EDP_Python_Tutorial.ipynb 1.33 MB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "twenty-celtic",
   "metadata": {},
   "source": [
    "## openEO Python Tutorial\n",
    "### Author michele.claus@eurac.edu\n",
    "### Date: 2021/07/15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "raised-livestock",
   "metadata": {},
   "source": [
    "## Useful links:\n",
    "OpenEO Python Client documentation: https://open-eo.github.io/openeo-python-client/index.html\n",
    "\n",
    "Getting started guide for openEO with python: https://openeo.org/documentation/1.0/python/\n",
    "\n",
    "## Topics:\n",
    "- Login to Eurac's openEO backend\n",
    "- Discover the data sets and processes\n",
    "- Access and download data\n",
    "- Process data via openEO processes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "statewide-malaysia",
   "metadata": {},
   "source": [
    "## Install the Python environment and the required libraries:\n",
    "1. Install Anaconda, the following link provides the instructions for Windows, Linux and Mac: https://docs.anaconda.com/anaconda/install/index.html\n",
    "you can also check if it's already installed typing `conda --version` in a terminal\n",
    "2. Clone this repository:\n",
    "```\n",
    "git clone https://gitlab.inf.unibz.it/ado/openeo4ado.git\n",
    "cd openeo4ado\n",
    "cd python\n",
    "```\n",
    "3. Create and activate the conda environment:\n",
    "```\n",
    "conda env create -f environment.yml\n",
    "conda activate openeo\n",
    "```\n",
    "4. Start the jupyter notebook\n",
    "```\n",
    "jupyter notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tropical-render",
   "metadata": {},
   "source": [
    "### Import all the libraries and utilities functions included in the eo_utils.py file"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 17,
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   "id": "central-internet",
   "metadata": {},
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   "outputs": [],
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   "source": [
    "from eo_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "egyptian-satin",
   "metadata": {},
   "source": [
    "## Connect to EURAC backend"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "id": "fancy-rider",
   "metadata": {},
   "outputs": [],
   "source": [
    "euracHost = \"https://openeo.eurac.edu\"\n",
    "eurac = openeo.connect(euracHost)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unusual-indicator",
   "metadata": {},
   "source": [
    "### Discover the available collections:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "id": "included-magazine",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "        el.src = \"https://cdn.jsdelivr.net/npm/@openeo/vue-components@2/assets/openeo.min.js\";\n",
       "        document.head.appendChild(el);\n",
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       "\n",
       "        var font = document.createElement('font');\n",
       "        font.as = \"font\";\n",
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       "        document.head.appendChild(font);\n",
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       "    }\n",
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The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).\", \"keywords\": [\"RR anomalies\", \"relative precipitation anomalies\", \"precipitation anomalies\", \"ERA5\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"License to use Copernicus Products\", \"sci:citation\": \"N/A\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"Slovenian Environment Agency - ARSO\", \"url\": \"https://www.arso.gov.si/\", \"roles\": [\"Processor\"]}, {\"name\": \"Zentralanstalt f\\u00fcr Meteorologie und Geodynamik - ZAMG\", \"url\": \"https://www.zamg.ac.at/cms/de/aktuell\", \"roles\": [\"Producer\"]}], \"extent\": {\"spatial\": {\"bbox\": [[4.0563685981112565, 42.85381219788743, 17.360182784195487, 50.310634900383555]]}, \"temporal\": {\"interval\": [[\"1978-12-31T12:00:00Z\", \"2020-12-31T12:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"TBD\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"1978-12-31T12:00:00.000Z\", \"2020-12-31T12:00:00.000Z\"], \"step\": \"1-day\"}, \"X\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [4.0563685981112565, 17.360182784195487], \"reference_system\": 3035}, \"Y\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.85381219788743, 50.310634900383555], \"reference_system\": 3035}, \"bands\": {\"type\": \"bands\", \"values\": [\"REL_RR_6\"]}}, \"summaries\": {\"constellation\": [\"N/A\"], \"platform\": [\"N/A\"], \"rows\": 167, \"columns\": 202, \"instruments\": [\"N/A\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"REL_RR_6\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_SM_anomalies_ERA5_QM\", \"title\": \"Soil Moisture Anomalies - ERA5_QM\", \"description\": \"'ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim reanalysis. ERA5-Land offers 'land' variables with an enhanced resolution, compared to ERA5. Albeit, at the time of processing with a higher latency. Therefore, ERA5 was downscaled to the 9 km ERA5-Land grid using a quantile mapping approach. The soil moisture anomalies are based on the original ERA5 fields 'Volumetric soil water layer 1 - 4', representing the following depths: layer 1 (0-7cm), layer 2 (7-28cm), layer 3 (28-100 cm), layer 4 (100-289 cm). Anomalies were calculated based on the period 1981-2010 as a reference.'