <td>0.1042</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>0.0287</td>\n","      <td>0.0263</td>\n","      <td>0.0501</td>\n","      <td>0.0494</td>\n","      <td>0.1034</td>\n","      <td>0.2144</td>\n","      <td>0.2521</td>\n","      <td>0.2574</td>\n","      <td>0.2904</td>\n","      <td>0.2872</td>\n","      <td>0.1693</td>\n","      <td>0.0828</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>0.0248</td>\n","      <td>0.0295</td>\n","      <td>0.0473</td>\n","      <td>0.0469</td>\n","      <td>0.0953</td>\n","      <td>0.1992</td>\n","      <td>0.2304</td>\n","      <td>0.2368</td>\n","      <td>0.2704</td>\n","      <td>0.2638</td>\n","      <td>0.1666</td>\n","      <td>0.0833</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>0.0687</td>\n","      <td>0.0572</td>\n","      <td>0.0741</td>\n","      <td>0.0722</td>\n","      <td>0.1236</td>\n","      <td>0.2190</td>\n","      <td>0.2477</td>\n","      <td>0.2596</td>\n","      <td>0.2764</td>\n","      <td>0.4043</td>\n","      <td>0.1645</td>\n","      <td>0.0941</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>0.0377</td>\n","      <td>0.0311</td>\n","      <td>0.0485</td>\n","      <td>0.0453</td>\n","      <td>0.1020</td>\n","      <td>0.2027</td>\n","      <td>0.2358</td>\n","      <td>0.2495</td>\n","      <td>0.2699</td>\n","      <td>0.2622</td>\n","      <td>0.1443</td>\n","      <td>0.0690</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>0.0192</td>\n","      <td>0.0332</td>\n","      <td>0.0478</td>\n","      <td>0.0422</td>\n","      <td>0.0658</td>\n","      <td>0.1107</td>\n","      <td>0.1197</td>\n","      <td>0.1354</td>\n","      <td>0.1306</td>\n","      <td>0.1458</td>\n","      <td>0.0690</td>\n","      <td>0.0363</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>0.0966</td>\n","      <td>0.1360</td>\n","      <td>0.1252</td>\n","      <td>0.1132</td>\n","      <td>0.1729</td>\n","      <td>0.2472</td>\n","      <td>0.2685</td>\n","      <td>0.3198</td>\n","      <td>0.3021</td>\n","      <td>0.4324</td>\n","      <td>0.1806</td>\n","      <td>0.1036</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>0.0207</td>\n","      <td>0.0167</td>\n","      <td>0.0315</td>\n","      <td>0.0301</td>\n","      <td>0.0718</td>\n","      <td>0.1377</td>\n","      <td>0.1621</td>\n","      <td>0.1968</td>\n","      <td>0.1957</td>\n","      <td>0.2107</td>\n","      <td>0.1203</td>\n","      <td>0.0626</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>0.0239</td>\n","      <td>0.0210</td>\n","      <td>0.0246</td>\n","      <td>0.0275</td>\n","      <td>0.0729</td>\n","      <td>0.1377</td>\n","      <td>0.1620</td>\n","      <td>0.1932</td>\n","      <td>0.1946</td>\n","      <td>0.2132</td>\n","      <td>0.1314</td>\n","      <td>0.0694</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>0.0012</td>\n","      <td>0.0229</td>\n","      <td>0.0313</td>\n","      <td>0.0345</td>\n","      <td>0.0726</td>\n","      <td>0.1325</td>\n","      <td>0.1532</td>\n","      <td>0.1642</td>\n","      <td>0.1826</td>\n","      <td>0.2049</td>\n","      <td>0.1330</td>\n","      <td>0.0722</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>0.0057</td>\n","      <td>0.0095</td>\n","      <td>0.0280</td>\n","      <td>0.0285</td>\n","      <td>0.0610</td>\n","      <td>0.1261</td>\n","      <td>0.1442</td>\n","      <td>0.1848</td>\n","      <td>0.1808</td>\n","      <td>0.1998</td>\n","      <td>0.1127</td>\n","      <td>0.0580</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>0.0001</td>\n","      <td>0.0084</td>\n","      <td>0.0152</td>\n","      <td>0.0216</td>\n","      <td>0.0536</td>\n","      <td>0.1129</td>\n","      <td>0.1355</td>\n","      <td>0.1484</td>\n","      <td>0.1585</td>\n","      <td>0.1841</td>\n","      <td>0.1157</td>\n","      <td>0.0596</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>0.0044</td>\n","      <td>0.0033</td>\n","      <td>0.0241</td>\n","      <td>0.0237</td>\n","      <td>0.0635</td>\n","      <td>0.1270</td>\n","      <td>0.1434</td>\n","      <td>0.1744</td>\n","      <td>0.1687</td>\n","      <td>0.1918</td>\n","      <td>0.0987</td>\n","      <td>0.0509</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>0.0074</td>\n","      <td>0.0210</td>\n","      <td>0.0238</td>\n","      <td>0.0247</td>\n","      <td>0.0605</td>\n","      <td>0.1211</td>\n","      <td>0.1363</td>\n","      <td>0.1576</td>\n","      <td>0.1628</td>\n","      <td>0.1877</td>\n","      <td>0.1043</td>\n","      <td>0.0571</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>0.0484</td>\n","      <td>0.0478</td>\n","      <td>0.0380</td>\n","      <td>0.0307</td>\n","      <td>0.0565</td>\n","      <td>0.1137</td>\n","      <td>0.1235</td>\n","      <td>0.1344</td>\n","      <td>0.1568</td>\n","      <td>0.1787</td>\n","      <td>0.0830</td>\n","      <td>0.0382</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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0.6738  0.5927   \n","4   0.7489  0.6655  0.6711  0.6474  0.6725  0.6957  0.6238  0.6380  0.5923   \n","5   0.7187  0.6521  0.6721  0.6568  0.6590  0.6573  0.6045  0.6421  0.5555   \n","6   0.7647  0.6335  0.6543  0.6214  0.6718  0.6775  0.6007  0.6110  0.5617   \n","7   0.0288  0.0243  0.0374  0.0459  0.0976  0.1595  0.1839  0.2057  0.2137   \n","8   0.0167  0.0297  0.0399  0.0501  0.1021  0.1615  0.1889  0.2163  0.2145   \n","9   0.0199  0.0266  0.0441  0.0521  0.0999  0.1662  0.1885  0.2171  0.2200   \n","10  0.8842  0.8134  0.7573  0.7173  0.7434  0.7241  0.7057  0.7080  0.6778   \n","11  0.0262  0.0255  0.0457  0.0504  0.0993  0.1698  0.1972  0.2189  0.2264   \n","12  0.0245  0.0228  0.0375  0.0387  0.0779  0.1372  0.1568  0.1706  0.1795   \n","13  0.0243  0.0332  0.0516  0.0497  0.1000  0.1802  0.2051  0.2268  0.2386   \n","14  0.0326  0.0336  0.0553  0.0569  0.1119  0.1992  0.2303  0.2512  0.2630   \n","15  0.0304  0.0309  0.0487  0.0453  0.0982  0.1910  0.2192  0.2328  0.2501   \n","16  0.0232  0.0339  0.0521  0.0517  0.1033  0.2007  0.2351  0.2602  0.2588   \n","17  0.0182  0.0296  0.0479  0.0468  0.0951  0.1917  0.2200  0.2410  0.2533   \n","18  0.0239  0.0266  0.0496  0.0493  0.1037  0.2118  0.2361  0.2586  0.2691   \n","19  0.0268  0.0349  0.0532  0.0531  0.1080  0.2111  0.2462  0.2658  0.2790   \n","20  0.0186  0.0282  0.0491  0.0433  0.0982  0.2107  0.2467  