Shallow-water Seafloor Topography Refinement Using Sentinel-2 and Multi-Source Gravity Data via a Depth-Stratified XGBoost Approach

Abstract

Shallow-water depth data serve as the essential foundation for marine spatial planning and coastal engineering projects. However, due to the attenuation of spectral signals, traditional retrieval of shallow-water seafloor topography struggles to maintain high accuracy in highly turbid waters and deeper areas. Furthermore, existing machine learning models incorporating spatial information are highly susceptible to spatial overfitting, which severely limits their generalization ability in complex waters. To address these issues, this study proposes a depth-stratified Extreme Gradient Boosting (XGBoost) approach combining Sentinel-2 remote sensing data and multi-source gravity data. This model innovatively incorporates gravity features, which are unaffected by water turbidity, effectively overcoming the signal attenuation limitations of spectral-only models in deep and turbid waters. In addition, the stratification strategy effectively mitigates spatial overfitting, which commonly affects machine learning models. Application results in the Pearl River Estuary, China, showed that compared to the unstratified spatially uncorrelated XGBoost global model, the root mean square error (RMSE) of the proposed model was significantly reduced by 1.362 m. Ablation experiments further confirmed that incorporating gravity features reduced the RMSE in deeper-water areas (depth > 30 m) by 1.033 m, and dropped the RMSE in highly turbid waters from 1.548 m to 0.955 m. Finally, validation in an adjacent area showed that the model maintained useful local spatial generalization under similar environmental conditions, with an RMSE of 0.591 m in the 30–35 m interval.