Accessing the Planet's imagery (NICFI's) for analysis from Google Earth Engine (GEE)

Accessing the Planet's imagery (NICFI's) for analysis from Google Earth Engine (GEE)

Users can now access Planet's high-resolution, analysis-ready mosaics of the world's tropics through Norway's International Climate & Forests Initiative (NICFI) with an objective to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development for non-commercial purposes.

This program is launched in partnership with Norway's International Climate and Forest Initiative (NICFI), Kongsberg Satellite Services (KSAT), and Planet

Please, go to this link: for signing up. You have to fillup the form, and it will guide you on how to proceed with accessing data. For details and accessing the data, please do visit:

Before we move on, we need details of resolution and band information of the planet image. Here's the detail:

4.77 meters


Name Min Max Scale Description
B 0 10000 0.0001


G 0 10000 0.0001


R 0 10000 0.0001


NIR 0 10000 0.0001



Now I will move on to sample code with python, demonstrating how we can use it in the Google Earth Engine (GEE) platform.

  1. Import Earth Engine module for python API

    import geemap, ee
    # For authentication, Please load this
    # first time load will suffice
    # ee.Authenticate()
    # Initialize the GEE Api with already verified credentials. 
    # This works only if you have authenticated the GEE initally
  2. Define area of interest
    # get our Nepal boundary
    aoi = ee.FeatureCollection("FAO/GAUL/2015/level0").filter(ee.Filter.eq('ADM0_NAME','Nepal')).geometry()
  3. Planet & NICFI Basemaps for Tropical Forest Monitoring - Tropical Asia
    Earth Engine Data CatLog
    nicfi = ee.ImageCollection('projects/planet-nicfi/assets/basemaps/asia')
    Note: You must have access to this data from planet portal, else you will recieve an error. You can sign up and get details from this portal:
  4. I have added Sample Vegetation indices for testing:
    def getNDVI(image):
        # Normalized difference vegetation index (NDVI)
        ndvi = image.normalizedDifference(['N','R']).rename("NDVI")
        image = image.addBands(ndvi)
  5. Filter image for desired dates and apply map function
    # Filter basemaps by date and get the first image from filtered results
    basemap= nicfi.filter('2021-02-01','2021-07-01')).map(getNDVI).first()
  6. Visualization of planet imageries
    color = ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
                   '74A901', '66A000', '529400', '3E8601', '207401', '056201',
                   '004C00', '023B01', '012E01', '011D01', '011301']
    pallete = {"min":0, "max":1, 'palette':color}
    # initialize our map
    map1 = geemap.Map()
    map1.centerObject(aoi, 8)
    map1.addLayer(basemap.clip(aoi), {"bands":["R","G","B"],"min":64,"max":5454,"gamma":1.8}, "mosiac-false-color-planet")
    map1.addLayer(basemap.clip(aoi), {"bands":["N","R","G"],"min":64,"max":5454,"gamma":1.8}, "mosiac-planet")
    map1.addLayer(basemap.clip(aoi).select('NDVI'), pallete, "NDVI")

True color planet imaged visualized from GEE.

False color composite of planet image on GEE

NDVI image for planet image on GEE


Source code: github_link

Please let us know your thoughts on this with following comment section we will definetly reach out to you on your query and feedback.






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