Ecological Informatics (Jul 2025)
Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea
Abstract
Satellite-derived bathymetry (SDB) has emerged as a critical technique in response to the growing demand for large-scale coastal bathymetric mapping. However, high-resolution multispectral imagery from Gaofen satellites presents significant challenges owing to low signal-to-noise ratios (SNRs). This study aimed to enhance coastal bathymetric mapping by integrating high-resolution Gaofen satellite imagery with ICESat-2 lidar-derived bathymetry. The specific goals are to develop a novel physics-informed recurrent neural network (PI-RNN) for SDB that does not rely on prior information and assess its performance in terms of accuracy and robustness. We propose a physics-based RNN model that combines spectral and radiative transfer information from Gaofen satellite imagery with reference bathymetric data from ICESat-2. This methodology includes an adaptive ellipse density-based spatial clustering of applications with noise (AE-DBSCAN) algorithm for ICESat-2 data extraction, which surpasses standard DBSCAN in terms of accuracy. The RNN model was trained using various band combinations of Gaofen satellite data, and its performance was evaluated against in-situ measurements from Ganquan Island in the South China Sea. The physics-based RNN model achieved good bathymetric accuracy, with a coefficient of determination (R2) value >0.93 and a root mean square error (RMSE) 0.93 and a root mean square error (RMSE) < 0.83 m when compared to actual measurements from an island in the area. This study demonstrates that our method not only achieves remarkable bathymetric accuracy but also simplifies the mapping process by eliminating the need for complex atmospheric corrections. This advancement is a significant step forwards in the field of coastal mapping and offers a more efficient and effective tool for managing and understanding coastal resources and the environment.
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