{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "--------\n", "\n", "# Time series of custom index and masking\n", "\n", "--------\n", "\n", "**Short description**\n", "\n", "This notebook expands on the previous example, creating a custom water or vegetation index, by creating a time series of the index and masking non-target areas.\n", "\n", "In this notebook, you will search for, select, and obtain Sentinel-2 data over a time period. We want the selected data needs to be cloud-free in order to better analyze the index. Custom indices can then be calculated over the area. A mask only over areas with specific classifications (water for water indices, vegetation for vegetation indices) will be applied and the resulting data plotted. This example examines several water quality indices over a period of one year.\n", "\n", "--------" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "keC_14irx2wq" }, "source": [ "### 1 - Import spacesense object(s) and other dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "l1WpZ6RSx2wr" }, "outputs": [], "source": [ "from spacesense import Client\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import json\n", "\n", "import os\n", "if \"SS_API_KEY\" not in os.environ:\n", " from getpass import getpass\n", " api_key = getpass('Enter your api key : ')\n", " os.environ[\"SS_API_KEY\"] = api_key" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2 - Define AOI and output options\n", "\n", "The GeoJSON of this AOI, containing a small portion of Lake Matano and coastline in Indonesia, can be downloaded [here](../resources/aois/lake_matano.geojson)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define the AOI\n", "with open(\"../resources/aois/lake_matano.geojson\", mode=\"r\") as file:\n", " aoi = json.load(file)\n", "\n", "# Get an instance of the SpaceSense Client object\n", "client = Client(id=\"s2_custom_index_ts_mask\")\n", "\n", "# Enable to save data in local files\n", "client.enable_local_output()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3 - Search S2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddatetilevalid_pixel_percentageplatformrelative_orbit_numberproduct_iddatetimeswath_coverage_percentageno_datacloud_shadowsvegetationnot_vegetatedwatercloud_medium_probabilitycloud_high_probabilitythin_cirrussnow
17S2B_51MUT_20210101_0_L2A2021-01-0151MUT99.12sentinel-2b060S2B_MSIL2A_20210101T021349_N0214_R060_T51MUT_2...2021-01-01T02:18:17Z100.00.00.2524.380.0073.220.530.100.000.0
16S2B_51MUT_20210111_0_L2A2021-01-1151MUT99.01sentinel-2b060S2B_MSIL2A_20210111T021349_N0214_R060_T51MUT_2...2021-01-11T02:18:18Z100.00.00.0925.730.0072.620.000.000.900.0
15S2A_51MUT_20210225_0_L2A2021-02-2551MUT97.55sentinel-2a060S2A_MSIL2A_20210225T021341_N0214_R060_T51MUT_2...2021-02-25T02:18:19Z100.00.00.1621.801.0473.370.361.930.000.0
14S2B_51MUT_20210315_0_L2A2021-03-1551MUT99.98sentinel-2b103S2B_MSIL2A_20210315T022329_N0214_R103_T51MUT_2...2021-03-15T02:28:12Z100.00.00.0025.610.0473.670.020.000.000.0
13S2A_51MUT_20210317_0_L2A2021-03-1751MUT100.00sentinel-2a060S2A_MSIL2A_20210317T021341_N0214_R060_T51MUT_2...2021-03-17T02:18:18Z100.00.00.0026.040.0073.090.000.000.000.0
