The frequent outbreak of dust storms every spring is one of the extreme weathers in East Asia. Geostationary meteorological satellite thermal infrared (TIR) imagery provides observations for high‐frequency, all‐day, large‐scale monitoring of dust sources and transport. This study proposes an integrated method to detect dust over East Asia by using the multispectral and multi‐temporal Himawari‐8/Advanced Himawari Imager (AHI) TIR observations, based on a Feedforward Neural Network (FNN) model and the Robust Principal Component Analysis based Anomalous Dust Detection (ADD) method. The FNN model is trained to map the multispectral Brightness Temperatures (BTs) to the three category probabilities (i.e., cloud, dust, and clear sky), and the ADD method calculates the dust score by normalizing multi‐temporal BT difference anomalous variation. The Integrated Dust Index (IDI) reflects the quantified dust confidence of the AHI pixel, which is defined as the sum of the dust probability and the dust score. The IDI was evaluated using 2 months of the Sentinel‐5P TROPOMI Ultraviolet Aerosol Index and achieved a total dust detection accuracy above 90%. A comparison with Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation nighttime observations shows that the IDI can also detect dust at night. The times series IDI of the massive 14–16 March 2021 dust storm show its capabilities to trace dust sources and monitor dust transport.

Land surface albedo inversion based on satellite data requires multiple consecutive (generally greater than or equal to 7) observations. Due to weather reasons such as cloud occlusion, it is difficult to obtain sufficient observation data, which leads to low inversion accuracy and even unsuccessful inversion. The anisotropic flat index (AFX) index was used to classify the 5-year multiangle observation data set of reflectance and eight bidirectional reflectance distribution function (BRDF) archetypes were obtained in Hefei, Anhui, China. The eight obtained BRDF archetypes in the Hefei area were applied to FY-2G satellite data for land surface albedo retrieval, and the retrieved land surface albedo was compared with MODIS land surface albedo products. The results show that the land surface albedo can be retrieved well using FY-2G data by BRDF archetypes.