Three-Dimensional Geographically Weighted Inverse Regression (3GWR) Model for Satellite Derived Bathymetry Using Sentinel-2 Observations

Current trends of development of satellite derived bathymetry (SDB) models rely on applying calibration techniques including analytical approaches, neuro-fuzzy systems, regression optimization and others. In most of the cases, the SDB models are calibrated and verified for test sites, that provide favorable conditions for the remote derivation of bathymetry such as high water clarity, homogenous bottom type, low amount of sediment in the water and other factors. In this paper, a novel 3-dimensional geographical weighted regression (3GWR) SDB technique is presented, it binds together methods already presented in other studies, the geographically weighted local regression (GWR) model, with depth dependent inverse optimization. The proposed SDB model was calibrated and verified on a relatively difficult test site of the South Baltic near-shore areas with the use of multispectral observations acquired by a recently launched Sentinel-2 satellite observation system. By conducted experiments, it was shown that the proposed SDB model is capable of obtaining satisfactory results of RMSE ranging from 0.88 to 1.23[m] depending on the observation and can derive bathymetry for depths up to 12m. It was also shown, that the proposed approach may be used operationally, for instance, in the continuous assessment of temporal bathymetry changes, for areas important in the context of ensuring local maritime safety.