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: https://www.planet.com/nicfi/#sign-up 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:

• https://www.planet.com/nicfi/
• Planet Developer's note on NICFI

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

Resolution
4.77 meters

Bands:

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
   ee.Initialize()

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: https://www.planet.com/nicfi/
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)
   
       return(image)

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(ee.Filter.date('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")
   
   map1.addLayerControl()
   map1

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.