Spatial–Temporal Fusion of 10-Min Aerosol Optical Depth Products with the GEO–LEO Satellite Joint Observations

Geosynchronous equatorial orbit (GEO) satellite-derived AOD possesses huge advantages for monitoring atmospheric aerosol with high frequency; however, the data missing existing in products dramatically limits this expected advantage due to cloud obscuration and retrieval algorithm. In recent years, numerous fusion algorithms have been proposed, while these are mostly developed blend daily derived from low Earth (LEO) satellites generally neglect discrepancies different categories of products. Therefore, a spatiotemporal framework based on Bayesian maximum entropy theorem, blending GEO LEO satellite observations incorporating (GL-BME), is complementarily recover Advanced Himawari-8 Imager (AHI) over East Asia. The results show that GL-BME significantly improves average spatial completeness 20.3% 67.6% ensured reliability, accuracy merged AODs nearly maintains original AHI AODs. Moreover, comparison monthly distribution between conducted evaluate performance significance GL-BME, which indicates could further restore real situation certain extent basis dramatic coverage improvement.