A process for Earth observation and analysis by pre-processing remote sensing images which may be from sources including MODIS, Proba-V, Landsat and/or Sentinel or any other space-borne or airborne sensor. Filtering the images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process. Applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process. Applying the output from machine learning to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring. Generating calibrated output images corresponding to the specific regions defining the Areas of Interest.