Lagoon Water Depth Estimation Using Hyperspectral Remote Sensing

Estimation of lagoon water depths using satellite imagery plays an important role in monitoring water level for future planning in environmental protection. Although the application of these methods for lagoon bathymetry is relatively new, the methods are basically similar. The main purpose of this paper is to investigate the ability of hyperspectral satellite imagery for shallow water bathymetry. However, the best method, i.e., Lyzenga and Stumpf Rational Model, is employed and tested in this research. The algorithm is based on the ratios of transformed reflectance values in the visible bands, retrieving greater depths than previous algorithms and compensating for variable bottom type and albedo. This method requires a few numbers of parameters to be tuned and can be applied to low-albedo features. We have applied the proposed method to the hyperspectral image data obtained by Hyperion sensor of EO-1 satellite. This dataset covers a shallow water area around Qeshm Island, in south of Iran. The results were indicative of the usefulness of such image data and remote sensing techniques, especially for the areas which lack precise bathymetric information or for the purpose of monitoring application of seasonal depth variation.