DOWNSCALING AND EVALUATION OF EVAPOTRANSPIRATION USING REMOTELY SENSED DATA AND MACHINE LEARNING ALGORITHMS (STUDY AREA: MOGHAN PLAIN, IRAN)

Abstract

Water balance estimation in arid and semi-arid areas is highly essential for water and irrigation management. In arid regions, around ninety percent of the rainfall that reaches the surface of earth is returned to the atmosphere by evaporation and transpiration process. Evapotranspiration (ET) estimation has been drastically improved by the help of cutting-edge technology of Remote Sensing (RS) and Machine Learning (ML) techniques. Satellite RS approaches can be advantageous in monitoring land surface processes over vast areas and different approaches have been advanced for assessing ET from moderate to low resolutions with the help of remotely-sensed data. This research demonstrated a MODIS 8-day (500m) ET downscaling technique in Moghan plain based on Landsat-8 indices (30m) and Random Forest Regression (RFR), Support Vector Regression (SVR) models. The outcome of this research showed that SVR outperformed RFR for both days. In SVR model, the accuracy assessment indices on the first and second days are respectively: RMSE= 9.28 and 8.65, rRSME= 27.85 and 63.71, MAE= 5.71 and 3.97. This study has illustrated the possibility of implementing ML methods for downscaling MODIS ET product considering their efficacy and relatively ease of execution. Nevertheless, our research has identified that the MODIS ET accuracy is the primary reason of the accuracy of the downscaled ET. Future research can investigate the utility of spatial-temporal fusion models with remotely-sensed data to ultimately improve the spatio-temporal resolution of downscaled ET maps.