Machine Learning-Based Algorithm for 1-D Wave Spectrum Retrieval From SAR Imagery as Passing Oceanic Eddy

The inversion of the 1-D wave spectrum from dual-polarized synthetic aperture radar (SAR) data is performed using machine learning methods, namely, random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector regression (SVR), and convolutional neural networks (CNNs). This process incorporates an improved hydrodynamic modulation transfer function (MTF), calibrated with more than 8000 Sentinel-1 (S-1) Ground Range Detected (GRD) images from 2021 to 2024, to account for shear currents induced by oceanic eddies. This study primarily aims to identify the differences between algorithms for 1-D wave spectrum retrieval from S-1 SAR image as passing oceanic eddy and examine the characteristics of sea surface waves change observed by SAR passing through oceanic eddies. SAR retrievals are compared with significant wave heights (SWHs) from Haiyang-2 (HY-2) altimeter and wave spectrum from the Surface Wave Investigation and Monitoring (SWIM) and National Data Buoy Center (NDBC) buoys. The XGBoost model is established as superior for retrieving 1-D wave spectra and SWH, recording the lowest root mean squared error (RMSE) (0.33 m versus SWIM, 0.30 m versus HY-2, and 0.68 m versus Buoy) and highest correlation in validation. Applied to a North Atlantic case study, it effectively derives sea states from the S-1 image. This high-resolution analysis reveals that oceanic eddies significantly amplify wave energy, resulting in elevated SWH inside their cores. While model accuracy declines in high kinetic energy eddies, the SAR-derived retrievals maintain reliability across eddy-affected regions.