Aerosol Optical Depth Retrieval Over South Asia Using FY-4A/AGRI Data

The Advanced Geosynchronous Radiation Imager (AGRI) is one of the main imaging sensors onboard the Fengyun-4A (FY-4A) satellite. Because of its high observation frequency, AGRI is suitable for continuous monitoring of atmospheric aerosols. In this study, we propose an aerosol optical depth (AOD) retrieval algorithm called the multichannel (MC) algorithm, which uses four channels (0.65, 0.83, 1.61, and $2.25~\mu \text{m}$ ) of AGRI. The algorithm assumes that the ratios between surface reflectance of different channels remain unchanged within two weeks, and the ratios are calculated by using Moderate-Resolution Imaging Spectroradiometer (MODIS)-combined AOD data to perform atmospheric correction on AGRI data under low pollution conditions (AOD at 550 nm less than 0.5). Since this algorithm is not developed for specific surface types, AOD retrieval can be achieved over both dark targets and bright surfaces. This algorithm has been applied to aerosol retrieval in South Asia. The accuracy assessment of the AGRI AOD dataset in 2019 and 2020 using the ground-based data from 11 aerosol robotic network (AERONET) sites shows that the AGRI AOD dataset has a high accuracy, and the statistical parameters of AGRI AOD dataset are slightly better than those of MODIS-combined AOD dataset. The root-mean-square error (RMSE), mean absolute error (MAE), relative mean bias (RMB), and percentage of data with errors within the expected error $\pm (0.05+0.15 \times {{\text {AOD}}}_{{\text {AERONET}}})$ (EE15) of AGRI AOD dataset are 0.16, 0.12, 0.23, and 63.71%, respectively. The RMSE, MAE, RMB, and EE15 of MODIS-combined AOD dataset are 0.18, 0.13, 0.24, and 61.06%, respectively.