\", \"keywords\": [\"Soil moisture\", \"soil moisture anomalies\", \"ERA5\", \"ERA5-Land\", \"Copernicus\", \"ADO Project\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"Licence to use Copernicus Products\", \"sci:citation\": \"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor\\u00e1nyi, A., Mu\\u00f1oz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th\\u00e9paut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 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SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.\", \"keywords\": [\"SPEI\", \"standardised precipitation-evapotranspiration index,surface water balance anomalies\", \"ERA5\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"Licence to use Copernicus Products\", \"sci:citation\": \"Vicente-Serrano, S.M., Beguer\\u00eda, S. and J.I. L\\u00f3pez-Moreno, 2009: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. 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SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.\", \"keywords\": [\"SPEI\", \"standardised precipitation-evapotranspiration index,surface water balance anomalies\", \"ERA5\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"Licence to use Copernicus Products\", \"sci:citation\": \"Vicente-Serrano, S.M., Beguer\\u00eda, S. and J.I. 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Boston, American Meteorological Society, 179\\u2013184.\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"Slovenian Environment Agency - ARSO\", \"url\": \"https://www.arso.gov.si/\", \"roles\": [\"Processor\"]}, {\"name\": \"Zentralanstalt f\\u00fcr Meteorologie und Geodynamik - ZAMG\", \"url\": \"https://www.zamg.ac.at/cms/de/aktuell\", \"roles\": [\"Producer\"]}], \"extent\": {\"spatial\": {\"bbox\": [[4.0563685981112565, 42.85381219788743, 17.360182784195487, 50.310634900383555]]}, \"temporal\": {\"interval\": [[\"1978-12-31T12:00:00Z\", \"2020-12-31T12:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"TBD\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"1978-12-31T12:00:00.000Z\", \"2020-12-31T12:00:00.000Z\"], \"step\": \"1-day\"}, \"X\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [4.0563685981112565, 17.360182784195487], \"reference_system\": 3035}, \"Y\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.85381219788743, 50.310634900383555], \"reference_system\": 3035}, \"bands\": {\"type\": \"bands\", \"values\": [\"SPI_6\"]}}, \"summaries\": {\"constellation\": [\"N/A\"], \"platform\": [\"N/A\"], \"rows\": 167, \"columns\": 202, \"instruments\": [\"N/A\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"SPI_6\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_SSPI_10d_SNOWGRID\", \"title\": \"Standardised Snow Pack Index - ERA5_QM SSPI-10\", \"description\": \"The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data.\", \"keywords\": [\"SSPI\", \"standardised snow pack index\", \"ERA5\", \"SNOWGRID\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"Licence to use Copernicus Products\", \"sci:citation\": \"Olefs, Marc, Roland Koch, Wolfgang Sch\\u00f6ner, and Thomas Marke. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961\\u20132020\\u2014A Model Based Approach Atmosphere 11, no. 12: 1330. https://doi.org/10.3390/atmos11121330. EDO Indicator Fact Sheet: Standardised Snow Pack Index: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_sspi.pdf\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"Slovenian Environment Agency - ARSO\", \"url\": \"https://www.arso.gov.si/\", \"roles\": [\"Producer\"]}, {\"name\": \"Zentralanstalt f\\u00fcr Meteorologie und Geodynamik - ZAMG\", \"url\": \"https://www.zamg.ac.at/cms/de/aktuell\", \"roles\": [\"Producer\"]}], \"extent\": {\"spatial\": {\"bbox\": [[4.0563685981112565, 42.85381219788743, 17.360182784195487, 50.310634900383555]]}, \"temporal\": {\"interval\": [[\"1978-12-31T12:00:00Z\", \"2020-12-31T12:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"TBD\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"1978-12-31T12:00:00.000Z\", \"2020-12-31T12:00:00.000Z\"], \"step\": \"P1D\"}, \"X\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [4.0563685981112565, 17.360182784195487], \"reference_system\": 3035}, \"Y\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.85381219788743, 50.310634900383555], \"reference_system\": 3035}, \"bands\": {\"type\": \"bands\", \"values\": [\"SSPI10\"]}}, \"summaries\": {\"constellation\": [\"N/A\"], \"platform\": [\"N/A\"], \"rows\": 167, \"columns\": 202, \"instruments\": [\"N/A\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"SSPI10\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_SSPI_30d_SNOWGRID\", \"title\": \"Standardised Snow Pack Index - ERA5_QM SSPI-30\", \"description\": \"The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data.\", \"keywords\": [\"SSPI\", \"standardised snow pack index\", \"ERA5\", \"SNOWGRID\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"Licence to use Copernicus Products\", \"sci:citation\": \"Olefs, Marc, Roland Koch, Wolfgang Sch\\u00f6ner, and Thomas Marke. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961\\u20132020\\u2014A Model Based Approach Atmosphere 11, no. 12: 1330. https://doi.org/10.3390/atmos11121330. EDO Indicator Fact Sheet: Standardised Snow Pack Index: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_sspi.pdf\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"Slovenian Environment Agency - ARSO\", \"url\": \"https://www.arso.gov.si/\", \"roles\": [\"Producer\"]}, {\"name\": \"Zentralanstalt f\\u00fcr Meteorologie und Geodynamik - ZAMG\", \"url\": \"https://www.zamg.ac.at/cms/de/aktuell\", \"roles\": [\"Producer\"]}], \"extent\": {\"spatial\": {\"bbox\": [[4.0563685981112565, 42.85381219788743, 17.360182784195487, 50.310634900383555]]}, \"temporal\": {\"interval\": [[\"1978-12-31T12:00:00Z\", \"2020-12-31T12:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"TBD\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"1978-12-31T12:00:00.000Z\", \"2020-12-31T12:00:00.000Z\"], \"step\": \"P1D\"}, \"X\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [4.0563685981112565, 17.360182784195487], \"reference_system\": 3035}, \"Y\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.85381219788743, 50.310634900383555], \"reference_system\": 3035}, \"bands\": {\"type\": \"bands\", \"values\": [\"SSPI30\"]}}, \"summaries\": {\"constellation\": [\"N/A\"], \"platform\": [\"N/A\"], \"rows\": 167, \"columns\": 202, \"instruments\": [\"N/A\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"SSPI30\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_SWI_1km_4326\", \"title\": \"Copernicus Surface Soil Moisture - 1km\", \"description\": \"The Soil Water Index quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation.\", \"keywords\": [\"surface soil moisture\", \"ASCAT\", \"Sentinel-1\", \"ADO Project\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"CC-BY-4.0\", \"sci:citation\": \"Copernicus Service information [2015-2020]\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"TU Wien\", \"url\": \"https://www.tuwien.at/en/\", \"roles\": [\"Producer\"]}, {\"name\": \"VITO NV on behalf of the European Commission Joint Research Centre\", \"url\": \"https://vito.be/en\", \"roles\": [\"Provider\"]}], \"extent\": {\"spatial\": {\"bbox\": [[3.691964285713869, 42.995535714283896, 17.156249999999197, 50.55803571428218]]}, \"temporal\": {\"interval\": [[\"2015-01-01T00:00:00Z\", \"2020-04-19T00:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"https://creativecommons.org/licenses/by/4.0/\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"2015-01-01T00:00:00.000Z\", \"2020-04-19T00:00:00.000Z\"], \"step\": \"P1D\"}, \"Long\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [3.691964285713869, 17.156249999999197], \"reference_system\": 4326}, \"bands\": {\"type\": \"bands\", \"values\": [\"SWI_002\", \"SWI_005\", \"SWI_010\", \"SWI_015\", \"SWI_020\", \"SWI_040\", \"SWI_060\", \"SWI_100\"]}, \"Lat\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.995535714283896, 50.55803571428218], \"reference_system\": 4326}}, \"summaries\": {\"constellation\": [\"Sentinel-1; ASCAT\"], \"platform\": [\"Sentinel-1 A/B; MetOp A/B\"], \"rows\": 848, \"columns\": 1509, \"instruments\": [\"SAR Scatterometer\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"SWI_002\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_005\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_010\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_015\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_020\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_040\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_060\", \"center_wavelength\": 0.0, \"gsd\": 0.0}, {\"name\": \"SWI_100\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_TCI_MODIS_231m_3035\", \"title\": \"Temperature Condition Index - 231 m 8 days\", \"description\": \"The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST)  MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions. \", \"keywords\": [\"temperature condition index\", \"tci\", \"modis\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"CC BY 4.0\", \"sci:citation\": \"Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91\\u2013100. Kogan, F. N. (eds) (1997): Global Drought Watch from Space. In: Bulletin of the American Meteorological Society, 78 (4), 621\\u2013636. Kogan, F. N. (eds) (2000): Satellite-Observed Sensitivity of World Land Ecosystems to El Nino/La Nina. In: Remote Sensing of Environment, 74, 445\\u2013462.\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"Eurac Research\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"Processor\"]}, {\"name\": \"NASA\", \"url\": \"https://modis.gsfc.nasa.gov/\", \"roles\": [\"Producer\"]}], \"extent\": {\"spatial\": {\"bbox\": [[3.99537296130427, 42.87349365119738, 17.523924303829347, 50.32636203215727]]}, \"temporal\": {\"interval\": [[\"2001-01-01T00:00:00Z\", \"2021-01-03T00:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"https://creativecommons.org/licenses/by/4.0/\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"2001-01-01T00:00:00.000Z\", \"2021-01-03T00:00:00.000Z\"], \"step\": \"P8D\"}, \"X\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [3.99537296130427, 17.523924303829347], \"reference_system\": 3035}, \"Y\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.87349365119738, 50.32636203215727], \"reference_system\": 3035}, \"bands\": {\"type\": \"bands\", \"values\": [\"8d_tci\"]}}, \"summaries\": {\"constellation\": [\"Terra\"], \"platform\": [\"Terra\"], \"rows\": 3606, \"columns\": 4430, \"instruments\": [\"MODIS\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"8d_tci\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_VCI_MODIS_231m_3035\", \"title\": \"Vegetation Condition Index - 231 m 8 days\", \"description\": \"The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.