Validation of a neuro-variational inversion of ocean colour images

Abstract 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 (Angstrom 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.

[1]  Claude Lemaréchal,et al.  Some numerical experiments with variable-storage quasi-Newton algorithms , 1989, Math. Program..

[2]  James W. Brown,et al.  A semianalytic radiance model of ocean color , 1988 .

[3]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.

[4]  S. Thiria,et al.  Use of a Neurovariational Inversion for Retrieving Oceanic and Atmospheric Constituents from Ocean Color Imagery: A Feasibility Study , 2005 .

[5]  H. Gordon,et al.  Remote sensing of ocean color and aerosol properties: resolving the issue of aerosol absorption. , 1997, Applied optics.

[6]  H. Claustre,et al.  Determination of chlorophylls and carotenoids of marine phytoplankton: separation of chlorophyll a from divinylchlorophyll a and zeaxanthin from lutein , 1996 .

[7]  D. Antoine,et al.  Algal biomass and sea surface temperature in the Mediterranean basin intercomparison of data from various satellite sensors, and implications for primary production estimates , 2002 .

[8]  Sylvie Thiria,et al.  Monitoring aerosol optical properties over the Mediterranean from SeaWiFS images using a neural network inversion , 2004 .

[9]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[10]  H. Gordon.  Atmospheric correction of ocean color imagery in the Earth Observing System era , 1997 .

[11]  F. D’Ortenzio,et al.  Validation of empirical SeaWiFS algorithms for chlorophyll-a retrieval in the Mediterranean Sea: A case study for oligotrophic seas , 2002 .

[12]  P. Deschamps,et al.  SIMBAD: a field radiometer for satellite ocean-color validation. , 2004, Applied optics.