Abstract

1. Estimating aerosol parameters above the ocean from MERIS observations using topological maps Top-of-Atmosphere reflectance Solar reflectance Molecule/Aerosol scattering Aerosol absorption Water-leaving reflectance Phytoplankton scattering Phytplancton absorption • The reflectance rtoacan be decomposed into several components (Gordon, 1994) as : • ratm(l) stands for the atmospheric contribution (multiple scattering contributions from the atmospheric gases, the aerosols and the Rayleigh-aerosol coupling), • rg is the contribution of the sun glitter at the sea surface attenuated by the direct atmospheric transmittance T(l), • rw(l) is the water leaving reflectance (including the foam contribution) attenuated by the diffuse atmospheric transmittance t(l). • We define also : • It represents the part of the TOA signal that is not impacted by the ocean (ocean is “black”). We define the glitter index iG that represents the relative importance of the glitter contamination on the rtoa signal : where rg is the glitter reflectance computed using the Cox and Munck (1954) model and is the Euclidean norm of the TOA reflectance. 1) We consider 3 classes of glitter defined by : class 1: iG < 1%, class 2: 1% ≤iG <3%, class 3: 3% ≤iG 5%. 2) For each class of glitter, we consider 3 aerosol types : type 1 consists of the spectra where t < 0.1. This subset corresponds to situations of clear sky, for which the shape of the aerosol phase function has a minor impact, type 2 (continental type) consists of the spectra where t≥0.1 and in presence of continental aerosols, type 3 (desertic type) consists of the spectra where t≥0.1 and in presence of desertic aerosols. Processing for the August 20th 2003 Real colour image(RGB) Atmospheric reflectance rblack (490nm) retrieved with NN algorithm Optical thickness t retrieved with NN algorithm and wind speed Optical thickness t retrieved with standard MERIS algorithm J. BRAJARD*(1)(2), A. NIANG(3), S. SAWADOGO(3), F. FELL(4), R. SANTER(5) AND S. THIRIA(1) (1) LOCEAN, BC 100, T 45-55, 4 place Jussieu, 75252 PARIS Cedex 05, France (2)ACRIst, Sophia-Antipolis, France (3)University of Dakar, BP 5085, Dakar-Fann, Senegal (4)INFORMUS GmbH, Gustav-Meyer-Allee 25, 13355 Berlin, Germany (5)Université du Littoral Côte d'Opale, Maison de la Recherche en Environnement (MREN), 62930 Wimereux, France *Corresponding author. Email : Julien.Brajard@lodyc.jussieu.fr Radiative transfer modelling Abstract A topological Neural Network (Kohonen map) was used here to retrieve the aerosol optical properties over ocean case 1 waters from the MEdium Resolution Imaging Spectrometer (MERIS) observations. The Kohonen map was first used to aggregate similar spectral MERIS signatures in 400 pertinent groups without any a-priori information on the corresponding optical properties of atmosphere and ocean. For this purpose, more than 148.000 spectra taken from 85 MERIS images collected in 2003 and 2004 over the Mediterranean Sea were processed to train the Kohonen map. The 400 groups were then labelled using synthetic spectra (of which the corresponding optical properties are known) generated by the MOMO (Matrix Operator MethOd) code. This algorithm was successfully applied to a set of 5 MERIS images representative of the conditions prevailing in the Eastern Mediterranean. The good spatial homogeneity of the level-2 atmospheric products – the atmospheric reflectance and the aerosol optical thickness (AOT) – is a first indication of the potential of the presented method. Ground based measurements of the AOT provided by the AERONET (AErosol RObotic NEtwork) program, collected on a small island (Lampedusa) in the Mediterranean Sea, validated our method for the five selected days.. Objective : Inverse the rtoa signal to retrieve atmospheric parameters such as rblack or the aerosol optical thickness t Methodology • Learning the MERIS data • 148 357 rtoa spectra are extracted from 85 MERIS images of the Eastern Mediterranean Sea taken between July 3th 2003 and April 4th 2004.They are used to train a Kohonen map 20x20 (Kohonen, 2001). After this training phase, each neuron of the map is associated with a particular reference vector. The map has learn useful information from the data. 2. Labelling the Kohonen map The MOMO code (Fell and Fischer, 2001) is used to generate a synthetic set of rtoa.Each spectrum is associated with physical parameters such as rblack, t, the measurement geometry and the type of aerosol. The synthetic spectra are projected onto the Kohonen map that has been learnt in the previous step. Each neuron is then associated with a set of rtoa spectra and a set of physical parameters. This set is divided into nine subset that take into account the glitter contamination and the type of aerosol that has been used to generate the rtoa spectrum. 3. Processing a MERIS image To invert a pixel of a given MERIS image. We project the measured spectrum onto the Kohonen map. This pixel being associated with a neuron. The glitter index iG is computed to choose one of the three mean branch of the neuron (see the figure below), and then the pixel is associated with the closest spectrum on the secondary branch. At the end, each pixel is associated with physical parameters such as rblack and t. Validation with in-situ data We validate the retrieved AOT values by using in situ measurements from the AERONET station located at Lampedusa island (35°31’ N, 12°37’ E). Five measurements taken in close temporal vicinity to the satellite overpass were considered as being of sufficient quality for the comparison Conclusion The method reported here was inspired from that reported in Niang et al. (2003) but some modifications have been introduced to specifically account for MERIS images. The contamination of the images by the sunglint lead to the definition of 3 classes which describe its relative level. The results are promising in terms of spatial continuity of the products as well as regarding the validation. The retrieval of the aerosol optical thickness is good when compared with the ground based measurements and the MERIS standard algorithm values. Going further in the removal of sunglint effects is a strong requirement for the MERIS data processing. In the present stage our method does not provide acceptable results for strong sunglint but further developments are foreseen. As a perspective, the treatment of case 2 waters could be also approached using a similar methodology. Ackownledgments The NAOC project, supported by the European Commission under project number EVG1-CT-2000-00034, initiated the present study. Particularly, we took advantage of the synthetic data set generated by Th. Schröder and J. Fischer of Freie Universität Berlin using the radiative transfer code MOMO. We also thank ESA for providing MERIS data and J. Vidot from ULCO for making these data available to us and assisting in the data processing. The authors also would like to thank the AERONET project lead by GSFC. References Cox, C. and Munk W., 1954, Measurements of roughness of the sea surface from photographs of the sun glitter. Journal of Optical Society in America, 44 (11), 838-888. Fell, F. and Fischer, J., 2001, Numerical simulation of the light field in the atmosphere-ocean system using the matrix-operator method. 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