(1) Background: Recognising the full spatial and temporal distribution of PM2.5 is important in order to understand the formation, evolution and impact of pollutants. The high temporal resolution satellite, Himawari-8, providing an hourly AOD dataset, has been used to predict real-time hourly PM2.5 concentrations in China in previous studies. However, the low observation frequency of the AOD due to long-term cloud/snow cover or high surface reflectance may produce high uncertainty in characterizing diurnal variation in PM2.5. (2) Methods: We fill the missing Himawari-8 AOD with MERRA-2 AOD, and drive the random forest model with the gap-filled AOD (AODH+M) and Himawari-8 AOD (AODH) to estimate hourly PM2.5 concentrations, respectively. Then we compare AODH+M-derived PM2.5 with AODH-derived PM2.5 in detail. (3) Results: Overall, the non-random missing information of the Himawari-8 AOD will bring large biases to the diurnal variations in regions with both a high polluted level and a low polluted level. (4) Conclusions: Filling the gap with the MERRA-2 AOD can provide reliable, full spatial and temporal PM2.5 predictions, and greatly reduce errors in PM2.5 estimation. This is very useful for dynamic monitoring of the evolution of PM2.5 in China.
Keywords: Himawari-8; MERRA-2; PM2.5; gap-filled; random forest.