Aerosol retrieval algorithm based on adaptive land-atmospheric decoupling for polarized remote sensing over land surfaces

Land-atmospheric decoupling is important for the retrieval accuracy in atmospheric remote sensing. In this study, we developed an algorithm that adaptively decouples the land surface and atmosphere reflected solar radiation and retrieves the aerosol properties (adaptive land-atmospheric decoupling algorithm, ALAD). The ALAD algorithm allows precise global aerosol optical depth (AOD) retrievals. The four sub-procedures in ALAD comprise estimating a first guess for surface properties, aerosol property retrieval, atmospheric correction, and assessing the results. The ALAD algorithm was applied to PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) and the retrieved AODs were compared with the AOD products from the Moderate resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET). ALAD and MODIS retrieved AOD values were with good consistency in terms of their spatial distribution, regardless of the region or the global distribution. The correlation coefficient between the global ALAD and AERONET AOD was 0.875, and about 60.7% of the AODs retrieved by ALAD were within the expected error range of ±(15% AOD + 0.05). The results obtained using ALAD agreed well with the AERONET values. We also investigated the aerosol and land surface properties that might introduce errors into the AOD values retrieved by ALAD. It was found that the value of AOD had the greatest effect on the accuracy of the retrieval results. The errors introduced by the Angstrom exponent and Normalized Difference Vegetation Index varied slightly but they made no significant contributions.