0.2666  0.2788   \n","21  0.0263  0.0291  0.0543  0.0500  0.1037  0.2236  0.2548  0.2773  0.2854   \n","22  0.0238  0.0296  0.0472  0.0412  0.0908  0.2060  0.2359  0.2646  0.2662   \n","23  0.0323  0.0318  0.0506  0.0489  0.1026  0.2193  0.2487  0.2667  0.2796   \n","24  0.0152  0.0335  0.0585  0.0526  0.1081  0.2200  0.2549  0.2720  0.2848   \n","25  0.0177  0.0352  0.0586  0.0589  0.1086  0.2245  0.2650  0.2840  0.2995   \n","26  0.0287  0.0263  0.0501  0.0494  0.1034  0.2144  0.2521  0.2574  0.2904   \n","27  0.0248  0.0295  0.0473  0.0469  0.0953  0.1992  0.2304  0.2368  0.2704   \n","28  0.0687  0.0572  0.0741  0.0722  0.1236  0.2190  0.2477  0.2596  0.2764   \n","29  0.0377  0.0311  0.0485  0.0453  0.1020  0.2027  0.2358  0.2495  0.2699   \n","30  0.0192  0.0332  0.0478  0.0422  0.0658  0.1107  0.1197  0.1354  0.1306   \n","31  0.0966  0.1360  0.1252  0.1132  0.1729  0.2472  0.2685  0.3198  0.3021   \n","32  0.0207  0.0167  0.0315  0.0301  0.0718  0.1377  0.1621  0.1968  0.1957   \n","33  0.0239  0.0210  0.0246  0.0275  0.0729  0.1377  0.1620  0.1932  0.1946   \n","34  0.0012  0.0229  0.0313  0.0345  0.0726  0.1325  0.1532  0.1642  0.1826   \n","35  0.0057  0.0095  0.0280  0.0285  0.0610  0.1261  0.1442  0.1848  0.1808   \n","36  0.0001  0.0084  0.0152  0.0216  0.0536  0.1129  0.1355  0.1484  0.1585   \n","37  0.0044  0.0033  0.0241  0.0237  0.0635  0.1270  0.1434  0.1744  0.1687   \n","38  0.0074  0.0210  0.0238  0.0247  0.0605  0.1211  0.1363  0.1576  0.1628   \n","39  0.0484  0.0478  0.0380  0.0307  0.0565  0.1137  0.1235  0.1344  0.1568   \n","\n","        B9     B11     B12  \n","0   0.5571  0.0278  0.0237  \n","1   0.5862  0.0819  0.0775  \n","2   0.6534  0.0664  0.0545  \n","3   0.6389  0.0719  0.0612  \n","4   0.6221  0.0406  0.0302  \n","5   0.5964  0.0331  0.0224  \n","6   0.6025  0.0275  0.0178  \n","7   0.2164  0.1696  0.0933  \n","8   0.2158  0.1857  0.1059  \n","9   0.2167  0.1882  0.1075  \n","10  0.8138  0.5358  0.4255  \n","11  0.2165  0.1812  0.0991  \n","12  0.2163  0.1233  0.0561  \n","13  0.2340  0.1830  0.0967  \n","14  0.2505  0.1938  0.1001  \n","15  0.2379  0.1842  0.0921  \n","16  0.2500  0.2010  0.1046  \n","17  0.2498  0.1958  0.0967  \n","18  0.2595  0.1987  0.1007  \n","19  0.2748  0.2098  0.1067  \n","20  0.2693  0.1738  0.0871  \n","21  0.2766  0.2030  0.0978  \n","22  0.2652  0.1927  0.0914  \n","23  0.2838  0.1979  0.0957  \n","24  0.2905  0.2083  0.1031  \n","25  0.2959  0.2154  0.1042  \n","26  0.2872  0.1693  0.0828  \n","27  0.2638  0.1666  0.0833  \n","28  0.4043  0.1645  0.0941  \n","29  0.2622  0.1443  0.0690  \n","30  0.1458  0.0690  0.0363  \n","31  0.4324  0.1806  0.1036  \n","32  0.2107  0.1203  0.0626  \n","33  0.2132  0.1314  0.0694  \n","34  0.2049  0.1330  0.0722  \n","35  0.1998  0.1127  0.0580  \n","36  0.1841  0.1157  0.0596  \n","37  0.1918  0.0987  0.0509  \n","38  0.1877  0.1043  0.0571  \n","39  0.1787  0.0830  0.0382  "]},"execution_count":121,"metadata":{},"output_type":"execute_result"}],"source":["get_s2_time_series(r_lon, r_lat, scale, selected_features, i_date, f_date)"]},{"cell_type":"markdown","metadata":{"id":"FcqedklUJfZJ"},"source":["### Image download"]},{"cell_type":"markdown","metadata":{"id":"qHP_g584JpNh"},"source":["'B3_25p','B3_50p','B12_75p',"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GJhJoNycJfL3"},"outputs":[],"source":["def get_s2_time_series_images(aoi, image_source, target_band, i_date, f_date, scale):\n","    # Load a collection\n","    CloudCoverMax = 20\n","    # Define the location of interest with a point.