{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kOn6Dorgx2wk" }, "source": [ "--------\n", "\n", "# Time series of vegetation indices\n", "\n", "--------\n", "\n", "**Short description**\n", "\n", "This notebook demonstrates a general usecase for the SpaceSense library.\n", "\n", "In this notebook, you will search for, select, and obtain Sentinel-2 data and vegetation indices, and performa simple time series analysis.\n", "\n", "--------\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 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 an agricultural field in Sardinia, can be downloaded [here](../resources/aois/sardinia_field.geojson)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define the AOI\n", "with open(\"../resources/aois/sardinia_field.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_time_series\")\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
80S2B_32TMK_20210108_0_L2A2021-01-0832TMK50.73sentinel-2b022S2B_MSIL2A_20210108T101319_N0214_R022_T32TMK_2...2021-01-08T10:19:47Z100.00.00.000.000.000.049.270.00.00.0
79S2A_32TMK_20210116_0_L2A2021-01-1632TMK100.00sentinel-2a065S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2...2021-01-16T10:29:44Z100.00.00.0097.560.000.00.000.00.00.0
78S2B_32TMK_20210118_0_L2A2021-01-1832TMK100.00sentinel-2b022S2B_MSIL2A_20210118T101249_N0214_R022_T32TMK_2...2021-01-18T10:19:47Z100.00.00.0096.590.490.00.000.00.00.0
77S2A_32TMK_20210123_0_L2A2021-01-2332TMK100.00sentinel-2a022S2A_MSIL2A_20210123T101321_N0214_R022_T32TMK_2...2021-01-23T10:19:48Z100.00.00.0097.400.000.00.000.00.00.0
76S2A_32TMK_20210126_0_L2A2021-01-2632TMK100.00sentinel-2a065S2A_MSIL2A_20210126T102311_N0214_R065_T32TMK_2...2021-01-26T10:29:44Z100.00.00.0097.400.000.00.000.00.00.0
.........................................................
4S2B_32TMK_20211127_0_L2A2021-11-2732TMK89.81sentinel-2b065S2B_MSIL2A_20211127T102259_N0301_R065_T32TMK_2...2021-11-27T10:29:39Z100.00.010.1949.817.910.00.000.00.00.0
3S2A_32TMK_20211212_0_L2A2021-12-1232TMK100.00sentinel-2a065S2A_MSIL2A_20211212T102431_N0301_R065_T32TMK_2...2021-12-12T10:29:43Z100.00.00.0064.097.700.00.000.00.00.0
2S2B_32TMK_20211217_0_L2A2021-12-1732TMK100.00sentinel-2b065S2B_MSIL2A_20211217T102329_N0301_R065_T32TMK_2...2021-12-17T10:29:37Z100.00.00.0065.5818.540.00.000.00.00.0
1S2A_32TMK_20211219_0_L2A2021-12-1932TMK100.00sentinel-2a022S2A_MSIL2A_20211219T101431_N0301_R022_T32TMK_2...2021-12-19T10:19:48Z100.00.00.0067.4810.160.00.000.00.00.0
0S2B_32TMK_20211224_0_L2A2021-12-2432TMK94.91sentinel-2b022S2B_MSIL2A_20211224T101329_N0301_R022_T32TMK_2...2021-12-24T10:19:43Z100.00.05.0944.010.000.00.000.00.00.0
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81 rows × 18 columns

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" ], "text/plain": [ " id date tile valid_pixel_percentage \\\n", "80 S2B_32TMK_20210108_0_L2A 2021-01-08 32TMK 50.73 \n", "79 S2A_32TMK_20210116_0_L2A 2021-01-16 32TMK 100.00 \n", "78 S2B_32TMK_20210118_0_L2A 2021-01-18 32TMK 100.00 \n", "77 S2A_32TMK_20210123_0_L2A 2021-01-23 32TMK 100.00 \n", "76 S2A_32TMK_20210126_0_L2A 2021-01-26 32TMK 100.00 \n", ".. ... ... ... ... \n", "4 S2B_32TMK_20211127_0_L2A 2021-11-27 32TMK 89.81 \n", "3 S2A_32TMK_20211212_0_L2A 2021-12-12 32TMK 100.00 \n", "2 S2B_32TMK_20211217_0_L2A 2021-12-17 32TMK 100.00 \n", "1 S2A_32TMK_20211219_0_L2A 2021-12-19 32TMK 100.00 \n", "0 S2B_32TMK_20211224_0_L2A 2021-12-24 32TMK 94.91 \n", "\n", " platform relative_orbit_number \\\n", "80 sentinel-2b 022 \n", "79 sentinel-2a 065 \n", "78 sentinel-2b 022 \n", "77 sentinel-2a 022 \n", "76 sentinel-2a 065 \n", ".. ... ... \n", "4 sentinel-2b 065 \n", "3 sentinel-2a 065 \n", "2 sentinel-2b 065 \n", "1 sentinel-2a 022 \n", "0 sentinel-2b 022 \n", "\n", " product_id datetime \\\n", "80 S2B_MSIL2A_20210108T101319_N0214_R022_T32TMK_2... 