## 3 Methodology
### Design of Detection Criteria  
The multiple criteria for the SSDA development were designed based on the spectral and spatial features of training samples. Firstly, we identified various cover types based on their spectral reflectance characteristics. Because the blue reflectance (M3) of clouds, bare soil, dust, and smoke aerosols are all greater than 0.1 (Figure 3), we defined all other cover types with a low reflectance in the blue band as dark surfaces that were eliminated first. Among the classes in Figure 3, clouds have the unique spectral characteristic of lower M10 reflectance than the M11 band (Figure 3a). This signature may be due to the high microscopic liquid water content in clouds that is not present in bright surface, smoke, or dust samples. In addition, reflectance values of the first four bands (M3-M7) in cloud spectrums are much higher than other cover types (Figure 3a). For both bare soil and dust classes, their common spectral signature is the significantly increasing reflectance values, ramping with increasing wavelengths of the first six bands (M3-M10) (Figure 3b). This feature is relatively stable for many bright soil types and consistent with previous reports [49] (Figure 3b). We grouped bare soil and dust classes into the bright surface category. For smoke, the unique spectral feature is the slightly higher reflectance of blue and green bands than the red band (Figure 3c). This phenomenon is mainly caused by Mie scattering dominating among the smoke aerosols. Mie scattering reaches peak efficiency for particles with diameters similar to wavelengths of the incident light and it is much stronger than Rayleigh scattering [50]. Since reported smoke particle sizes [33] are much closer to the wavelengths at shorter optical bands, Mie scattering leads to relatively high reflectance values in green and blue bands for the smoke spectrum. This feature could also help distinguish smoke aerosols from other aerosols with much coarser particle sizes, such as dust aerosols.  
Figure 3: The spectral signatures of (**a**) cloud, (**b**) bare soil, (**c**) smoke, and (**d**) dust derived from training samples. The horizontal axis labels of M3, M4, M5, M7, M8, M10, and M11 represent VIIRS moderate-resolution bands (Mband) centered at 0.488 μm (blue band), 0.555 μm (green band), 0.672 μm (red band), 0.865 μm, 1.24 μm, 1.61 μm, 2.25 μm (near-infrared bands) (Table 1), respectively. N is the pixel number of training samples from each cover type.  
Second, the cover types were further separated based on a combination of the reflectance ratio M11/M3 and the brightness temperature (BT) at the M16 band (Figure 4). Theoretically, the M11/M3 ratio should be low for clouds and smoke plumes because of their relatively high reflectance at M3 and low reflectance at M11. The low reflectance at M11 for these two cover types is caused by less Mie and selective scattering by particles if the particle size is far smaller than the wavelength at longer bands, such as M11 at 2.25 \(\upmu\)m [27]. Further, the brightness temperature of smoke is significantly higher than clouds and marginally lower than bare soil. Therefore, besides using spectral reflectance characteristics, smoke was also detected based on M11/M3 ratio and brightness temperature values: clouds were distinguished using very low brightness temperatures, and bright bare soil surfaces were separated using significantly higher M11/M3 ratios.  
Finally, the normalized difference vegetation index (NDVI) and spatial standard deviation of blue band reflectance (StdM03) were also used as additional criteria in the detection procedure. The NDVI helps separate smoke from dense vegetation due to the lower NDVI value of smoke [51; 52]. The spatial standard deviation helps to further distinguish smoke from clouds because clouds always have a significantly high spatial standard deviation [28; 53].