Grid Computing for Multi-Spectral Tomographic Reconstruction of Chlorophyll Concentration in Ocean Water

In the last decades, the development of inversion methodologies for radiative transfer problems has been an important research topic in many branches of science and engineering [Go02, Mc92]. The direct or forward radiative transfer problem in hydrologic optics, in the steady state, involves the determination of the radiance distribution in a body of water, given the boundary conditions, source term, inherent optical properties (IOPs), such as the absorption and scattering coefficients, and the phase function. The inverse radiative transfer problem arises when physical properties, internal light sources, and/or boundary conditions must be estimated from radiometric measurements of the underwater light field. A challenge in the inverse hydrological optics problem is to determine the IOPs, considering only the water-leaving radiance. The inverse problem is formulated as an optimization problem and iteratively solved using a recent intrinsic regularization scheme [PrEtAl04, SoEtAl04b] coupled to an ant colony optimization (ACO). The regularization scheme pre-selects candidate solutions based on their smoothness, quantified by a Tikhonov norm [PrEtAl04]. Profiles generated with the wrong curvature are filtered out using a second derivative criterion [SoEtAl09, SoEtAl07]. An objective function is given by the square difference between computed and experimental radiances at every iteration. Each candidate solution corresponds to a discrete chlorophyll profile.