I want to downscale time-series imagery data using precipitation and evapotranspiration (temporal) and possibly even topography (static) using Google Earth Engine (GEE) Random Forest Regression. I have processed the remote sensing products to the same temporal and spatial resolution and joined them.

Typically the code would be something like:

// Create a classifier

var classifier = ee.Classifier.smileRandomForest().setOutputModel('REGRESSION').train({

features:training_data,

classProperty: 'what_I_want_to_predict',

inputProperties: ['predictor_variables']

});

var classified = predictor_variables_data.classify(classifier);

My question is:

1. How do I include temporal and static data as predictor variables (training data)? How do I sample it?

2. How does one apply the RDR model across monthly images over 2 years for example? Do you run the model 24 respective times?

Regards,

Cindy

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