2021-01-08T10:19:47Z \n", "79 S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2... 2021-01-16T10:29:44Z \n", "78 S2B_MSIL2A_20210118T101249_N0214_R022_T32TMK_2... 2021-01-18T10:19:47Z \n", "77 S2A_MSIL2A_20210123T101321_N0214_R022_T32TMK_2... 2021-01-23T10:19:48Z \n", "76 S2A_MSIL2A_20210126T102311_N0214_R065_T32TMK_2... 2021-01-26T10:29:44Z \n", ".. ... ... \n", "4 S2B_MSIL2A_20211127T102259_N0301_R065_T32TMK_2... 2021-11-27T10:29:39Z \n", "3 S2A_MSIL2A_20211212T102431_N0301_R065_T32TMK_2... 2021-12-12T10:29:43Z \n", "2 S2B_MSIL2A_20211217T102329_N0301_R065_T32TMK_2... 2021-12-17T10:29:37Z \n", "1 S2A_MSIL2A_20211219T101431_N0301_R022_T32TMK_2... 2021-12-19T10:19:48Z \n", "0 S2B_MSIL2A_20211224T101329_N0301_R022_T32TMK_2... 2021-12-24T10:19:43Z \n", "\n", " swath_coverage_percentage no_data cloud_shadows vegetation \\\n", "80 100.0 0.0 0.00 0.00 \n", "79 100.0 0.0 0.00 97.56 \n", "78 100.0 0.0 0.00 96.59 \n", "77 100.0 0.0 0.00 97.40 \n", "76 100.0 0.0 0.00 97.40 \n", ".. ... ... ... ... \n", "4 100.0 0.0 10.19 49.81 \n", "3 100.0 0.0 0.00 64.09 \n", "2 100.0 0.0 0.00 65.58 \n", "1 100.0 0.0 0.00 67.48 \n", "0 100.0 0.0 5.09 44.01 \n", "\n", " not_vegetated water cloud_medium_probability cloud_high_probability \\\n", "80 0.00 0.0 49.27 0.0 \n", "79 0.00 0.0 0.00 0.0 \n", "78 0.49 0.0 0.00 0.0 \n", "77 0.00 0.0 0.00 0.0 \n", "76 0.00 0.0 0.00 0.0 \n", ".. ... ... ... ... \n", "4 7.91 0.0 0.00 0.0 \n", "3 7.70 0.0 0.00 0.0 \n", "2 18.54 0.0 0.00 0.0 \n", "1 10.16 0.0 0.00 0.0 \n", "0 0.00 0.0 0.00 0.0 \n", "\n", " thin_cirrus snow \n", "80 0.0 0.0 \n", "79 0.0 0.0 \n", "78 0.0 0.0 \n", "77 0.0 0.0 \n", "76 0.0 0.0 \n", ".. ... ... \n", "4 0.0 0.0 \n", "3 0.0 0.0 \n", "2 0.0 0.0 \n", "1 0.0 0.0 \n", "0 0.0 0.0 \n", "\n", "[81 rows x 18 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_date = \"2021-01-01\"\n", "end_date = \"2022-01-01\"\n", "res_S2 = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date, query_filters = {\"valid_pixel_percentage\" : {\">=\": 50}})\n", "res_S2.filter_duplicate_dates()\n", "res_S2.dataframe" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2021 \n", "\n", " January February March \n", "Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su\n", " 1 2 3 1 2 3 4 \u001b[41m 5\u001b[0m 6 7 1 2 3 4 5 6 \u001b[41m 7\u001b[0m \n", " 4 5 6 7 \u001b[42m 8\u001b[0m 9 10 8 9 10 11 12 13 14 8 9 10 11 12 13 14 \n", "11 12 13 14 15 \u001b[41m16\u001b[0m 17 \u001b[41m15\u001b[0m 16 \u001b[42m17\u001b[0m 18 19 \u001b[42m20\u001b[0m 21 15 16 17 18 19 20 21 \n", "\u001b[42m18\u001b[0m 19 20 21 22 \u001b[41m23\u001b[0m 24 22 23 24 \u001b[41m25\u001b[0m 26 27 28 \u001b[42m22\u001b[0m 23 24 25 26 27 28 \n", "25 \u001b[41m26\u001b[0m 27 28 29 30 31 \u001b[42m29\u001b[0m 30 31 \n", "\n", " April May June \n", "Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su\n", " \u001b[42m 1\u001b[0m 2 3 4 1 2 1 2 3 4 \u001b[41m 