Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.

We study the role of airborne gravimetry for seamless bathymetry modeling over the Paracel Islands in northern South China Sea, and investigate the possibility of using ICESat-2 data and satellite-derived bathymetry (SDB) to evaluate bathymetry models over shallow waters. We use ICESat-2 data for training Sentinel-2 imagery and derive the SDB data with a Root Mean Squared Error (RMSE) of 0.29-0.50 m, which is lower than 10% of the maximum depths. The local bathymetry is modeled by using a modified version of S&S band-pass filter, and a partition-wise scheme is applied for determining the scaling factors. Numerical experiments verify the feasibility of using ICESat-2 and SDB data to assess bathymetry models. By utilizing the airborne gravity data, the fit between the computed bathymetry and the SDB data is significantly improved, by 18.7-58.0% over different shallow waters compared to recently released bathymetry models. The bathymetry predicted from the airborne data has also higher performance in deep water areas, which performs best in all these depth ranges from 500 to 3000 m. In comparison to the existing models, the RMSEs of the misfits between the computed bathymetry and the National Oceanic and Atmospheric Administration depths are reduced by tens to hundreds of meters in different depth ranges. Our study highlights that using airborne gravimetry for bathymetry modeling over island areas is advantageous, in both shallow and deep waters; and that ICESat-2 and SDB data can largely alleviate the lack of in-situ depths over shallow waters.

Gravity anomaly over shallow waters is one of the fundamental data sources for studying sea level change, ocean currents, and water exchanges between coastal areas and open seas. However, the acquirement of gravity data over shallow waters faces multiple challenges due to the degraded quality of satellite altimetry data and the scarcity of surveyed gravimetric observations. To alleviate this problem, we establish a framework for marine gravity anomaly refinement by using satellite-derived bathymetry (SDB). We use a cosine-tapered bandpass filter to extract high-frequency gravity signals from the SDB data, which compensate for the unresolved signals in satellite altimetric gravity data. Numerical experiments over the Discovery Reef and an offshore region near the Port Hedland, Western Australia, demonstrate that the utilization of SDB effectively strengths marine gravity anomaly. By combining the SDB data, the fits between the enhanced gravity anomaly models and surveyed airborne gravity data are improved, by 5.3%–15.7% in comparison with an altimetric gravity model DTU21GRA. The SDB calculated from the linear band model has slightly better performances in gravity anomaly modeling than that computed from the band ratio model and physical-based approach, agreeing well with the SDB validation results. Our results verify the feasibility of using the SDB computed from the physical-based approach for gravity anomaly augmentation, which is of great value in areas devoid of ground-truth depths. This study cements a way for the augmentation of marine gravity anomaly worldwide, especially in remote regions characterized by the scarcity of ground-based gravity data.

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), utilizing a 532 nm green laser, provides critical data for high-precision depth measurements in shallow water areas. However, due to the high sensitivity of photon detection to solar radiation noise and complex underwater topography, the data contains significant noise, making precise extraction of underwater terrain a major challenge. Vertical segmentation methods are ineffective in accurately capturing steep terrain features. This paper proposes a denoising algorithm based on point cloud gridding, which converts discrete photon data into a raster grid, emphasizing the spatial distribution of the water surface and underwater terrain. Through multi-scale density-adaptive processing, the algorithm effectively separates photons above, at, and below the water surface, accurately locating underwater terrain points, making it particularly suitable for photon data analysis in complex underwater environments. Experimental validation across multiple regions shows that the proposed algorithm significantly outperforms Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points to Identify the Clustering Structure (OPTICS) in terms of accuracy and robustness. Experimental results show that the proposed algorithm achieves a coefficient of determination (R²) of 0.99 across different regions, with root mean square error (RMSE) ranging from 0.27 to 0.38 meters, and mean absolute error (MAE) ranging from 0.21 to 0.33 meters. Compared to traditional methods, the proposed algorithm effectively removes noise points in sparse photon regions while preserving the continuity and details of underwater terrain in photon-dense areas. The experimental results demonstrate that the proposed algorithm accurately extracts the underwater contours of complex terrain and shows strong adaptability and stability in various aquatic environments. This method demonstrates outstanding precision and denoising capabilities in underwater terrain extraction in shallow water areas, providing a reliable and efficient approach for high-precision depth measurement of complex underwater topography.