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
If I understand correctly, I think a way you could do this is by using a reducer through your timeline.
For those following this question - the solution was:
1 joining time series data
2. concatenating the stationary data.
// JOIN - Combine/join image collections for training
var joinA = GW_anomaly2.select('GW_anomaly');
var joinB = CHIRPS_resampleD.select('precipitation');
var joinC = MODIS_resampleD.select('ET','PET');
var filter2 = ee.Filter.equals({
leftField: 'system:time_start',
rightField: 'system:time_start'
});
var simpleJoinB = ee.Join.inner();
var innerJoinB = ee.ImageCollection(simpleJoinB.apply(joinA, joinB, filter2));
var joinedB = innerJoinB.map(function(feature) {
return ee.Image.cat(feature.get('primary'),feature.get('secondary'));
});
var simpleJoinC = ee.Join.inner();
var innerJoinC = ee.ImageCollection(simpleJoinC.apply(joinedB, joinC, filter2));
var joinedC = innerJoinC.map(function(feature) {
return ee.Image.cat(feature.get('primary'),feature.get('secondary'));
});
// Concatenate (combine into single collection with multiple bands) the given long term mean (single band)
function add_topo(image){
var concatenate2 = ee.Image.cat([DEM_resampleD]);
return image.addBands(concatenate2);
}
//Map function over entire time-series collection
var combined_input = joinedC.map(add_topo);
To include spatial and temporal data in a GEE regression model, you can use the following steps:
Here is an example of how to include spatial and temporal data in a GEE regression model:
library(geepack)
# Create the spatial dataset
coordinates
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