A Geographically and Physics-Informed Convolutional Neural Network for Satellite-Derived Bathymetry Using ICESat-2 and Sentinel-2 Datasets
TL;DR
A geographically and physics-informed convolutional neural network (GPI-CNN) that integrates Sentinel-2 multispectral imagery with ICESat-2-derived bathymetric samples and geographic coordinates and improves bathymetric inversion accuracy is proposed.
Abstract
Accurate bathymetric data are vital for the development of the blue economy and marine ecological civilization, but conventional surveys are costly and spatially limited. Satellite-derived bathymetry offers broader coverage at lower cost, yet its accuracy is often constrained by limited reference-depth data and spatial variations in water-column optical properties and bottom reflectance. To address these limitations, we propose a geographically and physics-informed convolutional neural network (GPI-CNN) that integrates Sentinel-2 multispectral imagery with ICESat-2-derived bathymetric samples and geographic coordinates. By incorporating geographic and physics-informed features derived from radiative-transfer theory, the model accounts for spatial variations in the spectral-depth relationship and improves bathymetric inversion accuracy. The proposed method was evaluated in two representative coastal areas: Coral Island in the Xisha Islands of the South China Sea and Guam in the western Pacific. The GPI-CNN model was compared with the traditional Stumpf model, Random Forest (RF), and U-Net. The results demonstrate that GPI-CNN outperforms the Stumpf model, RF, and U-Net, achieving root mean square errors of 0.53 m and 0.85 m, and mean absolute errors of 0.34 m and 0.64 m, respectively. These results confirm the advantage of GPI-CNN in improving the accuracy of shallow-water bathymetric estimation.