PROJECT TITLE :

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

ABSTRACT:

Bathymetric data is crucial to the study and management of coastal zones. Passive remote sensing provides a price-effective different 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 link between image pixel spectral values and known water depth measurements, in that the log-linear inversion model is most generally used. Given a group of known water depth sample points, a bathymetric grid/map will be created by employing a spatial interpolation technique. However, when a restricted range of water depth sample points are on the market, the interpolation result's often unsatisfactory for portraying benthic morphology. During this letter, we have a tendency to 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 methodology as compared with standard kriging and log-linear inversion ways. It shows that the RK technique will manufacture more correct water depth estimations than the log-linear inversion methodology due to the account of the spatial pattern of the modeling residuals. The bathymetric grid created from the RK contains abundant additional spatial details regarding the ocean floor morphology than that from the ordinary kriging owing to the incorporation of auxiliary data from multispectral satellite imagery.