## 5 Conclusions  
The data blanks in the geostationary satellite AOD products constrain its advantage and application in continuous monitoring of atmospheric aerosols with high frequency to a certain extent. In this study, we develop a spatiotemporal fusion framework maximally excavating the complementary advantages of LEO and GEO satellite sensors to obtain the Himawari-8 AHI 10-min AOD dataset with higher spatiotemporal coverage and desired  
Figure 9: Maps of the monthly spatial distribution in 2017 for the original Himawari-8 L2 AHI and merged AOD datasets. The color bar represents the AOD values.  
accuracy. The proposed framework is a pixel-based AOD fusion algorithm on the moving windows of Himawari-8 AOD guided by the Bayesian maximum entropy theorem, combined with incorporating the discrepancies from different categories of products. The overall averaged spatial completeness of the merged AOD after GL-BME fusion significantly improves with an increment of nearly 50% (from 20.3% to 67.6%) over the original AHI AOD product and increases by nearly 20% than merged AOD via only AHI AOD, which shows the contribution of LEO to enhancing the performance of the merged AOD to a certain extent. The accuracy validation against AERONET and MAN AOD observations shows that there exists a slight decrease in the accuracy of the merged AOD compared to the accuracy of the original AHI AOD, yet the merged AODs possess a relatively higher ratio within the EE envelop than the original AHI AODs. Moreover, an estimation error variance analysis of the merged AOD for different moments indicates that the merged AODs via GL-BME fusion have sufficient reliability for aerosol-related applications, such as the assessment of aerosol radiation effects and their interaction with clouds. Furthermore, a comparison of the monthly averaged AOD maps between the original AHI AOD and merged AOD products shows that the merged AOD after the GL-BME fusion could better characterize the real atmospheric pollution situation than the original AHI AOD.  
Since the quality of CALIOP data after 2017 is heavily influenced by low-energy laser shots, we accordingly adopt 2017 as the most recent study period and as a representative to evaluate the feasibility and performance of the GL-BME AOD fusion framework. Obviously, the proposed GL-BME fusion framework is also applicable to other geostationary satellites (e.g., the FY-4A and GOES-R series) and other years to substantially improve the availability of GEO satellite AOD products. Theoretically speaking, the merged AOD products could provide more spatiotemporally continuous information on aerosols, which is of practical significance for extensive atmospheric research and applications.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs15082038/s1](https://www.mdpi.com/article/10.3390/rs15082038/s1)]([https://www.mdpi.com/article/10.3390/rs15082038/s1](https://www.mdpi.com/article/10.3390/rs15082038/s1)).  
Conceptualization, X.X. and T.Z.; funding acquisition, W.G.; investigation, J.D., S.F. and W.X.; project administration, L.W.; supervision, Z.Z., W.W., Y.G., Y.L. and X.Z.; visualization, X.X.; writing--original draft, X.X. All authors have read and agreed to the published version of the manuscript.  
This study was supported financially by the National Natural Science Foundation of China (No. 42071353).  
The authors thank JAXA and NASA for their free provision of the Himwari-8 MODIS and CALIOP products. Thanks are due to AERONET for their data maintenance. We express our sincere gratitude to the anonymous reviewers and the editor for their constructive comments.  
The authors declare no conflict of interest.