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Open AccessArticle

Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA

1
Centre d’Études Spatiales de la Biosphère, CESBIO, Université de Toulouse-CNES-CNRS-IRD-UPS, 18 Avenue Édouard Belin, 31401 Toulouse CEDEX 9, France
2
Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, 91191 Gif-Sur-Yvette, France
3
Institut Pierre-Simon Laplace, CNRS/Université Pierre et Marie Curie, 4 place Jussieu, 75252 Paris CEDEX 5, France
4
Centre National d’Études Spatiales, 18 Avenue Édouard Belin, 31401 Toulouse CEDEX 9, France
*
Authors to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1230; https://doi.org/10.3390/rs9121230
Received: 3 October 2017 / Revised: 9 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform cloud detection and atmospheric correction. For the correction of aerosols effects, MAJA makes an estimate of the aerosol optical thickness (AOT) based on multi-temporal and multi-spectral criteria, but there is insufficient information to infer the aerosol type. The current operational version of MAJA uses an aerosol type which is constant with time, and this assumption impacts the quality of the atmospheric correction. In this study, we assess the potential of using an aerosol type derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis. The performances, with and without the CAMS information, are evaluated. Firstly, in terms of the aerosol optical thickness retrievals, a comparison against sunphotometer measurements over several sites indicates an improvement over arid sites, with a root-mean-square error (RMSE) reduced by 28% (from 0.095 to 0.068), although there is a slight degradation over vegetated sites (RMSE increased by 13%, from 0.054 to 0.061). Secondly, a direct validation of the retrieved surface reflectances at the La Crau station (France) indicates a reduction of the relative bias by 2.5% on average over the spectral bands. Thirdly, based on the assumption that surface reflectances vary slowly with time, a noise criterion was set up, exhibiting no improvement over the spectral bands and the validation sites when using CAMS data, partly explained by a slight increase in the surface reflectances themselves. Finally, the new method presented in this study provides a better way of using the MAJA processor in an operational environment because the aerosol type used for the correction is automatically inferred from CAMS data, and is no longer a parameter to be defined in advance. View Full-Text
Keywords: remote sensing; atmospheric correction; time series; aerosols; surface reflectance; Sentinel-2; CAMS; MACCS; MAJA remote sensing; atmospheric correction; time series; aerosols; surface reflectance; Sentinel-2; CAMS; MACCS; MAJA
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Rouquié, B.; Hagolle, O.; Bréon, F.-M.; Boucher, O.; Desjardins, C.; Rémy, S. Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA. Remote Sens. 2017, 9, 1230.

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