## 6 Conclusions  
Accurate smoke detection maps would undoubtedly be quite useful in scientific research related to climate change simulation, weather prediction, and human environment monitoring. In this study, we propose a new stepwise smoke detection algorithm, called SSDA, based on the strong Mie scattering of smoke aerosols at blue and green bands in the VIIRS sensor data record. Visually compared with smoke-related data, including VIIRS true-color images, TROPOMI smoke, VIIRS ADP, and previously published results, the SSDA smoke detection shows more robust results in separating smoke from clouds and bright surfaces and a stronger capability of detecting smoke plumes released from both large and small fires. Using manually delineated smoke reference data as a benchmark, we also quantitatively validated SSDA smoke detection results for fire-prone regions globally. Statistical analysis shows that the commission errors of the SSDA smoke detections are 8%, 3%, 2%, 29%, 4%, and 7%, whereas the omission errors are 17%, 51%, 48%, 35%, 24%, and 42% in North America, South America, Africa, Indonesia, Siberia, and Australia, respectively. Commission errors of SSDA are much lower than TROPOMI smoke and comparable with ADP product, while omission errors of SSDA are significantly lower than both these two datasets. Commission errors were most prevalent under conditions of haze pollution, whereas omission errors generally corresponded to areas of extremely thin smoke plumes, especially when above bright surfaces or mixed with clouds. Further,comparison with smoke reference data indicates that the SSDA smoke detection approach works very well over regions associated with dense smoke plumes but also improves smoke plume detection over regions characterized by thin smoke plumes. Finally, the SSDA algorithm shows powerful capabilities for calculating smoke occurrence across the globe with comparable spatial patterns to fire density derived from VIIRS active fire product. More importantly, this new smoke detection algorithm provides an improved tool for fire monitoring communities for global smoke-related applications. Although the SSDA was developed using VIIRS data aboard S-NPP, this algorithm can be readily adapted to other sensors due to the common availability of blue or green channels as compared to smoke detection methods that are reliant on ultraviolet channels.  
Conceptualization and methodology, X.L. and X.Z.; validation and formal analysis, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L., X.Z., F.L., M.A.C., and P.C. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the NASA projects of MRV and REDD+, grant number 80 NSSC18K02 35 and 80 NSSC20K0408.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The manuscript contents are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NASA or the U.S. Government. The authors thank the NOAA CLASS for providing the datasets publicly including VIIRS SDR, VIIRS Cloud Mask EDR, and VIIRS Aerosol Optical Depth and Aerosol Particle Size EDRs; thank the European Space Agency (ESA) Copernicus for providing the Sentinel-5P TROPOMI aerosol index data freely; thank the NASA Direct Readout Laboratory (DRL) for providing the VIIRS corrected reflectance code publicly; thank the NASA Worldview website for enabling us to interactively browse the true-color images freely; and thank three anonymous reviewers' comments that improved this paper.  
The authors declare no conflict of interest.