雷达学报2026，Vol.15Issue(2)：759-778,20.DOI:10.12000/JR25099
基于机器学习的Sentinel-1 SAR多普勒频移风、浪响应建模与海流反演
Machine Learning-based Modeling of Wind and Wave Responses in Sentinel-1 SAR Doppler Shifts for Ocean Current Retrieval
摘要
Abstract
Ocean currents play a critical role in global climate regulation.Synthetic Aperture Radar(SAR)provides high-resolution observational support for ocean current detection by measuring Doppler shifts;however,SAR Doppler shifts contain multiple contributing components.To accurately retrieve ocean currents from these data,nongeophysical contributions must be precisely corrected,and wind-and wave-induced Doppler shifts must be accurately estimated.This paper proposes a machine learning-based method for modeling such shifts and retrieving ocean currents from Sentinel-1 SAR data.First,nongeophysical contributions in the SAR Doppler shift are precisely corrected to remove the effects unrelated to ocean motion.Second,BackproPagation Neural Network(BPNN)and eXtreme Gradient Boosting(XGBoost)models,optimized using the particle swarm optimization algorithm,are developed to describe the nonlinear relationship between the wind-wave Doppler shift and sea-surface wind-wave parameters derived from SAR data.Finally,the corrected Doppler shift is utilized to retrieve ocean surface current velocities.This paper comparatively evaluates the estimation accuracies of the wind-wave Doppler shifts obtained using the BPNN and XGBoost models,as well as the respective influence of each model's performance on the effectiveness of ocean current retrieval.Results indicate that the XGBoost model achieves superior estimation accuracy compared with the BPNN model.The Root Mean Square Error(RMSE)of the Doppler shift estimated by the XGBoost model is approximately 4.043 Hz,which is 2.898 Hz lower than that of the BPNN model.Compared with those of the HYCOM current data,the RMSE of the currents retrieved by the XGBoost model is about 0.202 m/s;this value is reduced by 0.122 m/s compared with that of the BPNN model.Validations against the current velocities detected by HF radar show that the RMSE of currents retrieved by the XGBoost model is 0.21 m/s,representing a 16%reduction compared with that of the BPNN model.These findings indicate that the proposed technical approach for ocean current retrieval using spaceborne SAR is highly accurate.关键词
合成孔径雷达/多普勒频移/海流反演/风-浪多普勒贡献/机器学习Key words
Synthetic Aperture Radar(SAR)/Doppler shift/Ocean current retrieval/Wind-wave Doppler contribution/Machine learning分类
信息技术与安全科学引用本文复制引用
车佳恒,闫秋双,范陈清,孟俊敏,张杰..基于机器学习的Sentinel-1 SAR多普勒频移风、浪响应建模与海流反演[J].雷达学报,2026,15(2):759-778,20.基金项目
国家自然科学基金(42206178)The National Natural Science Foundation of China(42206178) （42206178）