12S2B_51MUT_20210322_0_L2A2021-03-2251MUT100.00sentinel-2b060S2B_MSIL2A_20210322T021349_N0214_R060_T51MUT_2...2021-03-22T02:18:18Z100.00.00.0025.850.0073.050.000.000.000.0
11S2B_51MUT_20210501_0_L2A2021-05-0151MUT100.00sentinel-2b060S2B_MSIL2A_20210501T021339_N0300_R060_T51MUT_2...2021-05-01T02:18:15Z100.00.00.0026.020.0072.950.000.000.000.0
10S2B_51MUT_20210603_0_L2A2021-06-0351MUT99.08sentinel-2b103S2B_MSIL2A_20210603T022329_N0300_R103_T51MUT_2...2021-06-03T02:28:14Z100.00.00.0024.690.0973.290.230.690.000.0
9S2A_51MUT_20210608_0_L2A2021-06-0851MUT100.00sentinel-2a103S2A_MSIL2A_20210608T022331_N0300_R103_T51MUT_2...2021-06-08T02:28:14Z100.00.00.0026.250.0072.960.000.000.000.0
8S2B_51MUT_20210623_0_L2A2021-06-2351MUT99.95sentinel-2b103S2B_MSIL2A_20210623T022329_N0300_R103_T51MUT_2...2021-06-23T02:28:14Z100.00.00.0025.810.0072.980.000.000.050.0
7S2A_51MUT_20210725_0_L2A2021-07-2551MUT100.00sentinel-2a060S2A_MSIL2A_20210725T021351_N0301_R060_T51MUT_2...2021-07-25T02:18:22Z100.00.00.0026.080.0073.270.000.000.000.0
6S2A_51MUT_20210817_0_L2A2021-08-1751MUT99.44sentinel-2a103S2A_MSIL2A_20210817T022331_N0301_R103_T51MUT_2...2021-08-17T02:28:15Z100.00.00.0024.060.7573.430.030.530.000.0
5S2B_51MUT_20211001_0_L2A2021-10-0151MUT95.10sentinel-2b103S2B_MSIL2A_20211001T022329_N0301_R103_T51MUT_2...2021-10-01T02:28:13Z100.00.00.0025.740.0068.320.600.204.100.0
4S2A_51MUT_20211003_0_L2A2021-10-0351MUT99.66sentinel-2a060S2A_MSIL2A_20211003T021351_N0301_R060_T51MUT_2...2021-10-03T02:18:24Z100.00.00.0226.010.1472.640.320.000.000.0
3S2A_51MUT_20211023_0_L2A2021-10-2351MUT98.51sentinel-2a060S2A_MSIL2A_20211023T021351_N0301_R060_T51MUT_2...2021-10-23T02:18:24Z100.00.00.3523.490.4773.460.300.840.000.0
2S2B_51MUT_20211110_0_L2A2021-11-1051MUT99.97sentinel-2b103S2B_MSIL2A_20211110T022329_N0301_R103_T51MUT_2...2021-11-10T02:28:13Z100.00.00.0026.090.0072.940.000.000.030.0
1S2B_51MUT_20211127_0_L2A2021-11-2751MUT100.00sentinel-2b060S2B_MSIL2A_20211127T021339_N0301_R060_T51MUT_2...2021-11-27T02:18:15Z100.00.00.0026.150.0073.050.000.000.000.0
0S2B_51MUT_20211220_0_L2A2021-12-2051MUT100.00sentinel-2b103S2B_MSIL2A_20211220T022319_N0301_R103_T51MUT_2...2021-12-20T02:28:07Z100.00.00.0025.830.0073.010.000.000.000.0
\n", "
" ], "text/plain": [ " id date tile valid_pixel_percentage \\\n", "17 S2B_51MUT_20210101_0_L2A 2021-01-01 51MUT 99.12 \n", "16 S2B_51MUT_20210111_0_L2A 2021-01-11 51MUT 99.01 \n", "15 S2A_51MUT_20210225_0_L2A 2021-02-25 51MUT 97.55 \n", "14 S2B_51MUT_20210315_0_L2A 2021-03-15 51MUT 99.98 \n", "13 S2A_51MUT_20210317_0_L2A 2021-03-17 51MUT 100.00 \n", "12 S2B_51MUT_20210322_0_L2A 2021-03-22 51MUT 100.00 \n", "11 S2B_51MUT_20210501_0_L2A 2021-05-01 51MUT 100.00 \n", "10 S2B_51MUT_20210603_0_L2A 2021-06-03 51MUT 99.08 \n", "9 S2A_51MUT_20210608_0_L2A 2021-06-08 51MUT 100.00 \n", "8 S2B_51MUT_20210623_0_L2A 2021-06-23 51MUT 99.95 \n", "7 S2A_51MUT_20210725_0_L2A 2021-07-25 51MUT 100.00 \n", "6 S2A_51MUT_20210817_0_L2A 2021-08-17 51MUT 99.44 \n", "5 S2B_51MUT_20211001_0_L2A 2021-10-01 51MUT 95.10 \n", "4 S2A_51MUT_20211003_0_L2A 2021-10-03 51MUT 99.66 \n", "3 S2A_51MUT_20211023_0_L2A 2021-10-23 51MUT 98.51 \n", "2 S2B_51MUT_20211110_0_L2A 2021-11-10 51MUT 99.97 \n", "1 S2B_51MUT_20211127_0_L2A 2021-11-27 51MUT 100.00 \n", "0 S2B_51MUT_20211220_0_L2A 2021-12-20 51MUT 100.00 \n", "\n", " platform relative_orbit_number \\\n", "17 sentinel-2b 060 \n", "16 sentinel-2b 060 \n", "15 sentinel-2a 060 \n", "14 sentinel-2b 103 \n", "13 sentinel-2a 060 \n", "12 sentinel-2b 060 \n", "11 sentinel-2b 060 \n", "10 sentinel-2b 103 \n", "9 sentinel-2a 103 \n", "8 sentinel-2b 103 \n", "7 sentinel-2a 060 \n", "6 sentinel-2a 103 \n", "5 sentinel-2b 103 \n", "4 sentinel-2a 060 \n", "3 sentinel-2a 060 \n", "2 sentinel-2b 103 \n", "1 sentinel-2b 060 \n", "0 sentinel-2b 103 \n", "\n", " product_id datetime \\\n", "17 S2B_MSIL2A_20210101T021349_N0214_R060_T51MUT_2... 2021-01-01T02:18:17Z \n", "16 S2B_MSIL2A_20210111T021349_N0214_R060_T51MUT_2... 2021-01-11T02:18:18Z \n", "15 S2A_MSIL2A_20210225T021341_N0214_R060_T51MUT_2... 2021-02-25T02:18:19Z \n", "14 S2B_MSIL2A_20210315T022329_N0214_R103_T51MUT_2... 2021-03-15T02:28:12Z \n", "13 S2A_MSIL2A_20210317T021341_N0214_R060_T51MUT_2... 2021-03-17T02:18:18Z \n", "12 S2B_MSIL2A_20210322T021349_N0214_R060_T51MUT_2... 2021-03-22T02:18:18Z \n", "11 S2B_MSIL2A_20210501T021339_N0300_R060_T51MUT_2... 2021-05-01T02:18:15Z \n", "10 S2B_MSIL2A_20210603T022329_N0300_R103_T51MUT_2... 2021-06-03T02:28:14Z \n", "9 S2A_MSIL2A_20210608T022331_N0300_R103_T51MUT_2... 2021-06-08T02:28:14Z \n", "8 S2B_MSIL2A_20210623T022329_N0300_R103_T51MUT_2... 2021-06-23T02:28:14Z \n", "7 S2A_MSIL2A_20210725T021351_N0301_R060_T51MUT_2... 2021-07-25T02:18:22Z \n", "6 S2A_MSIL2A_20210817T022331_N0301_R103_T51MUT_2... 2021-08-17T02:28:15Z \n", "5 S2B_MSIL2A_20211001T022329_N0301_R103_T51MUT_2... 2021-10-01T02:28:13Z \n", "4 S2A_MSIL2A_20211003T021351_N0301_R060_T51MUT_2... 2021-10-03T02:18:24Z \n", "3 S2A_MSIL2A_20211023T021351_N0301_R060_T51MUT_2... 2021-10-23T02:18:24Z \n", "2 S2B_MSIL2A_20211110T022329_N0301_R103_T51MUT_2... 2021-11-10T02:28:13Z \n", "1 S2B_MSIL2A_20211127T021339_N0301_R060_T51MUT_2... 2021-11-27T02:18:15Z \n", "0 S2B_MSIL2A_20211220T022319_N0301_R103_T51MUT_2... 2021-12-20T02:28:07Z \n", "\n", " swath_coverage_percentage no_data cloud_shadows vegetation \\\n", "17 100.0 0.0 0.25 24.38 \n", "16 100.0 0.0 0.09 25.73 \n", "15 100.0 0.0 0.16 21.80 \n", "14 100.0 0.0 0.00 25.61 \n", "13 100.0 0.0 0.00 26.04 \n", "12 100.0 0.0 0.00 25.85 \n", "11 100.0 0.0 0.00 26.02 \n", "10 100.0 0.0 0.00 24.69 \n", "9 100.0 0.0 0.00 26.25 \n", "8 100.0 0.0 0.00 25.81 \n", "7 100.0 0.0 0.00 26.08 \n", "6 100.0 0.0 0.00 24.06 \n", "5 100.0 0.0 0.00 25.74 \n", "4 100.0 0.0 0.02 26.01 \n", "3 100.0 0.0 0.35 23.49 \n", "2 100.0 0.0 0.00 26.09 \n", "1 100.0 0.0 0.00 26.15 \n", "0 100.0 0.0 0.00 25.83 \n", "\n", " not_vegetated water cloud_medium_probability cloud_high_probability \\\n", "17 0.00 73.22 0.53 0.10 \n", "16 0.00 72.62 0.00 0.00 \n", "15 1.04 73.37 0.36 1.93 \n", "14 0.04 73.67 0.02 0.00 \n", "13 0.00 73.09 0.00 0.00 \n", "12 0.00 73.05 0.00 0.00 \n", "11 0.00 72.95 0.00 0.00 \n", "10 0.09 73.29 0.23 0.69 \n", "9 0.00 72.96 0.00 0.00 \n", "8 0.00 72.98 0.00 0.00 \n", "7 0.00 73.27 0.00 0.00 \n", "6 0.75 73.43 0.03 0.53 \n", "5 0.00 68.32 0.60 0.20 \n", "4 0.14 72.64 0.32 0.00 \n", "3 0.47 73.46 0.30 0.84 \n", "2 0.00 72.94 0.00 0.00 \n", "1 0.00 73.05 0.00 0.00 \n", "0 0.00 73.01 0.00 0.00 \n", "\n", " thin_cirrus snow \n", "17 0.00 0.0 \n", "16 0.90 0.0 \n", "15 0.00 0.0 \n", "14 0.00 0.0 \n", "13 0.00 0.0 \n", "12 0.00 0.0 \n", "11 0.00 0.0 \n", "10 0.00 0.0 \n", "9 0.00 0.0 \n", "8 0.05 0.0 \n", "7 0.00 0.0 \n", "6 0.00 0.0 \n", "5 4.10 0.0 \n", "4 0.00 0.0 \n", "3 0.00 0.0 \n", "2 0.03 0.0 \n", "1 0.00 0.0 \n", "0 0.00 0.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_date = \"2021-01-01\"\n", "end_date = \"2022-01-01\"\n", "# Scenes should be cloud-free in order to best analyze the resulting indices\n", "res_S2 = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date, query_filters = {\"valid_pixel_percentage\" : {\">=\": 95}})\n", "res_S2.dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4 - Specify bands\n", "\n", "Only selecting bands from S2 that we are interested in. The water quality indices we want to calculate use bands 1, 2, 3, 4, and 8, thus we only choose those bands plus the scene classification." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "res_S2.output_bands = [\"B01\",\"B02\",\"B03\",\"B04\",\"B08\",\"SCL\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5 - Obtain S2 data through Fuse function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-11-07 16:30:23,862 INFO spacesense.core : created everything\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:  (time: 18, y: 2357, x: 327)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2021-01-01 2021-01-11 ... 2021-12-20\n",
       "  * y        (y) float32 -2.487 -2.487 -2.487 -2.487 ... -2.504 -2.504 -2.504\n",
       "  * x        (x) float64 121.3 121.3 121.3 121.3 ... 121.3 121.3 121.3 121.3\n",
       "Data variables:\n",
       "    S2_B01   (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0388 0.0388 0.0 0.0\n",
       "    S2_B02   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0397 0.0401 0.0415 0.0\n",
       "    S2_B03   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0277 0.0267 0.0264 0.0\n",
       "    S2_B04   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0184 0.0179 0.0179 0.0\n",
       "    S2_B08   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0126 0.0122 0.0129 0.0\n",
       "    S2_SCL   (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 6.0 6.0 6.0 6.0 6.0\n",
       "Attributes:\n",
       "    crs:              +init=epsg:4326\n",
       "    transform:        [ 8.98315284e-05  0.00000000e+00  1.21303645e+02  0.000...\n",
       "    res:              [8.98315284e-05 7.00728646e-06]\n",
       "    ulx, uly:         [121.30364507  -2.48706115]\n",
       "    s2_data_lineage:  {"Data origin": "S3 bucket (ARN=arn:aws:s3:::sentinel-c...\n",
       "    additional_info:  {'Info': 'You can put any additional information, such ...
