Retrieval of Near-Shore Bathymetry From Multispectral Satellite Images Using Generalized Additive Models

Abstract

Deriving shallow-water depth from multispectral satellite imageries appears very promising in near-shore bathymetric mapping for its high efficiency and low costs. Lyzenga’s linear band method, which currently is the most widely used algorithm in the field, performs a linear regression between some known water depths and image-derived input variables. In real-world situations, the linear assumption in Lyzenga’s method cannot be exactly satisfied because of complex environmental factors, and its performance is, therefore, often limited. In this letter, we improve Lyzenga’s method by introducing generalized additive models to describe the nonlinear relationship between water depths and image-derived regressors. The performance of the new method was evaluated by using Landsat-8 multispectral data over the coastal areas of Oahu Island, Hawaii. The experimental results of two independent validation regions show that the depth-retrieval accuracy of the proposed algorithm was about 20% better than that of Lyzenga’s method, and the performance improvement was most pronounced in the depth interval of 5–10 m.