Pixel-wise regression between two raster time series (e.g. NDVI and rainfall)

Doing a pixel-wise regression between two raster time series can be useful for several reasons, for example: find the relation between vegetation and rainfall for each pixel, e.g. a low correlation could be a sign of degradation derive regression coefficients to model the depending variable using the independend variable (e.g. model NDVI with rainfall data) … Continue reading Pixel-wise regression between two raster time series (e.g. NDVI and rainfall)

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