The conventional gravity-geologic method (GGM), as a widely used method for bathymetric prediction, is a single-point calculation method between the gravity anomaly and unknown bathymetry. To enhance the accuracy of bathymetric prediction, the improved GGM (IGGM) reliably removing the long-wavelength gravity effect of regional seafloor topography was proposed. The modeling of the long-wavelength gravity field was refined by calculating the short-wavelength gravity correction at the control points based on the weight parameters introduced into the Bouguer slab formulation. The IGGM bathymetric model for the experimental area (113°E-119°E, 12°N-19°N) in the South China Sea was constructed by combining shipborne bathymetry data from the National Centers for Environmental Information (NCEI) and the V31.1 gravity anomaly model from the Scripps Institution of Oceanography (SIO). The standard deviation of the difference between the IGGM model and shipborne bathymetry was approximately 103.46 m at iteration points, which was better than the DTU18, GEBCO_2021 and topo_23.1 bathymetric models. Compared with the accuracy of the model derived from the GGM, the accuracy of the model from IGGM was improved by about 27%. At checkpoints, the accuracy of IGGM was improved by approximately 17 m, and the improvement was larger in areas with complex terrain.

The gravity–geologic method (GGM) is widely used for bathymetric predictions. However, the conventional GGM cannot be applied in regions without actual bathymetric data. The modified gravity–geologic method (MGGM) enhances the accuracy of bathymetric models by supplementing short-wavelength gravity anomalies with an a priori bathymetric model, but it overlooks the significance of actual bathymetric data in the prediction process. In this study, we used the BP neural network (BPNN), incorporating shipborne depth soundings and coastline data as zero-depth estimates combined with the MGGM to produce a bathymetric model (BPGGM_BAT) for the South China Sea (105°E–122°E, 0°N–26°N). The results indicate that the BPGGM_BAT model decreases the root-mean-square (RMS) of bathymetry differences from 154.33 m to approximately 140.43 m relative to multibeam depth data. Additionally, the RMS differences between the BPGGM_BAT model and multibeam depth data show further improvements of 19.63%, 20.10%, and 19.54% when compared with the recently released SRTM15_V2.6, GEBCO_2022, and topo_V27.1 models, respectively. The precision of the BPGGM_BAT model is comparable to that of the SDUST2023BCO model, as verified using multibeam depth data in open sea regions. The BPGGM_BAT model outperforms existing models with RMS differences of 8.54% to 32.66%, as verified using Electronic Navigational Chart (ENC) bathymetric data in the regions around the Zhongsha and Nansha Islands. A power density analysis suggests that the BPGGM_BAT model is superior to the MGGM_BAT model for predicting seafloor topography within wavelengths shorter than 15 km, and its performance is closely consistent with that of the topo_V27.1 and SDUST2023BCO models. Overall, this integrated method demonstrates significant potential for improving the accuracy of bathymetric predictions.

The Surface Water and Ocean Topography (SWOT) mission, initially designed to observe oceans and inland waters, proves valuable for mapping intertidal flat topography. This study presents a fusion method that integrates freely available SWOT interferometric altimetry data with Sentinel‐2 imagery. The density‐based spatial clustering of applications with noise algorithm is employed to accurately identify observation data located in the intertidal zone. Intertidal topography with a 10 m resolution were generated from L2_HR_PIXC data collected during SWOT's science phase (July 2023–October 2024) in the intertidal region along the coast of Jiangsu Province, China. These Digital Elevation Models were compared with laser altimetry observations from the ICESat‐2. SWOT observations show high accuracy, with RMSEs of 0.24 m (multi‐cycle) and 0.41 m (single‐cycle), confirming their potential for intertidal monitoring. This study presents a data processing strategy that does not rely on ground‐based observations, demonstrating potential for application in a broader range of regions.

