mandatory:
    PRODUCTS_BASE_DIR: "/mnt/GEOSUD_WA/SoilMoisture/SurfWater"
    COL_ID: "get-omp-surfwater"
    COL_TITLE: "Monthly and yearly synthesis of Water Surface occurence"
    COL_DESC: >
      SURFWATER data collection contains yearly and monthly water occurrence 
      maps (ratio of water detections over clear satellite observations during 
      a year/month period) on a 10m resolution grid. Such maps are extracted 
      from optical (Sentinel-2 L2 from MAJA) and radar (Sentinel-1 GRD 
      processed by S1Tiling) datasets. Supervised algorithms guided by the 
      Global Surface Water reference are used to classify water/land scenes 
      for each optical/radar observation. Based on MERIT DEM information, 
      Height Above Neirest Drainage maps (HAND) are used to remove false 
      positives. Based on the validation scheme on [Pena Luque, 2021], 
      single-observation water maps have a median F1 score of 0.89 
      (precision= 0.9, recall : 0.9) evaluated on 26 labeled scenes. This 
      dataset is a demo production for France metropolitan territory on 2019. 
      THEIA/hydroweb.next plans to produce daily SURFWATER maps along 2022.
      [Pena Luque, 2021] https://www.mdpi.com/2072-4292/13/16/3279, 
      Validatin dataset https://zenodo.org/record/4657020
    # Public URL to a png image symbolyzing your collection (ex: https://imgbb.com/)
    COL_QUICLOOK_URL: "https://i.ibb.co/PvLy13Hr/surfwater-512.png"
    # Filename prefix to find your products in PRODUCTS_BASE_DIR
    PROD_NAME_PREFIX: "SW_"
    # Case sensitive raster filename extension to filter your products in PRODUCTS_BASE_DIR (tif,jpg,...)
    PROD_NAME_EXT: "TIF"
    # Assets designating your product href in catalog
    ASSETS:
        - key: "SW"
          title: "Water occurrence map"
          desc: "Water occurrence map derived from Sentinel-1 and Sentinel-2."
        - key: "QL"
          title: "Water occurrence map Quicklook"
          desc: "Water occurrence map Quicklook"  
    CONTACT:
        - name: "Santiago Pena Luque"
          organization: "CNES"
    PROVIDERS:
        - name: "Geosciences Environnement Toulouse - OSU OMP"
          url: "https://www.get.omp.eu/"
    DOI: "https://doi.org/10.3390/rs13163279"
    # 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]))T(([0-1][0-9])|(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: 255
    # ASSET_SCALES:
    #     - band_index: 1
    #       scale: 1.0
    #     - band_index: 2
    #       scale: 1.0
    #     - band_index: 3
    #       scale: 1.0
    # ASSET_OFFSETS:
    #     - band_index: 1
    #       offset: 1.0
    #     - band_index: 2
    #       offset: 1.0
    #     - band_index: 3
    #       offset: 1.0
    # ASSET_NODATA: 0.0
    
