Generalized Lyzenga's Predictor of Shallow Water Depth for Multispectral Satellite Imagery

Abstract

Multispectral satellite remote sensing can predict shallow-water depth distribution inexpensively and exhaustively, but it requires many in situ measurements for calibration. To extend its feasibility, we improved a recently developed technique, for the first time, to obtain a generalized predictor of depth. We used six WorldView-2 images and obtained a predictor that yielded a 0.648 m root-mean-square error against a dataset with a 5.544 m standard deviation of depth. The predictor can be used with as few as two pixels with known depth per image, or with no depth data, if only relative depth is needed.