Accurate seabed topography is crucial for marine research, resource exploration, and engineering applications. While deep learning techniques have been widely applied in seabed inversion, existing methods often overlook the multi-scale influence of gravity anomalies, particularly the critical role of short-wavelength gravity anomalies in resolving fine-scale bathymetric features. In this study, we propose a novel Fully Connected Deep Neural Network (FCDNN) approach that systematically integrates long-wavelength, short-wavelength, and residual gravity anomaly components for seabed topography inversion. Using multi-satellite altimetry-derived gravity anomaly data (SIO V32.1) and shipborne bathymetric data (NCEI), we constructed a high-resolution (1′ × 1′) seabed topography model for the South China Sea (108°E–121°E, 6°N–23°N), termed FCD_Depth_SCS. The workflow included multi-scale decomposition of gravity anomalies, linear regression-based residual calculation, and FCDNN-based nonlinear training to capture the complex relationships between gravity anomalies and water depth. The FCD_Depth_SCS model achieved a difference standard deviation (STD) of 44.755 m and a mean absolute percentage error (MAPE) of 2.903% when validated against 160,476 shipborne control points. This performance significantly outperformed existing models, including GEBCO_2024, SIOv25.1, DTU18, and GGM_Depth (derived from the Gravity–Geologic Method), whose STDs were 82.234 m, 108.241 m, 186.967 m, and 58.874 m, respectively. Notably, the inclusion of short-wavelength gravity anomalies enabled the model to capture fine-scale bathymetric variations, particularly in open-sea regions. However, challenges remain near coastlines and complex terrains, highlighting the need for further model partitioning to address localized nonlinearity. This study highlights the benefits of integrating multi-scale gravity anomaly data with a fully connected deep neural network. Employing this innovative and robust approach enables high-resolution inversion of seabed topography with enhanced precision. The proposed method provides significant advancements in accuracy and resolution, contributing valuable insights for marine environmental research, resource management, and oceanographic studies.

We assess the accuracy of the Surface Water and Ocean Topography (SWOT) mission for deriving marine gravity anomalies and bathymetry in the Philippine Sea using wide-swath L3 Ka-band radar interferometer (KaRIn) data. Our evaluation includes determining the deflection of vertical (DOV) using the least squares collocation (LSC) method, recovering gravity anomalies with the inverse Vening-Meinesz (IVM) formula, and predicting bathymetry through the gravity-geological method (GGM). The accuracy of the east-west DOV component has improved but remains lower than that of the north-south component. SWOT-derived (from 14-cycle data) gravity anomalies exhibit stronger signals in the short-wave ranges (< 20 km), with a precision of 2.37 mGal compared to shipborne measurements. The SWOT-derived bathymetry shows an accuracy of 124.02 m, representing an 8.36 m improvement compared to the conventional SIO altimeter-based model. We show that 14 cycles (10 months) of SWOT data provide more detailed information than 30 years of traditional nadir altimetry in both gravity anomaly and bathymetry. We have optimized the SWOT data processing strategy to enable the retrieval of highquality marine gravity anomalies. With advancements in data processing and the accumulation of observations, the SWOT mission is progressing toward its marine geophysics objectives.

The Surface Water and Ocean Topography (SWOT) mission provides novel perspectives. In this study, a new global bathymetric product, SYSU_Topo, is developed using gravity anomalies (the SWOT_02 model released by Scripps Institution of Oceanography) and the high-precision gravity-geological method (GGM). The data (NetCDF format; global range from 80°S to 80°N, 1-arc-minute resolution; variables: lat, lon, z) and processing codes are openly available for immediate reuse in ocean modeling, geophysics, and seafloor mapping. To reliably obtain the optimal density contrast for GGM, a sliding-window strategy of partition inversion was adopted, and a fusion method with boundary-constraint points is developed to effectively eliminate the splicing effect of partition inversion. The model has been reliably validated with 11,167,583 single-beam bathymetric points and newly added multibeam grid points from GEBCO_2024. The SYSU_Topo model achieves superior performance in the South China Sea, with a standard deviation of 132.07 m, which is 8%–26% better than other models. Compared to traditional altimeter-derived gravity anomalies, SWOT data exhibits greater potential in filling regions lacking high-precision bathymetry.