(untitled) (JURNAL GEOCELEBES, 2026)
Bathymetry mapping using conventional methods faces limitations in shallow waters. With the development of remote sensing technology, satellite-derived bathymetry (SDB) emerges as an alternative by utilizing wavelengths that penetrate water and capture depth information. This study compares the performance of three empirical SDB methods: Random Forest (RF), Multiple Linear Regression (MLR), and the Van Hengel and Spitzer (VHS) algorithm. SPOT 6 ORTHO-level satellite imagery and depth data from single beam echosounder measurements were used to construct the depth models. Model accuracy was evaluated using root mean square error (RMSE), mean absolute error (MAE), and total vertical uncertainty (TVU). Results show that the RF method achieves the highest accuracy across most depth ranges (1–5 m, 5–10 m, and 10–15 m), while the VHS algorithm performs best at 0–1 m. At depths beyond 15 meters, MLR shows relatively better performance compared to other methods, although overall uncertainty remains high. Based on the coefficient of determination (R2), RF achieves the best result with a value of 0.610, followed by MLR with 0.462, and VHS with 0.313. These findings highlight the superior adaptability of the RF method in estimating bathymetry across varying depth zones using optical satellite imagery.