Multi-band bottom index: A novel approach for coastal environmental monitoring using hyperspectral data

Abstract

Hyperspectral remote sensing has potential for improving shallow-water environmental monitoring, including benthic cover mapping and estimation of bathymetry and water optical properties. Detailed spectral information provided by hyperspectral sensors is particularly required to discriminate benthic covers that have similar spectral patterns (e.g., coral and algae), which cannot be achieved by multispectral sensors with a limited number of wavebands. An important process in such applications is separation of the bottom reflection from the absorption and scattering caused by the water column. Recently, inversion of a semi-analytical radiative transfer model using airborne hyperspectral data has become popular for simultaneous estimation of benthic cover, bathymetry, and water optical properties. However, it requires non-trivial settings and ancillary information, and generally is not user friendly. A cost-effective water-column correction method based on two-band combination has also been popular for multi-band remote sensing; however, it is not suited to hyperspectral data, because of the combinatorial proliferation of many hyperspectral wave bands. To fill the gap between the two approaches, in this paper, a new cost-effective method for water-column correction optimal for hyperspectral data is proposed. The method, called the multi-band bottom index (MBI), incorporates empirical radiative transfer theory and a traditional spectral enhancement technique (i.e., logarithmic residual method). A case study was conducted for the coral reef habitats around Ishigaki Island, using free hyperspectral satellite imagery (Hyperion). The result shows that our MBI approach has more robust performance in the estimation of the water attenuation coefficient than the existing water-column correction approach. The derived MBI map successfully mitigates water-column effects and enhances the spectral pattern of benthic materials. Comparison with in-situ spectra in the literature revealed that the overall spectral pattern of the MBI is reasonable for the benthic categories of coral, seagrass, and sandy bottom. The MBI approach is also promising for application to recently launched hyperspectral satellite sensors, such as HISUI, DESIS, and PRISMA.