Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

. Bathymetry, the measurement of sea depth, has conventionally been conducted using echo-sounders on vessels. However, various factors limit conventional shipborne surveys in coastal regions, including data continuity, geographic obstacles, diplomatic concerns, and marine infrastructures. Remote sensing technology can address these limitations, particularly with the advancement of satellite imaging technology. Indeed, many studies are underway to develop machine learning-based water depth estimation technologies. However, previous studies have focused on clear waters with low turbidity or uniform seabed sediment. Therefore, in this study, we developed a satellite-derived bathymetry (SDB) model using the random forest machine learning algorithm, which was applied to three coastal areas around the Korean Peninsula with distinct characteristics: clear waters (Samcheok), high turbidity (Cheonsuman), and varied seabed sediments (Hallim). We then compared the accuracy of the bathymetric mapping data derived in these three areas. The estimated depth values exhibited the highest accuracy in Samcheok, followed by Hallim and Cheonsuman. Based on Worldview-3 images and on-site surveys, we confirmed the presence of basalt on the seabed. However, the remote reflectance was attenuated due to the effect of the black rock, leading to an overestimation of the depth. In the future, additional satellite images will be applied as training data for the machine learning model to advance the SDB technology using turbidity and seabed sediment distribution data for each area. Ultimately, the SDB results will be applied as depth monitoring data to facilitate safe ship passage in coastal areas, including ports that require periodic and consistent coastal bathymetry. In addition, they can be applied as input data for numerical ocean models, contributing to various fields.