Publications

Wang, QX; Du, DS; Li, SW; Yang, J; Lin, H; Du, J (2021). Comparison of different methods of determining land surface reflectance for AOD retrieval. ATMOSPHERIC POLLUTION RESEARCH, 12(8), 101143.

Abstract
Determining land surface reflectance (LSR) is important for satellite retrieval of aerosol optical depth (AOD). Many studies have focused on the development of AOD retrieval through using different land surface reflectance determination algorithms. However, there is still lack of a comprehensive comparison among those algorithms. In this study, AOD retrieval algorithms with different land surface reflectance assumptions were compared and analyzed using Advanced Himawari Imager (AHI) data. The results demonstrate that the AOD retrieved by the algorithm considering the land surface bidirectional reflectance distribution function (BRDF) effects (Method 3) has a higher accuracy than that based on the dark target (Method 1) and second lowest reflectance (Method 2) methods. The overall correlation coefficient between the AOD retrievals considering land surface BRDF and AERONET AOD measurements is 0.93 and the Root Mean Square Error (RMSE) is 0.13. In addition, there are 68.91 % of AOD matchups falling within the expected error (+/- 0.05 +/- 0.2 x AOD (AERONET)). Method 3 shows a more robust performance than the other two methods for different seasons, normalized difference vegetation index (NDVI) values and scattering angles. It also shows the advantages of AOD retrieval in urban regions with relatively high LSR. The AOD retrievals based on Method 2 show the best agreement with MODIS 1 km AOD product, while Method 1 AODs are much closer to the MODIS 10 km AODs. The differences of annual average values of AHI AOD retrievals using different algorithms can be high to 0.13 at 11:00 (Beijing Time) over the Beijing-Tianjin-Hebei (BTH) region, which could cause a 23.6Wm(-2) discrepancy when calculating the direct aerosol radiative effect (DARE) at the bottom of the atmosphere.

DOI:
10.1016/j.apr.2021.101143

ISSN:
1309-1042