Deriving bathymetry and water properties from hyperspectral imagery by spectral matching using a full radiative transfer model

ABSTRACT

Many existing techniques for estimating the optical properties of a body of water directly from a hyperspectral remote sensing spectrum are based on the idea of ‘spectral matching’ – that is, input parameters in a forward model are systematically varied until the measured and modelled spectra are sufficiently similar. This is usually done by using numerical optimization methods with a simplified forward model, or by precomputing a large library of spectra with a more realistic model, and then searching the library for the best match. In this letter, we show that is possible to combine the two approaches by using a full radiative transfer model in an optimization routine. We include results of running the algorithm on both simulated and measured data.