Improved Tropical Cyclone Wind Estimation Using Dual‐Polarization C‐Band SAR and Machine Learning

Abstract Tropical cyclones (TCs) characterized by extreme wind speeds present severe hazards to human life and infrastructure. Spaceborne synthetic aperture radar (SAR) has emerged as a critical observational tool for TCs, owing to its all‐weather, high‐resolution imaging capabilities. Nevertheless, retrieving sea surface winds from satellite SAR remains challenging, as radar backscatter is modulated not only by local wind dynamics but also by non‐Bragg scattering mechanisms. Heavy precipitation within TCs further exacerbates errors in normalized radar cross section measurements. In this study, multi‐scene Sentinel‐1 SAR observations from multiple TCs during 2016–2020 were collected and collocated with stepped‐frequency microwave radiometer measurements to construct a dual‐polarization scattering feature data set. To address class imbalance, resampling was applied exclusively within the training data of each cross‐validation fold. Feature selection was conducted using correlation analysis combined with maximum relevance–minimum redundancy, also implemented in a storm‐independent manner. Three machine learning models were trained for comparison using a random 7:3 data split, among which the XGBoost model exhibited superior performance and was designated as the C‐band dual‐polarization wind speed model (CDP_WS1). It achieved a root mean square error (RMSE) of 2.65 m/s and 3.44 m/s on the training and test sets, respectively, maintaining stable performance across wind speeds up to 70 m/s. Furthermore, to rigorously assess storm‐independent generalization, a Leave‐One‐Storm‐Out (LOSO) cross‐validation was conducted across 14 TCs, yielding a RMSE of 4.10 m/s. Independent validation on two representative hurricanes further confirmed its robustness and accuracy.