\", \"keywords\": [\"vegetation condition index\", \"vci\", \"modis\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"CC BY 4.0\", \"sci:citation\": \"Michele Meroni, Dominique Fasbender, Felix Rembold, Clement Atzberger, Anja Klisch: Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options, Remote Sensing of Environment, Volume 221, 2019, Pages 508-521, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.11.041, (https://www.sciencedirect.com/science/article/pii/S0034425718305509)\", \"providers\": [{\"name\": \"Eurac EO WCS\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"host\"]}, {\"name\": \"Eurac Research\", \"url\": \"http://www.eurac.edu\", \"roles\": [\"Processor\"]}, {\"name\": \"NASA\", \"url\": \"https://modis.gsfc.nasa.gov/\", \"roles\": [\"Producer\"]}], \"extent\": {\"spatial\": {\"bbox\": [[3.99537296130427, 42.87349365119738, 17.523924303829347, 50.32636203215727]]}, \"temporal\": {\"interval\": [[\"2001-01-01T00:00:00Z\", \"2021-01-03T00:00:00Z\"]]}}, \"links\": [{\"rel\": \"licence\", \"href\": \"https://creativecommons.org/licenses/by/4.0/\", \"type\": \"text/html\", \"title\": \"License Link\"}], \"cube:dimensions\": {\"DATE\": {\"type\": \"temporal\", \"extent\": [\"2001-01-01T00:00:00.000Z\", \"2021-01-03T00:00:00.000Z\"], \"step\": \"P8D\"}, \"X\": {\"type\": \"spatial\", \"axis\": \"x\", \"extent\": [3.99537296130427, 17.523924303829347], \"reference_system\": 3035}, \"Y\": {\"type\": \"spatial\", \"axis\": \"y\", \"extent\": [42.87349365119738, 50.32636203215727], \"reference_system\": 3035}, \"bands\": {\"type\": \"bands\", \"values\": [\"8d_vci\"]}}, \"summaries\": {\"constellation\": [\"Terra\"], \"platform\": [\"Terra\"], \"rows\": 3606, \"columns\": 4430, \"instruments\": [\"MODIS\"], \"eo:cloud cover\": {\"min\": 0.0, \"max\": 0.0}, \"gsd\": [], \"eo:bands\": [{\"name\": \"8d_vci\", \"center_wavelength\": 0.0, \"gsd\": 0.0}]}, \"assets\": {}}, {\"engine\": \"WCPS\", \"stac_version\": \"0.9.0\", \"stac_extensions\": [\"datacube\"], \"id\": \"ADO_VHI_MODIS_231m_3035\", \"title\": \"Vegetation Health Index - 231 m 8 days\", \"description\": \"The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m  resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.\", \"keywords\": [\"vegetation health index\", \"vhi\", \"modis\"], \"version\": \"v1\", \"deprecated\": false, \"license\": \"CC BY 4.0\", \"sci:citation\": \"Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91\\u2013100. Kogan, F. N. (eds) (1997): Global Drought Watch from Space. In: Bulletin of the American Meteorological Society, 78 (4), 621\\u2013636. Kogan, F. N. (eds) (2000): Satellite-Observed Sensitivity of World Land Ecosystems to El Nino/La Nina. 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       "    </openeo-collections>\n",
       "    "
      ],
      "text/plain": [
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       "[{'stac_version': '0.9.0',\n",
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       "  'title': 'Corine Land Cover (CLC) 2018',\n",
       "  'description': 'CLC2018 is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as \"CORINE Land Cover Programme\"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.',\n",
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       "   'Land',\n",
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       "   '2018',\n",
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       "   'Corine Land Cover'],\n",
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       "  'version': 'v1',\n",
       "  'deprecated': False,\n",
       "  'license': 'CC-BY-4.0',\n",
       "  'sci:citation': 'Copyright holder: European Environment Agency (EEA)',\n",
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       "  'providers': [{'name': 'Eurac EO ODC',\n",
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       "    'roles': ['host']},\n",
       "   {'name': 'European Environment Agency (EEA)',\n",
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       "    'url': 'mailto:copernicus@eea.europa.eu',\n",
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       "  'links': [{'rel': 'licence',\n",
       "    'href': 'https://creativecommons.org/licenses/by/4.0/',\n",
       "    'type': 'text/html',\n",
       "    'title': 'License Link'}],\n",
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       "  'cube:dimensions': {'DATE': {'type': 'temporal',\n",
       "    'extent': ['2018-01-01T23:59:00+00:00', '2018-01-01T23:59:00+00:00']},\n",
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       "    'axis': 'x',\n",
