Bathymetry, the measurement of water depth and underwater terrain, is vital for scientific, commercial, and environmental applications. Traditional methods like shipborne echosounders are costly and inefficient in shallow waters due to limited spatial coverage and accessibility. Emerging technologies such as satellite imagery, drones, and spaceborne LiDAR offer cost-effective and efficient alternatives. This research explores integrating multi-sensor datasets to enhance bathymetric mapping in coastal and inland waters by leveraging each sensor’s strengths. The goal is to improve spatial coverage, resolution, and accuracy over traditional methods using data fusion and machine learning. Gülbahçe Bay in İzmir, Turkey, serves as the study area. Bathymetric modeling uses Sentinel-2, Göktürk-1, and aerial imagery with varying resolutions and sensor characteristics. Model calibration evaluates independent and integrated use of single-beam echosounder (SBE) and satellite-based LiDAR (ICESat-2) during training. After preprocessing, Random Forest and Extreme Gradient Boosting algorithms are applied for bathymetric inference. Results are assessed using accuracy metrics and IHO CATZOC standards, achieving A1 level for 0–10 m, A2/B for 0–15 m, and C level for 0–20 m depth intervals.
Gülher et al. (Thu,) studied this question.