New Framework Integrates ICESat-2 and Multi-Source Remote Sensing for High-Precision Bathymetric Mapping

Shallow seas are vital transition zones linking land and the deep ocean, providing essential resources for navigation, fisheries, energy exploration, and island reef development. Accurate bathymetric data form the foundation for marine engineering, channel safety, resource assessment, and ecological restoration. However, nearly 50% of global shallow-water regions still lack reliable depth information, posing a major obstacle to coastal management and sustainable ocean development.

A research team led by Prof. NIE Sheng at the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), has developed a new bathymetric mapping framework that integrates indirect inversion of ICESat-2 satellite LiDAR data with multi-source remote sensing observations. The framework, detailed in Remote Sensing of Environment combines wave-based indirect bathymetry, multi-source feature fusion, and a temporal sample-transfer strategy to achieve high-accuracy, wide-coverage mapping in optically complex shallow waters.

Satellite laser altimetry is currently the most precise technique for remote bathymetry, capable of capturing both water surface and seabed elevations. Yet its application remains limited by signal attenuation in turbid waters and the low generalization ability of optical inversion models. To overcome these challenges, the team designed a multi-component framework that fully leverages the complementary advantages of optical, SAR, and LiDAR observations.

At the core of the framework lies the Progressive Adaptive Window for Local Period (PAWLP) method, which enables wave-based indirect bathymetry using ICESat-2 data. By setting adaptive analysis windows along photon tracks, PAWLP dynamically matches local wave fluctuations to extract representative wave periods. Applying linear wave theory, it then estimates water depth even in regions where seabed echoes are absent. Compared with conventional fixed-period approaches, PAWLP maintains higher accuracy and stability across diverse water conditions, improving spatial consistency and robustness of bathymetric results.

To expand coverage beyond LiDAR-detectable zones, the researchers introduced a temporal sample-transfer strategy that reuses ICESat-2-derived reference points across multiple time phases. By integrating Sentinel-2 optical imagery, SAR texture features, tidal height, and tidal current velocity, the framework fuses multi-source and multi-temporal information within a random forest regression model. Validation against reference measurements shows strong agreement in both turbid and clear waters, significantly enhancing depth retrieval accuracy and spatial continuity.

The new framework effectively expands the spatial coverage and depth range of bathymetric inversion, particularly in turbid waters, enabling more accurate and reliable mapping of coastal seafloor topography. These advancements provide valuable support for coastal monitoring, management, and a wide range of marine applications.