Geographically Adaptive Inversion Model for Improving Bathymetric Retrieval From Satellite Multispectral Imagery

Abstract

Optical remote sensing imagery offers a cost-effective alternative to echo sounding and bathymetric light detection and ranging surveys for deriving high density bottom depth estimates for coastal and inland water bodies. The common practice of previous studies has been to calibrate a single global bathymetric inversion model for an entire image scene. The performance of conventional global models is limited when the bottom type and water quality vary spatially within the scene. To address the inadequacy of the conventional global models, this paper presents a geographically adaptive inversion model to better estimate bottom depth. Although the general mathematical form of the geographically adaptive model is the same, model parameters are optimally determined within a geographical region or a local area, in contrast to the entire scene in the global inversion model. By using high-resolution IKONOS and moderate-resolution Landsat satellite images, we demonstrated that regionally and locally calibrated inversion models can effectively address the problems introduced by spatial heterogeneity in water quality and bottom type, and provide significantly improved bathymetric estimates for more complex coastal waters.