\n","    poi = ee.Geometry.Point(r_lon, r_lat)\n","    TERRA = (ee.ImageCollection(image_source)\n","        .filterBounds(aoi)\n","        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',CloudCoverMax))\n","        .filter(ee.Filter.lt('CLOUD_COVERAGE_ASSESSMENT',CloudCoverMax))\n","        .select(target_band)\n","        .filterDate(i_date, f_date))\n","\n","    # Extract an image\n","    TERRA_list = TERRA.toList(TERRA.size())\n","\n","    numpy_array = []\n","    for i in range(TERRA.size().getInfo()):\n","        Terra_img = ee.Image(TERRA_list.get(i))\n","        #reproject image with user defined scale\n","        Terra_img = Terra_img.reproject(crs = ee.Projection('EPSG:4326'), scale=scale)\n","\n","        # check if image footprint contains the AOI\n","        image_footprint = Terra_img.geometry()\n","        if image_footprint.contains(aoi).getInfo():\n","            sample = Terra_img.select(target_band).sampleRectangle(region=aoi, defaultValue=0)\n","            numpy_array_new = np.array(sample.get(target_band).getInfo())\n","            numpy_array.append(numpy_array_new)\n","        else:\n","            print(f\"Image {i} does not contain the AOI\")\n","    numpy_array = np.array(numpy_array)\n","    return numpy_array"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bAEvqYHxJ-xt"},"outputs":[],"source":["#input parameter\n","aoi = ee.Geometry.Polygon(\n","        [[[1.665586676010049, 48.55860495478288],\n","          [1.665586676010049, 48.536502067708305],\n","          [1.7085878509368069, 48.536502067708305],\n","          [1.7085878509368069, 48.55860495478288]]])\n","i_date, f_date = '2018-01-01', '2018-12-31'\n","scale = 10\n","\n","image_source = 'COPERNICUS/S2_SR'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0mlfTgkqKHWg"},"outputs":[],"source":["#download selected bands\n","target_band = 'B3'\n","s2band = get_s2_time_series_images(aoi, image_source, target_band, i_date, f_date, scale)\n","#replace 0 with nan\n","s2band = s2band.astype(float)\n","s2band[s2band == 0] = np.nan\n","#compute quantile\n","s2band25p = np.nanquantile(s2band, 0.25, axis=0)\n","s2band50p = np.nanquantile(s2band, 0.5, axis=0)\n","\n","#save the results\n","# np.save(path + 'gee2Ddata/s2band50p.npy', s2band50p)\n","# np.save(path + 'gee2Ddata/s2band25p.npy', s2band25p)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":338},"executionInfo":{"elapsed":561,"status":"ok","timestamp":1684789222471,"user":{"displayName":"Weiying Zhao","userId":"03933767872317880138"},"user_tz":-60},"id":"Swtw1ZTSQyvc","outputId":"c96f095d-9375-4098-b680-ea84ebbcbc9c"},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fe47b24d630>"]},"execution_count":136,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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