Guoping Zhang

Satellite-derived bathymetry (SDB) commonly treats the sea surface as a source of radiometric disturbance, although the same surface wave field can also contain water depth information through gravity wave propagation. This article presents a Wave-Aware Physics-Informed Neural Network (WA-PINN) that represents these two roles within a unified shallow water bathymetry framework. The optical geometry stream combines Sentinel-2 multispectral observations with seafloor geometry. The disturbance stream converts European Centre for Medium Range Weather Forecasts Reanalysis Version 5 (ERA5) wind forcing into Mean Square Slope (MSS) through the Cox–Munk relationship and introduces a roughness-dependent sunglint contribution into the optical physics constraint. The signal stream retrieves local wavelength, propagation direction, and wave celerity from Sentinel-2 surface texture and constrains predicted depth with the finite depth gravity wave dispersion relation. A Wave Validity Factor (WVF) controls the influence of the dispersion constraint according to the coherence and physical plausibility of the retrieved wave field. The three streams are fused in a shared depth predictor constrained by supervised depth loss, optical physics loss, and wave dispersion physics loss. The framework is demonstrated over Culebra, St. Thomas, and St. John in the northeastern Caribbean Sea. The complete Dual Role model reaches an overall Root Mean Square Error (RMSE) of 1.21 m, a Mean Absolute Error (MAE) of 0.77 m, and an R² of 0.96. Relative to the Slope Aware configuration, the Disturbance and Signal models reduce RMSE by 15.7% and 12.8%, while the complete model reduces RMSE by 29.7%. The disturbance contribution increases under stronger wind forcing, whereas the signal contribution increases with WVF. Depth-dependent RMSE increases from 0.82 m at 0 to 5 m to 1.58 m at 20 to 30 m. These results illustrate the intended complementarity between correcting wave-induced optical contamination and exploiting wave propagation as an additional depth constraint.

Melt ponds function as a decisive regulator of the Arctic ice-albedo feedback and surface energy partition during the summer ablation season. However, their seasonal evolution poses a challenge for parameterization in climate models due to the sub-grid-scale hydro-thermodynamic processes involved in meltwater redistribution. While contemporary data-driven architectures have demonstrated proficiency in instantaneous melt pond mapping, their performance during multi-step temporal roll-out is often compromised by accumulative bias and physical inconsistency, manifesting as systematic geometric degradation and boundary blurring. To address these limitations, this study proposes a Dynamic Physics-Consistent Prediction Model (DPCPM), which reformulates melt pond evolution as a step-wise, physics-constrained temporal propagation problem. The core innovation lies in the dynamic integration of three process-based constraints at each propagation step: thermodynamic energy availability based on surface radiative forcing, morphology-constrained spatial propagation utilizing high-resolution micro-topography, and hydrologic water-balance stability. By enforcing physical consistency within the neural propagation cycle, the DPCPM effectively suppresses the divergent drift inherent in purely stochastic models. The framework was evaluated using multi-source observations including Sentinel-2 imagery, high-resolution digital elevation models, and ERA5 atmospheric forcing collected during the 2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Results across the 10 selected melt-pond regions during the 2020 MOSAiC melt period indicate that the DPCPM achieves a pixel-wise Root Mean Square Error (RMSE) between 0.06 and 0.17 for propagation horizons up to one month. The model maintains a high geometric fidelity for ponded regions, achieving Intersection over Union (IoU) values up to 0.53, outperforming unconstrained data-driven baselines. By explicitly bridging sub-grid meltwater dynamics with physical energy constraints, this work provides an observation-constrained and physically consistent framework that may support the development and evaluation of melt pond parameterization in large-scale climate simulations.

Traditional satellite-derived bathymetry (SDB) often suffers from systematic optical path distortions due to the neglect of seafloor slope effects, leading to significant accuracy degradation in high-gradient coastal areas. This study proposes a Slope-Aware Physics-Informed Neural Network (SA-PINN) framework that synergistically utilizes ICESat-2 bathymetric photons and Sentinel-2 multispectral imagery. The core innovation involves a slope-aware operator, integrated into the radiative transfer-based physics loss function, which explicitly rectifies directional optical path deviations induced by seafloor inclination. By fusing physical mechanisms with data-driven features, the model utilizes a seven-dimensional feature space comprising four spectral bands, two directional slope components, and prior depth. Applications at Culebra, Maui, and Molokai demonstrate that SA-PINN significantly outperforms the Stumpf model, Random Forest, and standard CNNs, achieving root mean square errors (RMSE) of 1.36 m, 2.91 m, and 1.34 m, respectively. Ablation studies confirm that SA-PINN reduces RMSE by up to 37% compared to CNN in complex regions with slopes exceeding 10°, ensuring superior physical consistency and spatial continuity. This research provides a robust, in situ-free automated solution for high-resolution bathymetric mapping in remote and steep coastal environments globally.

