High-precision Cloud Detection and Aerosol Retrieval Algorithm for FY-4A AGRI images

Cloud detection and aerosol retrieval precision improvement in China and the surrounding regions is a great challenge for passive remote sensing detector due to the influence of haze pollution and complex land cover. A high-precision cloud detection and aerosol retrieval algorithm for the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A satellite is developed in this research. The cloud detection algorithm is built with a combination of an adjusted fisher discrimination algorithm and spatial heterogeneity test. The rectified MODIS cloud detection result is used as a criterion to distinguish the cloudy and clear field of view. In the aerosol retrieval algorithm, a new snow cover detection and a bidirectional reflectance distribution function library is constructed to reduce the bias caused by a deviation in surface reflectance estimation. This algorithm achieved a result with high data coverage and accuracy in China and the surrounding regions.