Abstract
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.
Highlights
Time series of spaceborne high resolution optical images can be used to study the state and evolution of land surfaces
When comparing the performances of the different methods, we focus on these stable cases, even though we plot the other cases
It can be gap-filled if it fits none of the two following conditions set by Multi-sensor Atmospheric Correction and Cloud Screening (MACCS)-ATCOR Joint Algorithm (MAJA) to compute the aerosol optical thickness (AOT): for the multi-spectral method, the Normalized Difference Vegetation Index (NDVI) must be larger than 0.2; for the multi-temporal method, as we assume the surface reflectance varies slowly with time, the short-wave infrared (SWIR) surface reflectance must be close to the value of the previous date
Summary
Time series of spaceborne high resolution optical images can be used to study the state and evolution of land surfaces. Most of them rely on multi-spectral criteria, based on empirical relationships between the surface reflectances at different spectral bands, to estimate the aerosol optical thickness (AOT). This is the case of the algorithm described in [9], which is able to make an estimate of the aerosol type as well. This same method could be applied to Sentinel-2, but has not been used in practice and should be implemented in a future study
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