Nearshore bathymetry plays a vital role in marine ecological monitoring, coastal zone management, and nautical chart production. Satellite remote sensing has become a primary approach for bathymetry due to its broad spatial coverage, data abundance, and cost-effectiveness. However, single-temporal methods are often limited by transient noise, such as clouds, waves, and ship wakes, which reduce the accuracy. In response, multi-temporal methods have gained increasing attention. By fusing multi-temporal satellite images, these methods can suppress environmental noises, thereby improving the accuracy and stability of depth retrieval. Nonetheless, most existing multi-temporal methods lack physical constraint mechanisms, making them vulnerable to the influence of anomalous images during fusion, and they often overlook temporal variations in water properties and seafloor substrates. These limitations hinder their stability in complex nearshore environments. To address these challenges, this study proposes a novel Multi-Temporal Spaceborne Bathymetry (MTSB) method that integrates ICESat-2 laser altimetry data with multi-temporal Sentinel-2 images. The proposed method uses laser-derived depth points as physical constraints to guide high-quality image fusion. It then applies a stratified modelling strategy based on column laying and sediment classification to adapt to varying conditions. Experiments were conducted in three representative nearshore regions: Culebra, Puerto Rico; Oahu and Niihau, Hawaii. Results show that the MTSB method outperforms existing methods in accuracy. Specifically, the Root Mean Square Error (RMSE) was 1.32 m in Culebra, 1.48 m in Oahu, and 1.61 m in Niihau. Moreover, MTSB showed superior adaptability to environmental disturbances. This research provides a new perspective for high-precision satellite-derived bathymetry and demonstrates promising potential for filling bathymetric data gaps in remote reefs and other inaccessible marine areas.

Although the supraglacial lakes are significant for a comprehensive understanding of the glacier material balance mechanism, obtaining accurate supraglacial lake data by satellite remote sensing is challenging. The lack of three-dimensional information limits the accuracy of image-based methods, while the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data are limited to satellite trajectories. In this study, the detection and mapping of the supraglacial lakes are implemented by jointly processing the ICESat-2 and Sentinel-2 data. The lake-region images are extracted with the help of ICESat-2 photons, and the lake depths are retrieved. The depths of four supraglacial lakes on the Amery Ice Shelf (AIS), Antarctica, were mapped by trained machine learning methods and Bayesian Optimization (BO). The performance of the proposed method is evaluated by comparing it with manual results or self-verification. The results show that joint lake region extraction accuracy is 95.16%. The proposed photon extraction method, combined with BO-CatBoost, achieves the highest accuracy in retrieval, with an RMSE of 0.17 m. This study enables automatic operation without input parameters, thus improving the performance and automation level of supraglacial lake observation. With more abundant ICESat-2 data and multispectral images, the proposed method can automatically obtain large-scale surface melting in time and space.

The continuous demand for coastal zone resource exploitation and marine ecosystem protection challenges satellite remote sensing observation of shallow sea apparent optical properties. The existing satellite retrieval method of the light detection and ranging (LiDAR) attenuation coefficient ( $K_{\mathrm {lidar}}$ ) relies on water column photons, so it cannot achieve accurate water quality measurements in shallow water depths. In this study, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data were innovatively fused with Sentinel-3 image to retrieve comprehensive $K_{\mathrm {lidar}}$ . First, $K_{\mathrm {lidar}}$ is calculated in offshore areas using ICESat-2 water column photons and in nearshore areas using seafloor photons. ICESat-2-derived $K_{\mathrm {lidar}}$ was then integrated with Sentinel-3 image, employing Bayesian-optimized CatBoost (BO-CatBoost) to invert the spatial distribution of both offshore and nearshore areas. Finally, the retrieval results’ weights were assigned based on Sentinel-3’s band ratio, yielding comprehensive $K_{\mathrm {lidar}}$ results for the shallow seas. The proposed method successfully obtained an accurate spatial distribution of $K_{\mathrm {lidar}}$ in Passu Keah, Culebra, and Marquesas Keys. The results show that the coefficient of determination ( $R^{2}$ ), mean relative error (MRE), and mean absolute error (MAE) for the three study areas range from 0.7402 to 0.8557, 0.0405 to $0.0885~\mathrm {m}^{-1}$ , and 0.0033 to $0.0055~\mathrm {m}^{-1}$ , respectively. The root mean square error (RMSE) ranges from 0.0043 to $0.0077~\mathrm {m}^{-1}$ . It has higher reliability than Sentinel-3’s $K_{d}{(}532{)}$ . As satellite remote sensing improves, the method will efficiently map the LiDAR attenuation coefficient across vast areas, offering reliable data for resource development, water quality, and ecosystem conservation.