The repetitive observations of satellites provide rich multi-temporal information for coastal remote sensing, making it possible to improve the accuracy of bathymetric inversion through multi-temporal satellite data. This study takes Culebra, Puerto Rico, as the study area and attempts multi-temporal bathymetric inversion using 193 Sentinel-2 images and eight tracks of ICESat-2 ATL03 data. Two widely used machine-learning models, CatBoost and Random Forest (RF), were employed to construct bathymetric inversion models, and the Fusion followed by Inversion (FI) strategy and inversion followed by Fusion (IF) strategy were also compared. The results show that the R² of inversion based on multi-temporal observations exceeds 97.47%, with RMSE lower than 1.00 m, and MAE lower than 0.54 m, making the results more accurate than most single-phase results. The FI strategy yields better results than the IF strategy, with an RMSE of 0.81 m. Both CatBoost and RF models exhibit comparable robustness under the tested scenarios, with CatBoost showing minor advantages in specific cases, achieving an average RMSE of 0.88 m. Furthermore, multi-temporal observations effectively mitigate environmental interference, such as clouds and waves, enhancing the reliability of bathymetric inversion. The findings highlight the potential of combining the FI strategy with advanced machine-learning models to achieve more reliable bathymetric inversion results.

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.

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.