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       "    'extent': [3.097476122153762, 17.50687107025045],\n",
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       "    'reference_system': 3035},\n",
       "   'Y': {'type': 'spatial',\n",
       "    'axis': 'y',\n",
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       "    'extent': [42.49699937418966, 50.35760819184301],\n",
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       "    'reference_system': 3035},\n",
       "   'bands': {'type': 'bands', 'values': ['CLC2018_WM']}},\n",
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       "  'summaries': {'rows': 8332, 'columns': 10240, 'gsd': [100.0]}},\n",
       " {'stac_version': '0.9.0',\n",
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       "  'stac_extensions': ['datacube'],\n",
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       "  'id': 'ADO_NDVI_MODIS_231m_3035_ODC',\n",
       "  'title': 'ADO_NDVI_MODIS_231m_3035_ODC',\n",
       "  'description': 'ADO_NDVI_MODIS_231m_3035_ODC',\n",
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       "  'deprecated': False,\n",
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       "  'providers': [{'name': 'Eurac EO ODC',\n",
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       "    'href': 'https://creativecommons.org/licenses/by/4.0/',\n",
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       "    'title': 'License link'}],\n",
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       "    'axis': 'x',\n",
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       "    'extent': [3.5446677656937715, 18.990556343370127],\n",
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       "    'reference_system': 3035},\n",
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       "    'extent': [42.28250560234868, 48.77837603135202],\n",
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       "    'reference_system': 3035},\n",
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       "   'bands': {'type': 'bands', 'values': ['QA', 'DOY', 'NDVI', 'PLATFORM']}}},\n",
       " {'stac_version': '0.9.0',\n",
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       "  'stac_extensions': ['datacube'],\n",
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       "  'id': 'Backscatter_Sentinel1_Track015',\n",
       "  'title': 'Backscatter_Sentinel1_Track015',\n",
       "  'description': 'Sentinel-1 Data',\n",
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       "  'deprecated': False,\n",
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       "  'providers': [{'name': 'Eurac EO ODC',\n",
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       "   'X': {'type': 'spatial',\n",
       "    'axis': 'x',\n",
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       "    'reference_system': 3035},\n",
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       "  'id': 'DRI2_T32TPR',\n",
       "  'title': 'DRI2_T32TPR',\n",
       "  'description': 'Sentinel-2 pre-processed data for DRI2 project.',\n",
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       "  'deprecated': False,\n",
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       "    'title': 'License link'}],\n",
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       "  'cube:dimensions': {'DATE': {'type': 'temporal',\n",
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       "    'extent': ['2015-07-04T10:10:06+00:00', '2021-11-24T10:12:39+00:00']},\n",
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       "  'id': 'DRI2_T32TPS',\n",
       "  'title': 'DRI2_T32TPS',\n",
       "  'description': 'Sentinel-2 pre-processed data for DRI2 project.',\n",
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       "  'deprecated': False,\n",
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       "    'title': 'License link'}],\n",
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       "  'id': 'DRI2_T32TPT',\n",
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       "    'type': 'text/html',\n",
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       "    'title': 'License link'}],\n",
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       "  'id': 'DRI2_T32TQS',\n",
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       "  'description': 'Sentinel-2 pre-processed data for DRI2 project.',\n",
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       "  'deprecated': False,\n",
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       "    'type': 'text/html',\n",
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       "  'id': 'DRI2_T32TQT',\n",
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       "  'id': 'LIA_Sentinel1_Track015',\n",
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       "  'deprecated': False,\n",
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       "  'id': 'Modis_snow_eurac',\n",
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       "  'deprecated': False,\n",
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       "  'id': 'S2_L1C_T32TPS',\n",
       "  'title': 'S2_L1C_T32TPS',\n",