5\u001b[0m 6 \n", " 5 \u001b[41m 6\u001b[0m 7 \u001b[42m 8\u001b[0m 9 10 11 3 4 5 6 7 \u001b[42m 8\u001b[0m 9 \u001b[42m 7\u001b[0m 8 9 \u001b[42m10\u001b[0m 11 \u001b[41m12\u001b[0m 13 \n", "12 \u001b[41m13\u001b[0m 14 15 16 17 \u001b[42m18\u001b[0m 10 11 12 \u001b[41m13\u001b[0m 14 15 \u001b[41m16\u001b[0m 14 \u001b[41m15\u001b[0m 16 17 18 19 \u001b[42m20\u001b[0m \n", "19 20 21 22 \u001b[41m23\u001b[0m 24 25 17 \u001b[42m18\u001b[0m 19 20 \u001b[42m21\u001b[0m 22 23 21 \u001b[41m22\u001b[0m 23 24 \u001b[41m25\u001b[0m 26 \u001b[42m27\u001b[0m \n", "\u001b[41m26\u001b[0m 27 \u001b[42m28\u001b[0m 29 30 24 25 \u001b[41m26\u001b[0m 27 28 29 30 28 29 30 \n", "\n", " July August September \n", "Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su\n", " 1 \u001b[41m 2\u001b[0m 3 4 \u001b[41m 1\u001b[0m 1 2 \u001b[41m 3\u001b[0m 4 \u001b[42m 5\u001b[0m \n", "\u001b[41m 5\u001b[0m 6 7 8 9 \u001b[42m10\u001b[0m 11 2 3 \u001b[41m 4\u001b[0m 5 \u001b[42m 6\u001b[0m 7 8 6 7 8 9 10 11 12 \n", "\u001b[41m12\u001b[0m 13 14 \u001b[41m15\u001b[0m 16 \u001b[42m17\u001b[0m 18 \u001b[42m 9\u001b[0m 10 \u001b[41m11\u001b[0m 12 13 \u001b[41m14\u001b[0m 15 \u001b[41m13\u001b[0m 14 15 16 17 \u001b[42m18\u001b[0m 19 \n", "19 \u001b[42m20\u001b[0m 21 \u001b[41m22\u001b[0m 23 24 25 \u001b[42m16\u001b[0m 17 18 \u001b[42m19\u001b[0m 20 \u001b[41m21\u001b[0m 22 20 21 22 23 24 25 26 \n", "26 \u001b[42m27\u001b[0m 28 29 \u001b[42m30\u001b[0m 31 23 24 25 \u001b[42m26\u001b[0m 27 28 \u001b[42m29\u001b[0m 27 \u001b[42m28\u001b[0m 29 30 \n", "\n", " October November December \n", "Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su\n", " 1 2 \u001b[41m 3\u001b[0m 1 2 3 4 5 6 \u001b[42m 7\u001b[0m 1 2 3 4 5 \n", " 4 \u001b[42m 5\u001b[0m 6 7 \u001b[42m 8\u001b[0m 9 \u001b[41m10\u001b[0m 8 9 10 11 12 13 14 6 7 8 9 10 11 \u001b[41m12\u001b[0m \n", "11 12 \u001b[41m13\u001b[0m 14 \u001b[42m15\u001b[0m 16 17 15 16 \u001b[42m17\u001b[0m 18 \u001b[41m19\u001b[0m 20 21 13 14 15 16 \u001b[42m17\u001b[0m 18 \u001b[41m19\u001b[0m \n", "\u001b[42m18\u001b[0m 19 20 21 22 23 24 \u001b[41m22\u001b[0m 23 \u001b[42m24\u001b[0m 25 26 \u001b[42m27\u001b[0m 28 20 21 22 23 \u001b[42m24\u001b[0m 25 26 \n", "\u001b[42m25\u001b[0m 26 27 28 29 30 31 29 30 27 28 29 30 31 \n", "\n", "\u001b[41msentinel-2a (1)\u001b[0m\n", "\u001b[42msentinel-2b (1)\u001b[0m\n", "81 total dates\n" ] } ], "source": [ "print(res_S2.item_collection.calendar())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4 - Specify bands and pre-calculated indices\n", "\n", "Here, we select only the blue, red, and near infrared (NIR) bands of Sentinel-2, as well as the vegetation indices of NDVI and LAI" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "res_S2.output_bands = [\"B02\",\"B04\",\"B08\",\"NDVI\",\"LAI\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5 - Filter search results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select only the results which you require. Learn more about how to filter and manipulate search results with [this example](./3_filter_s1_s2.ipynb).