Highlights What are the main findings? A two-stage haze mapping algorithm (THMA) is developed using FY-4A/AGRI data, achieving high-precision classification of haze, clouds, and clear air, with robust performance over bright surfaces and in areas of vertically overlapping broken clouds and haze. Application to Asia in 2022 reveals distinct spatial-temporal patterns. The annual average number of haze days over China is 51.3, with 45-75 days in autumn/winter over emission-intensive regions and over 75 days in autumn in natural dust-dominated areas like the Taklamakan Desert. What are the implications of the main findings? By extending the traditional binary classification to specifically include haze, the THMA algorithm, developed for application to FY-4A/AGRI data, is designed for seamless application to similar instruments on geostationary satellites, such as FY-4B/C. The results confirm the complementary value of satellite remote sensing to ground-based observations for comprehensive haze monitoring, providing data for pollution process analysis and climate research.Highlights What are the main findings? A two-stage haze mapping algorithm (THMA) is developed using FY-4A/AGRI data, achieving high-precision classification of haze, clouds, and clear air, with robust performance over bright surfaces and in areas of vertically overlapping broken clouds and haze. Application to Asia in 2022 reveals distinct spatial-temporal patterns. The annual average number of haze days over China is 51.3, with 45-75 days in autumn/winter over emission-intensive regions and over 75 days in autumn in natural dust-dominated areas like the Taklamakan Desert. What are the implications of the main findings? By extending the traditional binary classification to specifically include haze, the THMA algorithm, developed for application to FY-4A/AGRI data, is designed for seamless application to similar instruments on geostationary satellites, such as FY-4B/C. The results confirm the complementary value of satellite remote sensing to ground-based observations for comprehensive haze monitoring, providing data for pollution process analysis and climate research.Abstract Haze, as a critical factor affecting regional air quality and human health, necessitates accurate remote sensing identification for pollution monitoring and climate research. This study proposes a two-stage haze mapping algorithm (THMA), based on a backpropagation neural network and a random forest model, which achieves high-precision identification of haze, clouds, and clear air using FY-4A AGRI geostationary satellite data, with small misclassification rates and high F1 scores. Through detailed comparison with CALIOP observations, THMA performs well over most regions over Asia, successfully extending the traditional binary classification task of distinguishing only clouds and clear air. Notably, the model provides good classification capability in vertically overlapping areas of broken clouds and haze, with minimal misclassification even over bright surfaces such as deserts and ice/snow. Statistical analysis for the year 2022 shows that the annual average number of haze days is 51.3 in China. This study confirms the significant complementary value of satellite remote sensing and ground-based observations for haze monitoring.