Accurately identifying and classifying aerosols is a key to understanding their sources, assessing their chemical and physical changes, and comprehending their feedback mechanisms within climate system. However, existing aerosol classification methods have two limitations: First, ground remote sensing products have limited spatial coverage, and polar orbit satellite data suffer from low temporal resolution. Second, satellite-based aerosol classification methods are affected by cloud interference, resulting in data gaps. To overcome these challenges, this study utilized the extreme tree algorithm (ET) and integrated the FY-4A TOAR dataset with meteorological and geographical information, using a sequential forward selection method for optimal input feature selection. Focusing on the Asian region (70-140 degrees E, 15-55 degrees N), three models were used to generate hourly aerosol classification products: the Clear air-Cloud-Aerosol-Mixed cloud and aerosol (CCAM-ET) model, the Dust-Polluted dust-Other aerosols-Mixed aerosols (DPOM-ET) model under clear sky conditions, and the DPOM-ET model under non-clear sky conditions. The evaluated accuracy of the CCAM-ET model was 85%, demonstrating its effectiveness in identifying cloud and aerosol under various conditions. The CCAM model can achieve an accuracy of 78% when aerosols are located above clouds and 59% when they are below clouds. The DPOM-ET model achieved an evaluation accuracy of 89% for clear sky areas and 87% for non-clear sky areas, respectively, and was superior to traditional methods. Long-term data indicated a significant correlation between the distributions of different types of aerosols and human activity. Polluted dust is common in southwest China during spring and in northern China during winter. Additionally, from 2018 to 2020, dust occurrences decreased on the Indian Peninsula, while non-dust aerosols increased significantly in India and Southeast Asia.