\n", "\n", "In this example, we wish to use data with little cloud interference to see the most accurate representation of vegetation indices. The initial search took only those S2 images with 50% or more \"valid_pixel_percentage\". This initial search can be used to quickly see how many images, in general, are available for the AOI and TOI. Further filtering and refinement can be done later depending on those results, like the one performed below. Here we see there are plenty of available images, so we can further filter them based on \"valid_pixel_percentage\", to attempt to get more cloud-free data.\n", "\n", "It is important to note that the Sentinel cloud detection is not perfect, so some exploration and visualziation of the data may be needed to validate the images. " ] }, { "cell_type": "code", "execution_count": 6, "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
79S2A_32TMK_20210116_0_L2A2021-01-1632TMK100.00sentinel-2a065S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2...2021-01-16T10:29:44Z100.00.00.0097.560.000.00.00.00.00.0
78S2B_32TMK_20210118_0_L2A2021-01-1832TMK100.00sentinel-2b022S2B_MSIL2A_20210118T101249_N0214_R022_T32TMK_2...2021-01-18T10:19:47Z100.00.00.0096.590.490.00.00.00.00.0
77S2A_32TMK_20210123_0_L2A2021-01-2332TMK100.00sentinel-2a022S2A_MSIL2A_20210123T101321_N0214_R022_T32TMK_2...2021-01-23T10:19:48Z100.00.00.0097.400.000.00.00.00.00.0
76S2A_32TMK_20210126_0_L2A2021-01-2632TMK100.00sentinel-2a065S2A_MSIL2A_20210126T102311_N0214_R065_T32TMK_2...2021-01-26T10:29:44Z100.00.00.0097.400.000.00.00.00.00.0
75S2A_32TMK_20210205_0_L2A2021-02-0532TMK100.00sentinel-2a065S2A_MSIL2A_20210205T102221_N0214_R065_T32TMK_2...2021-02-05T10:29:43Z100.00.00.0097.400.160.00.00.00.00.0
.........................................................
4S2B_32TMK_20211127_0_L2A2021-11-2732TMK89.81sentinel-2b065S2B_MSIL2A_20211127T102259_N0301_R065_T32TMK_2...2021-11-27T10:29:39Z100.00.010.1949.817.910.00.00.00.00.0
3S2A_32TMK_20211212_0_L2A2021-12-1232TMK100.00sentinel-2a065S2A_MSIL2A_20211212T102431_N0301_R065_T32TMK_2...2021-12-12T10:29:43Z100.00.00.0064.097.700.00.00.00.00.0
2S2B_32TMK_20211217_0_L2A2021-12-1732TMK100.00sentinel-2b065S2B_MSIL2A_20211217T102329_N0301_R065_T32TMK_2...2021-12-17T10:29:37Z100.00.00.0065.5818.540.00.00.00.00.0
1S2A_32TMK_20211219_0_L2A2021-12-1932TMK100.00sentinel-2a022S2A_MSIL2A_20211219T101431_N0301_R022_T32TMK_2...2021-12-19T10:19:48Z100.00.00.0067.4810.160.00.00.00.00.0
0S2B_32TMK_20211224_0_L2A2021-12-2432TMK94.91sentinel-2b022S2B_MSIL2A_20211224T101329_N0301_R022_T32TMK_2...2021-12-24T10:19:43Z100.00.05.0944.010.000.00.00.00.00.0
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79 rows × 18 columns

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" ], "text/plain": [ " id date tile valid_pixel_percentage \\\n", "79 S2A_32TMK_20210116_0_L2A 2021-01-16 32TMK 100.00 \n", "78 S2B_32TMK_20210118_0_L2A 2021-01-18 32TMK 100.00 \n", "77 S2A_32TMK_20210123_0_L2A 2021-01-23 32TMK 100.00 \n", "76 S2A_32TMK_20210126_0_L2A 2021-01-26 32TMK 100.00 \n", "75 S2A_32TMK_20210205_0_L2A 2021-02-05 32TMK 100.00 \n", ".. ... ... ... ... \n", "4 S2B_32TMK_20211127_0_L2A 2021-11-27 32TMK 89.81 \n", "3 S2A_32TMK_20211212_0_L2A 2021-12-12 32TMK 100.00 \n", "2 S2B_32TMK_20211217_0_L2A 2021-12-17 32TMK 100.00 \n", "1 S2A_32TMK_20211219_0_L2A 2021-12-19 32TMK 100.00 \n", "0 S2B_32TMK_20211224_0_L2A 2021-12-24 32TMK 94.91 \n", "\n", " platform relative_orbit_number \\\n", "79 sentinel-2a 065 \n", "78 sentinel-2b 022 \n", "77 sentinel-2a 022 \n", "76 sentinel-2a 065 \n", "75 sentinel-2a 065 \n", ".. ... ... \n", "4 sentinel-2b 065 \n", "3 sentinel-2a 065 \n", "2 sentinel-2b 065 \n", "1 sentinel-2a 022 \n", "0 sentinel-2b 022 \n", "\n", " product_id datetime \\\n", "79 S2A_MSIL2A_20210116T102351_N0214_R065_T32TMK_2... 