<![CDATA[Background: Satellite-Derived Bathymetry (SDB) has emerged as a cost-effective alternative for shallow-water depth mapping, particularly in remote and data-scarce marine regions. Raja Ampat, Southwest Papua, presents a challenging environment for bathymetric mapping due to its complex seafloor morphology and high water clarity, requiring robust analytical approaches to improve depth estimation accuracy. Objective: This study aims to evaluate the effectiveness of Sentinel-2–based SDB by comparing empirical regression models and a Random Forest machine learning approach for shallow-water bathymetry mapping in the waters of Raja Ampat. Methods: The research integrates Sentinel-2 multispectral imagery with three empirical regression methods—green band power regression, blue band power regression, and the Stumpf logarithmic ratio method—and a Random Forest algorithm. Field bathymetric data obtained from fishfinder measurements were used for model calibration and validation. All processing and analysis were conducted using the Google Earth Engine (GEE) platform. Results: The empirical regression analysis shows that the green band model (Band 3) achieved the highest accuracy (R² = 0.7097; RMSE = 1.80–14.12 m across depth classes), followed by the blue band model (R² = 0.6194) and the Stumpf method (R² = 0.5693). The Random Forest model outperformed all empirical approaches by effectively capturing non-linear relationships between spectral reflectance and water depth, particularly over heterogeneous seabed substrates. The green band model performed optimally at depths of 0–20 m, while the Stumpf method demonstrated greater stability beyond 20 m. Conclusion: The integration of Sentinel-2 imagery with machine learning provides an accurate, scalable, and cost-efficient solution for shallow-water bathymetric mapping in tropical archipelagic environments. This approach supports marine conservation planning and coastal resource management in Raja Ampat and similar regions.]]>
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