=> The main difficulty of the atmospheric correction comes from the determination of the aerosols optical properties: one has to know the optical properties of the aerosol type present in the atmosphere and determine their optical thickness. Using Sentinel-2 data to determine the aerosol type is very complicated, and our MAJA processor, used to generate Theia L2A products, only computes the aerosol optical thickness, while assuming a specific aerosol type. The current operational version of the MAJA processor uses a constant aerosol type during the atmospheric correction, independently from the location and from the time of the year, thus affecting the quality of the atmospheric correction if the chosen aerosol type is not appropriate. As an alternative, we tried to use the information from CAMS (Copernicus Atmosphere Monitoring Service), whichprovides forecasts of the Aerosol Optical Thickness (AOT, see figure below) of five different aerosol types: dust, black carbon, sea salt, sulfate and organic matter. 

CAMS aerosol optical thickness (AOT) forecasts at 550 nm on 14 June 2016, 03:00 UTC: (top left) Dust, (top right) Sea Salt, (bottom left) Black Carbon, and (bottom right) Sulfate.
The contribution of each one of these aerosols in the atmospheric correction performed by MAJA is then proportional to their AOT. Thanks to this new configuration, the aerosol type used in the atmospheric correction is variable both in time and space (and depends on the CAMS AOT forecasts), and is no longer a parameter to be defined in advance.Time series of Sentinel-2A data are used to perform the atmospheric correction over a selection of arid and vegetated sites, providing a wide range of surface and aerosol conditions. One method to assess the performances of MAJA when using CAMS information is to compare the AOT at 550 nm estimated by MAJA to the ground-based observations from AERONET. The stable cases, in blue, correspond to AERONET observations which are stable with time and with no more than 10% of clouds (according to MAJA cloud mask) within a 10-km neighborhood around the AERONET site. We focus on these cases when estimating the performances. Over arid sites on the figure below, using CAMS information (right) allows a significant improvement of the AOT estimated by MAJA (RMSE=0.068), compared to the constant aerosol method (left, RMSE=0.095). This is especially true over Saada (Morocco) with the RMSE reduced from 0.206 to 0.068, or over Banizoumbou (Niger) with the RMSE reduced from 0.464 to 0.256.
Scatter plot of AOT estimated by MAJA at 550 nm versus AERONET, over arid sites. Left: constant aerosol model. Right: five CAMS aerosol models.
 Over vegetated sites on the figure below, using CAMS information (right) shows a slight increase of the RMSE compared to the constant aerosol method (left), but the performances are of the same order of magnitude. This slight increase can be explained by the fact that the constant aerosol is a standard continental aerosol type, designed to perform better over vegetated sites, and by the noise due to passing from a constant aerosol to a variable one. 
Scatter plot of AOT at 550 nm versus AERONET, over vegetated sites. Left: constant aerosol model. Right: five CAMS aerosol models.


Initially, the objective was to avoid selecting a particular method or aerosol type according to the site being processed. Introducing CAMS information allows a variable aerosol type and is a suitable choice to significantly increase the performances over arid sites, and to keep almost the same performances over vegetated sites. More details about this study can be found in the article “Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA” published in Remote Sensing and available here.

3 thoughts on “Using aerosol type from Copernicus Atmosphere in MAJA

  1. Thank you for sharing this information. I would like to ask: are the five CAMS aerosol types already used in the correction of L2A images available on THEIA?Also, do you have any experience in the use of the derived AOT in studying air pollution. Some work has been done on this using MODIS, but I haven’t seen it yet with Sentinel-2.

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