2021-01-16T10:29:44Z \n", "78 S2B_MSIL2A_20210118T101249_N0214_R022_T32TMK_2... 2021-01-18T10:19:47Z \n", "77 S2A_MSIL2A_20210123T101321_N0214_R022_T32TMK_2... 2021-01-23T10:19:48Z \n", "76 S2A_MSIL2A_20210126T102311_N0214_R065_T32TMK_2... 2021-01-26T10:29:44Z \n", "75 S2A_MSIL2A_20210205T102221_N0214_R065_T32TMK_2... 2021-02-05T10:29:43Z \n", ".. ... ... \n", "4 S2B_MSIL2A_20211127T102259_N0301_R065_T32TMK_2... 2021-11-27T10:29:39Z \n", "3 S2A_MSIL2A_20211212T102431_N0301_R065_T32TMK_2... 2021-12-12T10:29:43Z \n", "2 S2B_MSIL2A_20211217T102329_N0301_R065_T32TMK_2... 2021-12-17T10:29:37Z \n", "1 S2A_MSIL2A_20211219T101431_N0301_R022_T32TMK_2... 2021-12-19T10:19:48Z \n", "0 S2B_MSIL2A_20211224T101329_N0301_R022_T32TMK_2... 2021-12-24T10:19:43Z \n", "\n", " swath_coverage_percentage no_data cloud_shadows vegetation \\\n", "79 100.0 0.0 0.00 97.56 \n", "78 100.0 0.0 0.00 96.59 \n", "77 100.0 0.0 0.00 97.40 \n", "76 100.0 0.0 0.00 97.40 \n", "75 100.0 0.0 0.00 97.40 \n", ".. ... ... ... ... \n", "4 100.0 0.0 10.19 49.81 \n", "3 100.0 0.0 0.00 64.09 \n", "2 100.0 0.0 0.00 65.58 \n", "1 100.0 0.0 0.00 67.48 \n", "0 100.0 0.0 5.09 44.01 \n", "\n", " not_vegetated water cloud_medium_probability cloud_high_probability \\\n", "79 0.00 0.0 0.0 0.0 \n", "78 0.49 0.0 0.0 0.0 \n", "77 0.00 0.0 0.0 0.0 \n", "76 0.00 0.0 0.0 0.0 \n", "75 0.16 0.0 0.0 0.0 \n", ".. ... ... ... ... \n", "4 7.91 0.0 0.0 0.0 \n", "3 7.70 0.0 0.0 0.0 \n", "2 18.54 0.0 0.0 0.0 \n", "1 10.16 0.0 0.0 0.0 \n", "0 0.00 0.0 0.0 0.0 \n", "\n", " thin_cirrus snow \n", "79 0.0 0.0 \n", "78 0.0 0.0 \n", "77 0.0 0.0 \n", "76 0.0 0.0 \n", "75 0.0 0.0 \n", ".. ... ... \n", "4 0.0 0.0 \n", "3 0.0 0.0 \n", "2 0.0 0.0 \n", "1 0.0 0.0 \n", "0 0.0 0.0 \n", "\n", "[79 rows x 18 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_S2.dataframe = res_S2.dataframe[res_S2.dataframe[\"valid_pixel_percentage\"] > 80]\n", "res_S2.dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6 - Obtain S2 data through Fuse function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We pass the filtered S2 search result object to the \"fuse\" function in order to download the organize the S2 result" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-11-07 16:17:37,288 INFO spacesense.core : created everything\n" ] } ], "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", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7 - See the fused dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the S2 images that we selected are now fused into the same file, and can be easily accessed via date or variable. [Click here]() to learn more about how to use the resulting Xarray Datasets" ] }, { "cell_type": "code", "execution_count": 8, "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: 79, y: 43, x: 88)\n",
                            "Coordinates:\n",
                            "  * time     (time) datetime64[ns] 2021-01-16 2021-01-18 ... 2021-12-24\n",
                            "  * y        (y) float32 39.83 39.83 39.83 39.83 ... 39.83 39.83 39.83 39.83\n",
                            "  * x        (x) float64 8.623 8.623 8.623 8.623 8.623 ... 8.63 8.63 8.63 8.63\n",
                            "Data variables:\n",
