Machine Learning Algorithms Assessment for Snow LWC Retrieval from SAR Data

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X- band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as 'model-driven'. The second one was defined as 'data-driven'. The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven"RANN = 0.60, RRF = 0.68; 'data-driven' RANN = 0.50, RRF = 0.88 at X-band). Moreover, the RF implementation trained with the 'data-driven' approach outperformed the 'model-driven' approach in terms of correlation coefficient R (RRF = 0.88 at X-band).