       "  'description': 'Sentinel-2 Level 1 C data for the 32TPS tile. It includes the FMASK layer, generated using FMASK 4.3 https://github.com/GERSL/Fmask.',\n",
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       "  'deprecated': False,\n",
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       "    'type': 'text/html',\n",
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       "    'extent': [10.290484068721153, 11.755584916051987],\n",
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       "  'keywords': ['Copernicus'],\n",
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       "  'deprecated': False,\n",
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       "  'deprecated': False,\n",
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       "    'type': 'text/html',\n",
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       "    'title': 'License link'}],\n",
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       "  'cube:dimensions': {'DATE': {'type': 'temporal',\n",
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       "    'extent': ['2017-01-03T23:59:59+00:00', '2019-12-31T23:59:59+00:00']},\n",
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       "    'extent': [-7.510046482086182, -5.41987419128418],\n",
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       "   'Y': {'type': 'spatial',\n",
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       "  'stac_extensions': ['datacube'],\n",
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       "  'id': 'SAR2Cube_SInCohMap_S1_L0_168_DSC_SOUTH_TYROL',\n",
       "  'title': 'SAR2Cube_SInCohMap_S1_L0_168_DSC_SOUTH_TYROL',\n",
       "  'description': 'SAR2Cube Sentinel-1 Data Level-0',\n",
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       "  'deprecated': False,\n",
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       "    'type': 'text/html',\n",
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       "    'title': 'License link'}],\n",
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       "  'cube:dimensions': {'DATE': {'type': 'temporal',\n",
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       "    'extent': ['2016-09-12T23:59:59+00:00', '2020-11-14T23:59:59+00:00']},\n",
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       "   'X': {'type': 'spatial',\n",
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       "    'extent': [9.718852043151855, 12.394761085510254],\n",
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       "   'Y': {'type': 'spatial',\n",
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       "    'extent': [45.19416809082031, 47.370574951171875],\n",
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       "  'stac_extensions': ['datacube'],\n",
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       "  'id': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI1',\n",
       "  'title': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI1',\n",
       "  'description': 'Sentinel-1 SLC Data. SAR2Cube Level-0 preprocessing.',\n",
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       "  'deprecated': False,\n",
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       "  'license': 'CC-BY-4.0',\n",
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       "    'type': 'text/html',\n",
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       "    'title': 'License link'}],\n",
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       "  'cube:dimensions': {'DATE': {'type': 'temporal',\n",
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       "    'extent': ['2017-11-06T23:59:59+00:00', '2018-11-25T23:59:59+00:00']},\n",
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       "   'X': {'type': 'spatial',\n",
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       "    'extent': [26.32445526123047, 30.129379272460938],\n",
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       "   'Y': {'type': 'spatial',\n",
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       "    'extent': [64.42527770996094, 65.43559265136719],\n",
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       "  'stac_extensions': ['datacube'],\n",
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       "  'id': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI2',\n",
       "  'title': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI2',\n",
       "  'description': 'Sentinel-1 SLC Data. SAR2Cube Level-0 preprocessing.',\n",
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       "  'deprecated': False,\n",
       "  'license': 'CC-BY-4.0',\n",
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       "  'providers': [{'name': 'Eurac EO ODC',\n",
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       "    'href': 'https://creativecommons.org/licenses/by/4.0/',\n",
       "    'type': 'text/html',\n",
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       "    'title': 'License link'}],\n",
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       "  'cube:dimensions': {'DATE': {'type': 'temporal',\n",
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       "    'extent': ['2017-11-06T23:59:59+00:00', '2018-11-25T23:59:59+00:00']},\n",
       "   'X': {'type': 'spatial',\n",