Gradient Boosting and Linear Regression for Estimating Coastal Bathymetry Based on Sentinel-2 Images

Abstract

Thousands of vessels travel around the world every day, making the safety, efficiency, and optimization of marine transportation essential. Therefore, the knowledge of bathymetry is crucial for a variety of maritime applications, such as shipping and navigation. Maritime applications have benefited from recent advancements in satellite navigation technology, which can utilize multi-spectral bands for retrieving information on water depth. As part of these efforts, this study combined deep learning techniques with satellite observations in order to improve the estimation of satellite-based bathymetry. The objective of this study is to develop a new method for estimating coastal bathymetry using Sentinel-2 images. Sentinel-2 was used here due to its high spatial resolution, which is desirable for bathymetry maps, as well as its visible bands, which are useful for estimating bathymetry. The conventional linear model approach using the satellite-derived bathymetry (SDB) ratio (green to blue) was applied, and a new four-band ratio using the four visible bands of Sentienl-2 was proposed. In addition, three atmospheric correction models, Sen2Cor, ALOCITE, and C2RCC, were evaluated, and Sen2Cor was found to be the most effective model. Gradient boosting was also applied in this study to both the conventional band ratio and the proposed FVBR ratio. Compared to the green to blue ratio, the proposed ratio FVBR performed better, with R2 exceeding 0.8 when applied to 12 snapshots between January and December. The gradient boosting method was also found to provide better estimates of bathymetry than linear regression. According to findings of this study, the chlorophyll-a (Chl-a) concentration, sediments, and atmospheric dust do not affect the estimated bathymetry. However, tidal oscillations were found to be a significant factor affecting satellite estimates of bathymetry.