Mapping Submarine Sand Wave Bathymetry from Sentinel-2 Texture Using a Spatial-Sequential Deep Learning Model

Submarine sand waves are widespread on shallow continental shelves. Their complex morphology and potential mobility create challenges for engineering surveys, navigation safety, and seabed stability assessment. Multibeam surveys provide accurate bathymetry but are costly and spatially limited, whereas satellite-based methods offer broader coverage but remain challenging in complex sand wave fields. Here, we propose a spatial-sequential 2DCNN–LSTM model for retrieving submarine sand wave bathymetry from Sentinel-2 surface reflectance imagery. The model represents each target point as a sequence of local multispectral image patches, allowing convolutional layers to extract two-dimensional textural features and LSTM layers to learn profile-scale rhythmic continuity associated with sand wave morphology. The model was trained using multibeam bathymetry and applied to a large extrapolation area of approximately 4000 km2 on the Taiwan Banks. Evaluation on the large extrapolated area against in situ bathymetric data achieved a root mean square error (RMSE) of 3.78 m, a mean absolute error (MAE) of 2.99 m, and a mean relative error (MRE) of 9.1%. The results demonstrate that sand wave-induced optical textures can provide useful information for broad-scale bathymetric reconstruction, although model performance remains dependent on image texture visibility controlled by hydrodynamic, illumination, and atmospheric conditions. This framework offers a cost-effective approach for satellite-based monitoring of large submarine sand wave fields, providing a new perspective for engineering applications.