Retrieval of Rainfall Information by Spaceborne C-Band Sar Based on Machine Learning

This study developed a machine-learning-based model to extract the rainfall information from C-band synthetic aperture radar (SAR) images acquired in dual-polarization (VV/VH) over hurricane conditions. The model is based on a back-propagation neural network (BPNN) tuned by 1,1762 pairs of samples from collocations of Sentinel-1 data and Stepped Frequency Microwave Radiometer (SFMR) measurements. The model inputs include four SAR measured physical parameters and one morphological feature related to the hurricane. Several comparative tests show that the selection of different SAR inputs has a significant impact on the performance of the models for rainfall estimation. For example, compared to the SFMR measurements, the root mean square error and correlation coefficient of rain rate obtained by BPNN with all five inputs reach the best, which are 3.61 mm/hr and 0.87, respectively.