Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

TLDR

Shallow‑water bathymetry is critical for navigation and coastal wave modeling, yet existing satellite‑derived methods rely on supervised data and cannot cover inaccessible areas. This study develops a generalized depth‑estimation model for shallow water bathymetry using random‑forest machine learning and multi‑temporal satellite imagery. The authors analyzed 135 Landsat‑8 images and extensive training bathymetry data from five sites in Google Earth Engine, then evaluated the model’s accuracy against reference bathymetry. The resulting system achieved a root‑mean‑square error of 1.41 m for depths of 0–20 m and is expected to be applicable to other shallow‑water regions with high water transparency.

Abstract

Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervised data for analysis, and cannot be used to analyze areas that are unreachable by boat or airplane. In this study, a mapping method for shallow water bathymetry was developed, using random forest machine learning and multi-temporal satellite images to create a generalized depth estimation model. A total of 135 Landsat-8 images, and a large amount of training bathymetry data for five areas were analyzed with the Google Earth Engine. The accuracy of SDB was evaluated by comparison with reference bathymetry data. The root mean square error in the final estimated water depth in the five test areas was 1.41 m for depths of 0 to 20 m. The SDB creation system developed in this study is expected to be applicable in various shallow water regions under highly transparent conditions.