A novel approach for bathymetry of shallow rivers based on spectral magnitude and shape predictors using stepwise regression

Spatial heterogeneities of substrate type, water-surface roughness and also inherent optical properties (IOPs) of the water column can pose substantial challenges to optical remote sensing of fluvial bathymetry. Development of robust techniques with respect to the optical complexities of riverine environments is then central to produce accurate bathymetry maps over large spatial extents. The empirical (regression-based) techniques (e.g., Lyzenga’s model) have widely been applied for estimation of bathymetry from optical imagery in inland/coastal waters. The models in the literature are built upon only magnitude-related predictors derived from spectral radiances/reflectances at different bands. However, optically complicating factors such as variations in bottom type and water column constituents can change not only the magnitude but also the shape of water-leaving spectra. This research incorporates spectral derivatives as shaperelated predictors in order to enhance the description of spectra through the regression-based depth retrieval. A stepwise regression is utilized to select the optimal predictors among all the possible Lyzenga (i.e., magnitude-related) and derivative (i.e., shape-related) predictors. Radiative transfer simulations are used to examine the bathymetry models in optically-complex shallow rivers by considering variable bottom-types and IOPs. The methods are also applied to a WorldView-3 image of the Sarca River located in Italian Alps and resultant bathymetry estimates are assessed using insitu measurements. The results indicate the effectiveness of spectral derivatives in improving the accuracies of depth retrievals particularly for optically-complex waters.