Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data

Bathymetry estimated from optical satellite imagery has been increasingly implemented as an alternative to traditional bathymetric survey techniques. The availability of new sensors such as Sentinel-2 with improved spatial and temporal resolution, in comparison with previous optical sensors, offers innovative capabilities for bathymetry derivation. This study presents an assessment of the fit between satellite data and the underlying models in the most widely used empirical algorithms: the linear band model and the log-transformed band ratio model using Sentinel-2A data. Both models were tested in two study areas of the Irish coast with different morphological and environmental conditions. Results showed that the linear band model fitted better than the log-transformed band ratio model providing coefficient of determination values, R², between 0.83 and 0.88 (0 m–10 m) for the five images considered in the study. The closest fit was found in the depth range 2 m–6 m. Atmospheric correction, bottom type influence, and water column conditions proved to be key factors in the bathymetric derivation using these satellite datasets.