Coastal bathymetric information is essential for marine engineering, navigation safety, and coastal management. Coastal bathymetric inversion based on wave characteristics extracted from synthetic aperture radar (SAR) images is a widely used technique. Existing studies that apply this method have mostly focused on long-period wave-dominated areas. However, in short-period wave-dominated areas, this method suffers from large errors and poor stability, limiting its practical application. To address this problem, this study proposes a hierarchical fusion method for SAR-based coastal bathymetric inversion in short-period wave-dominated areas. The proposed method significantly reduces bathymetric inversion errors by hierarchically fusing multi-angle wavelength estimation results and multi-temporal SAR bathymetry inversion results, thereby achieving more robust underwater topography mapping. A validation experiment was conducted on the Wengtian coast, Hainan Island. Compared with the best single-temporal inversion results, the proposed method reduces the MAE by 19.5% and the RMSE by 23.5%. Finally, the effects of the dispersion relation and the selection of the fusion strategy on the bathymetric inversion results are discussed. These results show that the proposed method significantly improves bathymetric inversion accuracy in short-period wave-dominated areas.

Synthetic aperture radar (SAR) provides high-resolution observations of radial sea surface currents. By combining radial currents observed from different viewing directions, the trajectory-crossing method can reconstruct the sea surface current vector field. In practice, observations from ascending and descending passes are acquired at different times, so the spatial structure of the surface current field may evolve between acquisitions. Directly combining multitemporal observations can therefore introduce spatial mismatch and reduce reconstruction accuracy. This study proposes a trajectory-crossing method for reconstructing sea surface current vectors from multitemporal Sentinel-1 Doppler observations. Maximum cross-correlation (MCC) is applied to gradient images of the radial current fields to estimate displacement and spatially register observations acquired at different times before vector reconstruction. The method is evaluated using Sentinel-1 data acquired over the Gulf Stream region and compared with geostrophic currents from the Copernicus Marine Environment Monitoring Service (CMEMS), Surface Water and Ocean Topography (SWOT) observations, and Global Drifter Program (GDP) drifter measurements. Results show that the proposed method reduces mismatch effects and improves the accuracy and stability of sea surface current vector reconstruction. It provides a practical approach for deriving surface current vector fields from multitemporal SAR Doppler observations.