Validation of a neuro-variational inversion of ocean colour images

Details Validation of a neuro-variational inversion of ocean colour images Validation of a neuro-variational inversion of ocean colour images

Jan 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006adspr..38.2169b&link_type=abstract

Advances in Space Research, Volume 38, Issue 10, p. 2169-2175.

Computer Science

1

Scientific paper

Ocean 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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