Satellite-Derived Bathymetry of Danish Coastal Waters Using Machine Learning with Sentinel-2 and ICESat-2 data - Kristoffersen,Freja (Master Thesis in Geographic Information Science, 2026)
Accurate shallow-water bathymetry is essential for navigation safety and coastal zone management, but conventional hydrographic surveys are costly and spatially limited, especially in dynamic nearshore environments. This thesis evaluates the newly released ICESat-2 ATL24 bathymetry product. It investigates its suitability as training data for satellite-derived bathymetry (SDB) using Sentinel-2 imagery. The workflow comprises two main steps (1) validation of ATL24 depths against in situ multibeam echosounder (MBES) measurements, and (2) development of SDB models using ATL24 as the target variable to estimate depth from Sentinel-2 reflectance. Four approaches are examined: the Stumpf Ratio Method (SRM) and three machine-learning (ML) models - Random Forest (RF), CatBoost (CB), and Multilayer Perceptron (MLP). The study focuses on two Danish coastal sites with contrasting seabed conditions: Jammerbugten (depths between 0-15m) and Nordfyn (0-8 m). Validation reveals that ATL24 depths have higher uncertainty at Jammerbugten with a Root Mean Square Error (RMSE) of 0.72 m but closer agreement at Nordfyn (RMSE of 0.27 m). Across both sites, the ML models consistently outperformed SRM: both CB and MLP achieve RMSE values of 1.02 m at Jammerbugten and MLP reaches 1.07 m at Nordfyn. Seabed characteristics strongly influence model performance: the uniform sandy seabed at Jammerbugten promotes a stable reflectance-depth relationship, whereas the spatially variable substrate at Nordfyn introduces uncertainty, particularly at shallow depths where SRM performs slightly better. This study demonstrates that combining ICESat-2 ATL24 with Sentinel-2 enables useful large-area coastal screening to detect zones of potential depth change where targeted surveys may be prioritized.
Keywords: Geography; GIS; Satellite-Derived Bathymetry; ICESat-2; ATL24; Machine Learning