Machine Learning-Based Sea Surface Wind Speed Retrieval from Dual-Polarized Sentinel-1 SAR During Tropical Cyclones

Highlights: What are the main findings? Machine learning models for TC wind speed retrieval are proposed using dual-polarized S-1 SAR data after noise removal, which can reduce the impact of additive and multiplicative noise on cross-polarized data. The variable of SST was introduced in the proposed machine learning model for C-band SAR data and improved wind speed inversion results under TC conditions. What are the implications of the main findings? The approach of fusing advanced signal processing (noise removal) with machine learning models that incorporate relevant geophysical variables can be extended to other satellite sensors and to retrieving other oceanic or atmospheric parameters. SST is a critical physical variable in the TC wind retrieval process that has been previously underutilized or overlooked in C-band SAR models. Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances.