Analysis of wave breaking on synthetic aperture radar at C-band during tropical cyclones

The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution sigma(wb) caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) sigma(0) minus the Bragg resonant roughness sigma(br) without the distortion of rain cells during TCs. The sigma(br) is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (sigma(wb)/sigma(0)) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution sigma(wb) during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95dB and the coefficient (COR) is 0.86, which is better than a 2.83dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.