A Comparative Analysis to Model Bathymetry using Multi-sensor Satellite Imageries

Abstract

Abstract Accurate and high-resolution water depth information are important for wide range of coastal research and monitoring. In this case, providing an accurate bathymetric map is a major challenge for remote sensing. This study developed and evaluated a semi-parametric regression to extract depth information using various image datasets (Landsat 8, Sentinel 2A and Worldview 2). We compared the ability of these imageries to map depth information using generalized additive model (GAM). GAM is a semi-parametric generalized linear model which allow for nonlinear relationships between covariates and the target variable. We used the Morotai shallow water area in Indonesia to apply GAM in deriving depth information. We found that higher image spatial resolution results in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques for deriving bathymetric data.