Yihong Chen

Significant wave height (SWH) is a fundamental parameter for ocean engineering, maritime safety, and climate studies. Although spaceborne synthetic aperture radar (SAR) wave mode observations provide global, all-weather coverage, accurate SWH retrieval remains challenging due to the complex ocean-to-SAR imaging mechanism. This paper presents an explainable machine learning algorithm for retrieving SWH from Sentinel-1 SAR wave mode data. To address the scarcity of high-quality collocations and the black-box nature of existing deep-learning approaches, we first correct ERA5 SWH against high-quality Spotter drifting in situ using quantile mapping (QM). The bias-corrected ERA5 data are then collocated with Sentinel-1 SAR to generate a large-scale, globally distributed, and Spotter-calibrated training dataset. Here, QM maps each quantile of the ERA5 data distribution onto the corresponding quantile of the collocated Spotter distribution, thereby adjusting the statistical distribution of the reanalysis. An eXtreme gradient boosting regression model is then trained on 32 physically interpretable features extracted from Sentinel-1 Level-2 products, including image statistics, spectral parameters, azimuth cutoff wavelength, normalized radar cross section, and wind-related variables. Comprehensive temporal-independent validations against Spotter buoys and multi-mission altimeter data throughout one year of 2021 (excluded both from the QM correction and from model training) demonstrate high accuracy, with root-mean-square errors of 0.36 m (WV1) and 0.45 (WV2) for SAR–Spotter matchups over the range 0.5–10.0 m. In addition, SHapley Additive exPlanation (SHAP) analysis is employed to quantify feature contributions and enhance model transparency. Furthermore, a compact 12-feature model selected via SHAP-based hierarchical clustering retains a nearly equivalent performance while improving interpretability. The proposed approach offers a reliable and transparent solution for global SAR-based SWH monitoring.

A synthetic aperture radar (SAR) provides vital global observations of ocean waves. However, the quasi-linear inversion algorithm routinely used for Sentinel-1 Level-2 Ocean Swell Wave (OSW) products suffers from inherent nonlinear imaging limitations. These include severe distortions and the inability to resolve wind-sea components under a strong azimuth cut-off effect. To address these challenges, this paper proposes a novel prior-knowledge-integrated machine learning framework to reconstruct complete and accurate directional wave spectra from Sentinel-1A SAR wave mode data. First, an extreme gradient boosting model is trained to accurately estimate wind-sea heights, which are then used to construct a theoretical JONSWAP prior spectrum. Subsequently, a U-Net architecture seamlessly integrates this physical prior knowledge with the official OSW swell spectra baseline. Independent validation demonstrates that the proposed framework significantly increases the spectral similarity against ERA5 reanalysis compared to the standard OSW. Furthermore, the derived parameters of total significant wave height, mean wave period, and mean wave direction exhibit remarkable improvements, with root mean square errors of 0.4026 m, 0.4342 s and 20.42°, respectively. The enhancement of SAR inferred two-dimensional wave spectra is also examined and discussed by three typical case studies. It is indicated that integrating physical wave knowledge with machine learning robustly mitigates the non-linear limitations of SAR imaging, providing highly reliable directional wave spectra for global ocean monitoring and forecasting.