Abstract
Inversion of ocean color reflectance measurements can be cast as an optimization problem, where particular parameters of a forward model are optimized in order to make the forward-modeled spectral reflectance match the spectral reflectance of a given in situ sample. Here, a simulated ocean color dataset is used to test the capability of a recently introduced global optimization process, particle swarm optimization (PSO), in the retrieval of optical properties from ocean color. The performance of the PSO method was compared with the more common genetic algorithms (GA) in terms of model accuracy and computation time. The PSO method has been shown to outperform the GA in terms of model error. Of particular importance to ocean color remote sensing is the speed advantage that PSO affords over GA.
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