mandatory:
    PRODUCTS_BASE_DIR: "/data/SoilMoisture/THERMOCITY"
    COL_ID: "thermocity-lst"
    COL_TITLE: "Thermocity Land Surface Temperature"
    COL_DESC: >
      ASTER and ECOSTRESS thermal data are reprocessed to provide 
      a state-of-the-art thermal data set over 5 french metropolises : 
      Marseille, Montpellier, Paris, Strasbourg and Toulouse. 
      For each metropolises, a 100km square ROI is used. The dataset extends 
      from 2003 to 2019 for ASTER and from 2018 to 2020 for ECOSTRESS. 
      For each mission and each ROI, we generally provide at least one acquisition 
      during day and night, and during winter and summer. 
      All the data have been geometrically corrected to fit 
      Copernicus Sentinel-2 mission geolocalization. They are provided 
      in the original spatial resolution (70m for ECOSTRESS and 90m for ASTER), 
      together with and enhanced resolution for ASTER when synchroneous visible data 
      are available (30m). Atmospheric corrections have been refined 
      for ASTER thanks to original atmospheric profiles from Meteo-France. 
      Finally, a dedicated TES algorithm trained on urban materials is used 
      to derive land surface temperature and emissivity.
    # Public URL to a png image symbolyzing your collection (ex: https://imgbb.com/)
    COL_QUICLOOK_URL: "https://i.ibb.co/3ybBSP7S/thermocity-512.png"
    # Filename prefix to find your products in PRODUCTS_BASE_DIR
    PROD_NAME_PREFIX: ""
    # Case sensitive raster filename extension to filter your products in PRODUCTS_BASE_DIR (tif,jpg,...)
    PROD_NAME_EXT: "zip"
    # Assets designating your product href in catalog
    ASSETS:
        - key: "LST"
          title: "LST image"
          desc: "Land Surface Temperature derived from ASTER and ECOSTRESS thermal data"
        - key: "ZIP"
          title: "Thermocity data"
          desc: "Thermocity production data"
        - key: "QL"
          title: "LST Quicklook"
          desc: "Land Surface Temperature Quicklook"  
    CONTACT:
        - name: "Vincent Lonjou"
          organization: "CNES"
        - name: "Aurelie Michel"
          organization: "ONERA"
        - name: "Laure Roupioz"
          organization: "ONERA"
    PROVIDERS:
        - name: "Space for Climate Observatory"
          url: "https://www.spaceclimateobservatory.org/fr/thermocity"
    DOI: "https://doi.org/10.60566/cckwb-hqr05"
    # use python datetime strftime regex (dt) for keys and use python re search regex (re) for value
    PROD_DATETIME_REGEX:
        - dt: "%Y-%m-%dT%H-%M-%S"
          re: '([1|2]\d{3})-((0[1-9])|(1[0-2]))-(0[1-9]|[1-2][0-9]|(3[0-1]))T(([0-1][1-9])|(2[0-3]))-([0-5][0-9])-([0-5][0-9])'
        - dt: "%Y%m%dT%H%M%S"
          re: '([1|2]\d{3})((0[1-9])|(1[0-2]))(0[1-9]|[1-2][0-9]|(3[0-1]))(([0-1][1-9])|(2[0-3]))([0-5][0-9])([0-5][0-9])'
        - dt: "%Y%m%d%H%M%S"
          re: '([1|2]\d{3})((0[1-9])|(1[0-2]))(0[1-9]|[1-2][0-9]|(3[0-1]))(([0-1][1-9])|(2[0-3]))([0-5][0-9])([0-5][0-9])'
        - dt: "%Y-%m-%d"
          re: '([1|2]\d{3})-((0[1-9])|(1[0-2]))-(0[1-9]|[1-2][0-9]|(3[0-1]))'
        - dt: "%Y%m%d"
          re: '([1|2]\d{3})((0[1-9])|(1[0-2]))(0[1-9]|[1-2][0-9]|(3[0-1]))'
optional:
    # Delete unnecessary key/value to fit your product architecture
    ASSET_NODATA: -32768
    