Work

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

Year: 2013

Type: article

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.

Source: Marine Geodesy

DOI: 10.1080/01490419.2013.839974

Authors: Ariyo Kanno, Yoji Tanaka, Akira Kurosawa, Masahiko Sekine

Institutions: Yamaguchi University, Yokohama National University, Hitachi Solutions Create, Ltd. (Japan), Hitachi (Japan)

Language: English

Topic: Coastal and Marine Dynamics

Subfield: Earth-Surface Processes

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

Funders: University of Tokyo, National Oceanic and Atmospheric Administration, Japan Society for the Promotion of Science

Award: Estimation of shallow water depth using satellite imagery : generalization of estimation formula with increased number of bands