How do you decide what interpolation method to use for resampling raster data?
I want to interpolate a certiain target variable over a large territory. I trained a random forest using a sample of points on that territory where the target is known (I have many regressor layers / variables). The information of these regressors is available over the whole territory (thus the capability to interpolate the target). The thing is that the regressor layers are available in different resolutions, I used resample to force them to the same resolution (1km^2). Overall the model works rather well (This on a 20% test set). And the output map looks good. But I wanted to know if the different techiniques available for doing resampling have anything to do with the quality of the final output map? What are the pros and cons of the different resampling techiniques available in ArcGis (Nearest neighbor assignment, Bilinear interpolation, Cubic convolution). I'm only referring to continuous variables.
From ArcGis Help: Bilinear interpolation or cubic convolution should not be used on categorical data since the categories will not be maintained in the output raster dataset. However, all three techniques can be applied to continuous data, with nearest neighbor producing a blocky output, bilinear interpolation producing smoother results, and cubic convolution producing the sharpest.