статья в журнале

En

IEEE Transactions on Geoscience and Remote Sensing

ISSN:01962892

2025

Fan, S.²

Zhang, B.²

10.1109/TGRS.2025.3635162

Geophysical Doppler shifts measured by synthetic aperture radar (SAR) are influenced by the combination of ocean surface winds, waves, and currents. Accurate retrieval of radial surface current velocities, therefore, requires precise estimation and removal of the wave-induced Doppler contributions. To address this challenge, we developed WaveDop, a hybrid physics-guided and data-driven model based on the extreme gradient boosting (XGBoost) machine learning algorithm, designed to estimate Doppler shift contributions arising from wind and wave effects. The model was trained on a comprehensive dataset comprising Sentinel-1 SAR Doppler measurements acquired over global coastal regions from January 2015 to October 2022, in conjunction with WAVEWATCH III (WW3) wave model outputs and drifter-observed surface currents. Our analysis reveals that wave-induced Doppler shifts are strongly modulated by the radar incidence angle, surface wind fields, and wave characteristics, with both wind waves and swell making significant contributions. By accounting for these wave effects and correcting for nongeophysical artifacts such as antenna electronic mis-pointing, satellite attitude variations, and azimuthal scalloping, we derive radial current velocities from SAR observations. Validation against independent drifter measurements indicates strong agreement, with a correlation coefficient of 0.82, negligible bias, and a root-mean-square error (RMSE) of 0.16 m/s. These results demonstrate that WaveDop effectively estimates wave-induced Doppler shifts and enables reliable retrieval of ocean surface current (OSC) velocities from SAR data.

Doppler shift, ocean surface current (OSC) , synthetic aperture radar (SAR) , WaveDop

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