The generation of shoreward nonlinear internal waves (NLIWs) on the continental shelf south of the Hainan Island (SHI) is investigated based on spaceborne synthetic aperture radar (SAR) observations and numerical simulations. Two types of shoreward NLIWs are identified from SAR images according to their distinct geographic distribution. One type of NLIWs, named Type‐N NLIWs, is distributed in the northern part of SHI, and the other one is named Type‐S NLIWs, distributed in the southern part of SHI. The SAR‐observed wave occurrence frequency during the spring and neap tides, combined with the calculated body force, suggests that the Type‐N NLIWs originate from the Xisha Islands, whereas the Type‐S NLIWs originate from both the Xisha Islands and the continental shelf break, and the shelf break has a larger contribution. The synergistic analyses of the internal tidal raypath, gamma parameter and earliest SAR‐observed NLIWs suggest that the Type‐N NLIWs are caused by the impingement of the diurnal internal tidal beams emanating from the Xisha Islands on the near‐surface pycnocline close to the continental shelf. Based on the realistic shelf‐slope topography and tidal forcing, the two‐dimensional numerical simulations using the MITgcm suggest that the Type‐S NLIWs result from the nonlinear disintegration of a mode‐1 diurnal internal tide, which develops from a depression formed at the continental shelf break. Furthermore, numerical experiments show that the background current can greatly affect the nonlinear evolution of the internal waves generated at the shelf break.

The accurate estimation of the upper layer thickness in a two-layer ocean is a crucial step in the retrieval of internal solitary wave (ISW) amplitude from synthetic aperture radar (SAR) data. In this paper, we present a method to derive the upper layer thickness and the consequent ISW amplitude by combining two consecutive SAR images with the extended Korteweg-de Vries (eKdV) equation. An ISW case observed twice by the Chinese C-band SAR GaoFen-3 (GF-3) and the German X-band SAR TerraSAR-X (TS-X) with a temporal interval of approximately 11 min in shallow water to the southeast of Hainan Island in the northwestern South China Sea was used to demonstrate the applicability of the method. Using the in situ measurements of temperature and salinity near the observed ISW, the proposed method yielded an ISW amplitude of −4.52 m, in close proximity to −5.66 ± 1.24 m derived by applying the classic Korteweg–de Vries (KdV) equation based on the continuously stratified theory. Moreover, the climatological dataset of the World Ocean Atlas 2013 (WOA13) was also used with the proposed method in the Hainan case, and the results showed that the method can still provide a reasonable estimate of ISW amplitude in shallow water even when in situ oceanic stratification measurements are absent. The application of our method to derive the ISW amplitude from consecutive SAR images seems highly promising with the increasing emergence of tandem satellites in orbits.

The distribution and source sites of nonlinear internal waves (NLIWs) northeast of Hainan Island were investigated using satellite observations and a wavefront propagation model. Satellite observations show two types of NLIWs (here referred to as type-S and type-D waves). The type-S waves are spaced at a semidiurnal tidal period and the type-D waves are spaced at a diurnal tidal period. The spatial distribution of the two types of NLIWs displays a sandwich structure in which the middle region is influenced by both types of NLIWs, and the northern and southern regions are governed by the type-S and type-D waves, respectively. Solving the wavefront model yields good agreement between simulated and observed wavefronts from the Luzon Strait to Hainan Island. We conclude that the NLIWs originate from the Luzon Strait.

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies.