Improving Satellite-Derived Bathymetry in Complex Coastal Environments: A Generalised Linear Model and Multi-Temporal Sentinel-2 Approach

Satellite-derived bathymetry (SDB) enhances monitoring capabilities in the context of global change and provides a cost-effective alternative to traditional in situ methods. However, a significant gap remains in the accuracy of SDB at shallow water depths (0–10 m), particularly in complex coastal settings. In this study, we developed a two-step methodology to improve shallow water depth estimates using empirical models and multi-temporal Sentinel-2 satellite imagery. Ten Sentinel-2 images from a one-year period were analysed using the Lyzenga and Stumpf empirical reference models, followed by the application of an empirical generalised linear model (GLM). Composite images were created by combining pixel values across the temporal dataset and compared with individual image results within the model. The validation results confirmed that the GLM outperformed the reference empirical models. The optimal selection of multi-temporal images demonstrated superior performance compared to single-image regression, achieving a 42% reduction in RMSE and a minimum MAE of 0.34 m. Furthermore, enhanced outlier identification within the multi-temporal analysis reduces local anomalies and enables further improvements in accuracy. These findings underscore the enhanced capability of GLM and multi-temporal images for improving the accuracy of SDB, with a relevant impact on many coastal monitoring applications and potential for scalable implementation in other regions.