FY-4A, as the latest generation of China’s geostationary meteorological satellite, has been launched on December 11, 2016. The Advanced Geosynchronous Radiation Imager (AGRI) is the primary payload onboard FY-4A, and it can image China and its surrounding areas 205 times per day. The AGRI with high-frequency observation capabilities can provide sufficient data support for aerosol monitoring, but few aerosol products are developed using AGRI. The accuracy of the available FY-4A AOD datasets is also inferior to that of the Himawari-8 aerosol product, which can also cover China. In this study, an aerosol optical depth (AOD) retrieval algorithm based on the database of ratio of surface reflectance of different channels is proposed to develop high-accuracy FY-4A AOD dataset.This algorithm involves four steps: (1) detect and remove cloud pixels in FY-4A L1 data; (2) perform gas absorption correction on FY-4A L1 data using the reanalysis data released by European Center for Medium-Range Weather Forecasts; (3) select the sub-dark pixels for each month from FY-4A data after gas absorption correction, and perform atmospheric correction on these sub-dark pixels using the background AOD (i.e., AOD at 550 nm is 0.02) to obtain the surface reflectance of VIS06 and NIR08 channels, obtain the ratio of surface reflectance of these two channels, and perform the abovementioned operations for all the pixels in the study area to complete the construction of surface reflectance ratio database of VIS06 and NIR08 channels; (4) retrieve AOD using FY-4A L1 data after gas absorption correction based on the constructed surface reflectance ratio database.The algorithm has been applied to aerosol retrieval over Beijing–Tianjin–Hebei region from May 2019 to October 2019. Comparison of FY-4A AOD retrieval results with MODIS AOD dataset released by NASA shows that the two AOD datasets have a consistent spatial distribution trend. Validation result of MODIS AOD dataset, the official Himawari-8 AOD dataset released by the Japan Meteorological Agency, and FY-4A AOD dataset against ground-based AOD data provided by Aerosol Robotic Network shows that the accuracy of FY-4A AOD dataset is better than that of Himawari-8 AOD dataset and MODIS AOD data. The root mean square error, mean absolute error, correlation coefficient with ground-based data, and percentage of retrieval results with error within ±(0.05+0.15AOD_AERONET) of FY-4A AOD dataset are 0.12, 0.09, 0.91, and 65.86%, respectively.Although the signal-to-noise ratio of FY-4A/AGRI is lower than that of Himawari-8/AHI, the absolute and relative errors of FY-4A AOD dataset are better than those of Himawari-8 AOD dataset. The statistical parameters of FY-4A AOD dataset are also slightly better than those of MODIS AOD dataset, which is one of the widely used AOD datasets with high accuracy. Therefore, the FY-4A AOD dataset developed in this study has high accuracy.

aerosol optical depth;remote sensing retrieval;FY-4A;advanced geosynchronous radiation imager;geostationary satellite