## 3 Methodology
### Smoke Detection Procedure  
The procedure for smoke detection followed the steps listed in Figure 5. Thresholds were initially derived based on both literature [54; 55; 56; 15; 51] and spectral and spatial features of training samples, as described in Section 3.1. We used relatively strict thresholds for dark surface, cloud, and bright surface detection, but relatively loose thresholds for smoke to detect as many potential smoke plumes as possible. The optimal thresholds were finally determined when we got the best overall accuracy through multiple sensitivity analyses. **Step 1: Dark surface detection**. The cover types with low blue reflectance values (M3 < 0.09) were classified as dark surfaces at the beginning since smoke, clouds, and bright surfaces all have relatively high blue reflectance (Figure 3). We also categorized vegetation, water, green coastal waves, and oceanic sun glint as dark surfaces (Figure 5). Specifically, pixels with NDVI greater than 0.5 and an M11/M3 ratio less than 0.05 were grouped into vegetation and water classes, respectively. Because green waves along coasts and sun glints over the ocean are easily confused with smoke, the green waves were separated using the high reflectance at the green band (FM = water and M4/M5 > 1.5). Oceanic sun glint was initially designated based on the oceanic glint from VIIRS CM and then further refined based on its spectral characteristics (FM = water and M11/M3 > 0.5 and BT > 290). Figure 4: The density distributions of (**a**) M11/M3 ratio and (**b**) brightness temperature at the M16 band for cover types of bright bare soil, cloud, and smoke. The training samples are the same as those of Figure 3.  
**Step 2:** Cloud detection. We did not use the VIIRS cloud mask (CM) product directly here because of its very high commission error. Similarly, we also did not use the cloud class from the VIIRS fire mask due to its relatively high omission error for thin clouds and cloud edges. Alternatively, we used two intermediate datasets of the VIIRS CM, cloud shadow and cirrus, as the cloud class for this study. Remaining cloud pixels were detected based on characteristically low reflectance at M10 compared with other bands, very high reflectance at short bands (M3-M7), a combination of low M11/M3 ratio, low brightness temperature, and high spatial standard deviation. Further, the cloud detection criteria developed in a contextual fire detection algorithm [54; 55] was also used in this study, but have been modified slightly by adding a brightness temperature threshold. **Step 3: Bright surface detection. We identified bright surfaces (bare soil and dust) based on near-constant increasing spectral reflectance of the first several bands (Figure 3b), as well as the combination of high brightness temperature and high M11/M3 ratio. For the bright surfaces of white sands and badlands, such as in southwestern North America, spectrums may be slightly different with bright bare soils. Therefore, we classified these areas as bright surfaces based on their own specific spectral features. Finally, we also grouped the snow class from VIIRS CM into the bright surface class. **  
Figure 5: Flow chart of the SSDA for smoke detection. FM refers to the VIIRS fire mask. CM represents the VIIRS enterprise cloud mask. **Step 4: Smoke detection**. Smoke was detected at the end to reduce interference from other cover types using its slightly higher or similar reflectance of blue or green bands relative to the red band, as well as the combination of relatively low M11/M3 ratio, relatively high brightness temperature, and low spatial standard deviation. Because of the complexity of smoke, with different aerosol size distributions, we also slightly adjusted the criteria of the smoke detection step to identify any potential smoke plumes using a slightly looser threshold for one criterion but stricter thresholds for the other criteria. In addition, we grouped fire hotspots from VIIRS fire mask into the smoke class.