AbstractOcean colour sensors on board satellite measure the solar radiation reflected by the ocean and the atmosphere. This information, denoted reflectance, is affected for about 90% by air molecules and aerosols in the atmosphere and only for about 10% by water molecules and phytoplankton cells in the ocean. Our method focuses on the chlorophyll-a concentration (chl-a) retrieval, which is commonly used as a proxy for phytoplankton concentration.Our algorithm, denoted NeuroVaria, computes relevant atmospheric (Angström coefficient, optical thickness, single-scattering albedo) and oceanic parameters (chl-a, oceanic particulate scattering) by minimizing the difference over the whole spectrum (visible+near-infrared) between the observed reflectance and the reflectance computed from artificial neural networks that have been trained with a radiative transfer model. This algorithm has been presented in [Jamet, C., Thiria, S., Moulin, C., Crepon, M. Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from ocean colour imagery. a feasibility study. J. Atmo. Ocean. Tech. 22 (4), 460–475, doi: 10.1175/JTECH1688.1, 2005]. NeuroVaria has been applied to SeaWiFS imagery in the Mediterranean sea. A comparison in the Mediterranean with in-situ measurements of the water-leaving reflectance, optical thickness and chl-a shows that NeuroVaria is consistent to process accurate atmospheric corrections and chl-a estimation for case-I waters and weakly absorbing aerosols. It validates the first step of this new approach of ocean colour images processing.


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    Title :

    Validation of a neuro-variational inversion of ocean colour images


    Contributors:
    Brajard, J. (author) / Jamet, C. (author) / Moulin, C. (author) / Thiria, S. (author)

    Published in:

    Advances in Space Research ; 38 , 10 ; 2169-2175


    Publication date :

    2006-03-26


    Size :

    7 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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