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 contain significant noise, making precise extraction of underwater terrain a major challenge. Vertical segmentation methods are ineffective in accurately capturing steep terrain features. This article 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 multiscale 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 ($R^{2}$) of 0.99 across different regions, with root mean square error (RMSE) ranging from 0.27 to 0.38 m, and mean absolute error (MAE) ranging from 0.21 to 0.33 m. 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.

Accurate beach topography mapping is crucial for understanding coastal dynamics and mitigating climate change impacts. However, traditional methods such as airborne LiDAR have limitations, leading to substantial gaps in national-scale elevation data. This study presents an innovative framework to reconstruct missing elevation data along New Zealand’s coastline by integrating airborne LiDAR, Sentinel-2 optical imagery, and geometric features (distance) using machine learning methods. Our results show that Artificial Neural Network (ANN) emerged as the best model (test set: R²=0.79, RMSE=0.91 m; validation set: 0.79, RMSE=0.93 m), outperforming other models in accuracy. The produced 10-m DEM for national-scale sandy beaches expands area coverage by 286.6% (114.15 km²), filling gaps in 1249 beaches, including remote areas such as Stewart Island. This novel framework offers a scalable solution for improving the comprehensiveness and accuracy of beach topography. It provides essential support for inundation prediction, habitat management, and the development of climate adaptation strategies, thereby facilitating more informed decision-making in coastal zone management and climate change mitigation efforts.

Bathymetry provides instrumental information for studying sedimentary processes, global climate change, and benthic morphologies. The advantages and applicabilities of different techniques for bathymetry detection vary. We propose a framework for bathymetry enhancement from multisource data based on spherical radial basis functions (SRBFs). A case study is conducted over the Paracel Islands in South China Sea (SCS), where Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) measurements, airborne gravimetric observations, echo soundings, and the reference model DTU18BAT are merged. Numerical results suggest that the fusion of ICESat-2 observations dramatically enhances the quality of the computed bathymetry model near the islands, the root-mean-squared error (RMSE) of which is reduced by 45.35%–67.95% compared to existing models when validated against the satellite-derived bathymetry (SDB) with decimeter-level accuracy. By additionally fusing the airborne gravimetric data, bathymetry is further enhanced by ~22.49%, particularly over islands with sparse ICESat-2 trajectories. Comparisons with surveyed airborne bathymetric lidar data over the northern Antelope Reef yielded results consistent with those obtained from the SDB, suggesting that SDB is possible to serve as control data in waters devoid of ground truth data. Further analysis reveals that the models constrained by echo soundings performed better than existing models in deep waters, with reductions of 17.79%–44.99% in terms of RMSE. By fusing airborne gravity data, bathymetry is improved by ~10%, highlighting the utilization of airborne gravimetry in both shallow and deep waters. The proposed SRBF approach offers an effective way to merge heterogeneous data for high-quality bathymetry determination.

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 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 band pass 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 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 to 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.

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.

This article analyzes the interferometric measurements of ground-based global navigation satellite systems (GNSSs) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the signal to noise ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach in comparison with the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, e.g., icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.