Application of simulated annealing in optical remote sensing of marine constituents

Oyvind Frette; Jakob J. Stamnes; Knut H. Stamnes; Svein Rune Erga

doi:10.1117/12.364187

16 September 1999 Application of simulated annealing in optical remote sensing of marine constituents

Oyvind Frette, Jakob J. Stamnes, Knut H. Stamnes, Svein Rune Erga

Proceedings Volume 3821, Environmental Sensing and Applications; (1999) https://doi.org/10.1117/12.364187
Event: Industrial Lasers and Inspection (EUROPTO Series), 1999, Munich, Germany

Abstract

Optical remote sensing of ocean color is a well-established technique. But most algorithms developed hitherto have been based on the assumption that only the phytoplankton affect the optical properties of the ocean. Such algorithms are often based on assumptions that become questionable in coastal areas. The assumption of a near-infrared dark pixel in the satellite image, will no longer be valid, and the band-ratio technique used for computing the algae concentration will also become inaccurate. To overcome these limitations we have developed an inverse-modeling algorithm for retrieval of marine constituents. Here the determination of ocean color is based on a three-component optical model consisting of chlorophyll-a, suspended matter, and yellow substance. We also use one parameter to describe the thickness of the aerosol layer. A simulated-annealing optimization scheme is employed to minimize the difference between measured satellite data and corresponding simulated data obtained using a coupled atmosphere-ocean radiative transfer code. The same optimization method has also been applied to the problem of retrieving the algae concentration in waters with vertical structure. In this case the marine parameters of interest are the algae concentration in two different layers as well as the thickness of the first layer.

Citation Download Citation

Oyvind Frette, Jakob J. Stamnes, Knut H. Stamnes, and Svein Rune Erga "Application of simulated annealing in optical remote sensing of marine constituents", Proc. SPIE 3821, Environmental Sensing and Applications, (16 September 1999); https://doi.org/10.1117/12.364187

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