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Advancing Coral Reef Bathymetry: A GAN-Augmented and Stratified CNN Analysis of Fused ICESat-2 and Sentinel-2 Dataset

Ziyao Chen,Li Wang,Wei Feng,Yan Gu,Jin Li,Ya Ping Wang

Published 2026 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

High-resolution bathymetry mapping of coral reefs is essential to morphodynamic study of reef habitats, assisting reef monitoring and conservation under global climate change. However, the accuracy of conventional satellite-derived bathymetry (SDB) is reduced at depths over 15 m with optical signal attenuation and training data insufficiency. To address this gap, here, we present an approach that synergizes ICESat-2 advanced topographic laser (ATL24) photon-counting LiDAR data with Sentinel-2 multispectral imagery. A generative adversarial network (GAN) is implemented to offset dataset deficiency at deeper depths, and a stratified convolutional neural network (CNN) is adapted to distinct optical-depth regimes. Bathymetry derived at Jiuzhang Atoll is in good agreement with the in situ multibeam measurements, with a mean absolute error (MAE) of 0.75 m and a root-mean-squared error (RMSE) of 10% of the present maximum depth of 19 m, validating the effectiveness of GAN-driven sample synthesis to make up measurement inadequacy, and the enhancement of model generalizability across a wide depth range by stratified CNN. This approach could be applied to bathymetry mapping of coral reefs worldwide at depths of 15–30 m, where biodiversity generally increases the most with multisource satellite observations.

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