                            "    S2_B02   (time, y, x) float32 0.0 0.0123 0.0144 0.0126 ... 0.0247 0.0265 0.0\n",
                            "    S2_B04   (time, y, x) float32 0.0 0.0208 0.0214 0.021 ... 0.0455 0.0449 0.0\n",
                            "    S2_B08   (time, y, x) float32 0.0 0.1198 0.1394 0.1282 ... 0.1478 0.1556 0.0\n",
                            "    S2_NDVI  (time, y, x) float32 nan 0.7041 0.7338 0.7185 ... 0.5292 0.5521 nan\n",
                            "    S2_LAI   (time, y, x) float32 0.4768 1.603 1.801 ... 1.773 1.856 0.4768\n",
                            "Attributes:\n",
                            "    crs:              +init=epsg:4326\n",
                            "    transform:        [ 8.98360017e-05  0.00000000e+00  8.62245945e+00  0.000...\n",
                            "    res:              [8.98360017e-05 9.04414549e-05]\n",
                            "    ulx, uly:         [ 8.62245945 39.83104984]\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: 79, y: 43, x: 88)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2021-01-16 2021-01-18 ... 2021-12-24\n", " * y (y) float32 39.83 39.83 39.83 39.83 ... 39.83 39.83 39.83 39.83\n", " * x (x) float64 8.623 8.623 8.623 8.623 8.623 ... 8.63 8.63 8.63 8.63\n", "Data variables:\n", " S2_B02 (time, y, x) float32 0.0 0.0123 0.0144 0.0126 ... 0.0247 0.0265 0.0\n", " S2_B04 (time, y, x) float32 0.0 0.0208 0.0214 0.021 ... 0.0455 0.0449 0.0\n", " S2_B08 (time, y, x) float32 0.0 0.1198 0.1394 0.1282 ... 0.1478 0.1556 0.0\n", " S2_NDVI (time, y, x) float32 nan 0.7041 0.7338 0.7185 ... 0.5292 0.5521 nan\n", " S2_LAI (time, y, x) float32 0.4768 1.603 1.801 ... 1.773 1.856 0.4768\n", "Attributes:\n", " crs: +init=epsg:4326\n", " transform: [ 8.98360017e-05 0.00000000e+00 8.62245945e+00 0.000...\n", " res: [8.98360017e-05 9.04414549e-05]\n", " ulx, uly: [ 8.62245945 39.83104984]\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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = fusion.dataset\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8 - Plot timeseries of vegetation indices with matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, process the data into time series format" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "ds = fusion.dataset\n", "\n", "# Take the field level mean (average over latitude and longitude) of the vegetation indices and remove N/A\n", "ndvi_ts = ds.S2_NDVI.mean(dim={'y','x'}).dropna(dim='time')\n", "lai_ts = ds.S2_LAI.mean(dim={'y','x'}).dropna(dim='time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then plot" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Veg 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 a time series plot\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "ax2 = ax.twinx()\n", "\n", "# Plot both NDVI and LAI on the same plot, with different y axes\n", "ndvi_ts.plot(color='blue',label=\"NDVI\",ax=ax)\n", "lai_ts.plot(color='green',label=\"LAI\",ax=ax2)\n", "fig.legend()\n", "plt.title(\"Veg Indices Time Series\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, this time series gives a good depiction of the changes in vegetation over a year. However, we do still see some errors, likely due to cloud coverage not correctly classified from the Sentinel scene classification." ] } ], "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 }