" ], "text/plain": [ "\n", "Dimensions: (time: 18, y: 2357, x: 327)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2021-01-01 2021-01-11 ... 2021-12-20\n", " * y (y) float32 -2.487 -2.487 -2.487 -2.487 ... -2.504 -2.504 -2.504\n", " * x (x) float64 121.3 121.3 121.3 121.3 ... 121.3 121.3 121.3 121.3\n", "Data variables:\n", " S2_B01 (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0388 0.0388 0.0 0.0\n", " S2_B02 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0397 0.0401 0.0415 0.0\n", " S2_B03 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0277 0.0267 0.0264 0.0\n", " S2_B04 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0184 0.0179 0.0179 0.0\n", " S2_B08 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0126 0.0122 0.0129 0.0\n", " S2_SCL (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 6.0 6.0 6.0 6.0 6.0\n", "Attributes:\n", " crs: +init=epsg:4326\n", " transform: [ 8.98315284e-05 0.00000000e+00 1.21303645e+02 0.000...\n", " res: [8.98315284e-05 7.00728646e-06]\n", " ulx, uly: [121.30364507 -2.48706115]\n", " s2_data_lineage: {\"Data origin\": \"S3 bucket (ARN=arn:aws:s3:::sentinel-c...\n", " additional_info: {'Info': 'You can put any additional information, such ..." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fusion = client.fuse(\n", " catalogs_list=[res_S2],\n", " additional_info={\"Info\": \"You can put any additional information, such as custom non-raster or non-vector data, in this parameter\"}\n", " )\n", "fusion.dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6 - Postprocessing: Calculating indices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Various water quality indices are calculated from the resulting S2 band data.\n", "\n", "The following quantities are used, and the empirical formulas can be found through the source paper:\n", "\n", "- Concentration of chlorophyll a (Chl_a) ([source](https://www.researchgate.net/publication/329737110_Use_of_Sentinel_2-MSI_for_water_quality_monitoring_at_Alqueva_reservoir_Portugal))\n", "- Suspended particle matter (SPM) ([source](https://www.researchgate.net/publication/318648040_Application_of_Sentinel_2_MSI_Images_to_Retrieve_Suspended_Particulate_Matter_Concentrations_in_Poyang_Lake))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (time: 18, y: 2357, x: 327)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2021-01-01 2021-01-11 ... 2021-12-20\n",
       "  * y        (y) float32 -2.487 -2.487 -2.487 -2.487 ... -2.504 -2.504 -2.504\n",
       "  * x        (x) float64 121.3 121.3 121.3 121.3 ... 121.3 121.3 121.3 121.3\n",
       "Data variables:\n",
       "    S2_B01   (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0388 0.0388 0.0 0.0\n",
       "    S2_B02   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0397 0.0401 0.0415 0.0\n",
       "    S2_B03   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0277 0.0267 0.0264 0.0\n",
       "    S2_B04   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0184 0.0179 0.0179 0.0\n",
       "    S2_B08   (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0126 0.0122 0.0129 0.0\n",
       "    S2_SCL   (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 6.0 6.0 6.0 6.0 6.0\n",
       "    CHL_A    (time, y, x) float32 nan nan nan nan nan ... 1.121 0.9701 nan nan\n",
       "    SPM      (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 13.72 13.18 14.12 0.0\n",
       "Attributes:\n",
       "    crs:              +init=epsg:4326\n",
       "    transform:        [ 8.98315284e-05  0.00000000e+00  1.21303645e+02  0.000...\n",
       "    res:              [8.98315284e-05 7.00728646e-06]\n",
       "    ulx, uly:         [121.30364507  -2.48706115]\n",
       "    s2_data_lineage:  {"Data origin": "S3 bucket (ARN=arn:aws:s3:::sentinel-c...\n",
       "    additional_info:  {'Info': 'You can put any additional information, such ...
