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.

Keywords:

• Bathymetry
• multispectral
• satellite remote sensing
• coral reef

Acknowledgements

This work was supported in part by a Grant-in-Aid for Research Activity Start-up (#22860044) from the Japan Society for the Promotion of Science. Depth measurements with a multi-narrow-beam echo sounder (Reson SeaBat 7125) were provided by the U.S. Naval Oceanographic Office via the website of the National Oceanic & Atmospheric Administration (NOAA)'s National Geophysical Data Center. Special thanks to Yukio Koibuchi of the University of Tokyo and Saki Nobuta and Kohei Ishida of Yamaguchi University, who helped with in situ bathymetry.