Prediction of Water Depth From Multispectral Satellite Imagery—The Regression Kriging Alternative

Bathymetric information is crucial to the study and management of coastal zones. Passive remote sensing provides a cost-effective alternative to acoustic surveys and bathymetric LiDAR techniques. Most previous studies estimated water depth from multispectral imagery in shallow coastal and inland waters by establishing the relationship between image pixel spectral values and known water depth measurements, in which the log-linear inversion model is most widely used. Given a set of known water depth sample points, a bathymetric grid/map can be created by using a spatial interpolation technique. However, when a limited number of water depth sample points are available, the interpolation result is often unsatisfactory for portraying benthic morphology. In this letter, we propose to use the regression kriging (RK) approach to combine the optimal spatial interpolation of kriging with the high-resolution auxiliary information of multispectral imagery for a detailed bathymetric mapping. A case study has been performed to demonstrate and evaluate the performance of the RK method in comparison with ordinary kriging and log-linear inversion methods. It shows that the RK method can produce more accurate water depth estimations than the log-linear inversion method due to the account of the spatial pattern of the modeling residuals. The bathymetric grid created from the RK contains much more spatial details about the ocean floor morphology than that from the ordinary kriging owing to the incorporation of auxiliary information from multispectral satellite imagery.