" ], "text/plain": [ "\n", "Dimensions: (time: 18, y: 2357, x: 327)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2021-01-01 2021-01-11 ... 2021-12-20\n", " * y (y) float32 -2.487 -2.487 -2.487 -2.487 ... -2.504 -2.504 -2.504\n", " * x (x) float64 121.3 121.3 121.3 121.3 ... 121.3 121.3 121.3 121.3\n", "Data variables:\n", " S2_B01 (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0388 0.0388 0.0 0.0\n", " S2_B02 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0397 0.0401 0.0415 0.0\n", " S2_B03 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0277 0.0267 0.0264 0.0\n", " S2_B04 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0184 0.0179 0.0179 0.0\n", " S2_B08 (time, y, x) float32 0.0 0.0 0.0 0.0 ... 0.0126 0.0122 0.0129 0.0\n", " S2_SCL (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 6.0 6.0 6.0 6.0 6.0\n", " CHL_A (time, y, x) float32 nan nan nan nan nan ... 1.121 0.9701 nan nan\n", " SPM (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 13.72 13.18 14.12 0.0\n", "Attributes:\n", " crs: +init=epsg:4326\n", " transform: [ 8.98315284e-05 0.00000000e+00 1.21303645e+02 0.000...\n", " res: [8.98315284e-05 7.00728646e-06]\n", " ulx, uly: [121.30364507 -2.48706115]\n", " s2_data_lineage: {\"Data origin\": \"S3 bucket (ARN=arn:aws:s3:::sentinel-c...\n", " additional_info: {'Info': 'You can put any additional information, such ..." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = fusion.dataset\n", "ds = ds.assign(CHL_A = (4.23 * ((ds.S2_B03/ds.S2_B01) ** 3.94)))\n", "ds = ds.assign(SPM = 2887 * (ds.S2_B08)**1.223)\n", "### Necessary to replace infinite values with nan\n", "ds['CHL_A'] = ds['CHL_A'].where(np.isinf(ds['CHL_A'])==False)\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7 - Postprocessing: Masking" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we mask out any non-water classifications of the AOI, as definied by the S2 scene classification (SCL) band. This can also be used with custom raster or vector datasets. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "### The SCL band value 6 is water. This mask looks only at pixels classified as water.\n", "ds['mask'] = ds.S2_SCL.sel(time=\"2021-06-08\")==6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8 - Visualization mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the mask that we created according to the S2 SCL band. We are looking to mask water, classified as \"6\" in the SCL band, represented by the number \"1\" and colored yellow in this mask." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.mask.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9 - Create time series of Chl_a and SPM with and without the mask applied" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Take the AOI level mean (average over lat and lon) of the indices and remove N/A\n", "# The \"where\" function applies the mask we previously created\n", "ts_Chl_a_masked = ds['CHL_A'].where(ds.mask).mean(dim={'y','x'}).dropna(dim='time')\n", "ts_Chl_a = ds['CHL_A'].mean(dim={'y','x'}).dropna(dim='time')\n", "\n", "# Apply the same process to SPM\n", "ts_SPM_masked = ds['SPM'].where(ds.mask).mean(dim={'y','x'}).dropna(dim='time')\n", "ts_SPM = ds['SPM'].mean(dim={'y','x'}).dropna(dim='time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10 - Plot time series\n", "\n", "As we can see, the masking makes a very large difference in the analysis. This is because the AOI includes a large portion of land, which strongly taints the water quality indicies. The reverse scenario is also true when analyzing vegetation indices, the non-vegetated areas in the AOI should be masked." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Water Quality Indices Time Series')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set up and plot time series using matplotlib\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "ax2 = ax.twinx()\n", "ts_Chl_a_masked.plot(ax=ax, color='green', label=\"Chlorophyll-a w/ mask\", linestyle=\"-\")\n", "ts_Chl_a.plot(ax=ax, color='green', label=\"Chlorophyll-a\", linestyle=\"--\")\n", "ts_SPM_masked.plot(ax=ax2, color='blue', label=\"SPM w/ mask\", linestyle=\"-\")\n", "ts_SPM.plot(ax=ax2, color='blue', label=\"SPM\", linestyle=\"--\")\n", "\n", "fig.legend(loc=\"upper left\")\n", "plt.title(\"Water Quality Indices Time Series\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.5 ('venv': venv)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "e1ad9e0bf6960974dd8425f76aaa88b32de1b03d6f54bb6bf7fb6a0ca773e449" } } }, "nbformat": 4, "nbformat_minor": 2 }