## 5 Discussion  
This study develops a new algorithm (SSDA) for automated daily detection of fire smoke plumes using 750 m VIIRS data. It provides a complementary biomass burning dataset for fire-related products, including active fire [41; 63], burned area [64; 65], biomass  
Figure 11: (**a**) SSDA smoke occurrence frequency map obtained from daily SSDA smoke detections during August 2018; and (**b**) The \(1^{\circ}\times 1^{\circ}\) global fire detection density derived from VIIRS 750 m active fire observations during August 2018.  
burning emissions [48; 66], and aerosol data [67]. This newly developed algorithm has several advantages:  
1. The theoretical basis is the strong Mie scattering at blue and green bands caused by smoke aerosols. These two bands are more commonly available on satellite sensors than ultraviolet bands. 2. The corrected reflectance, instead of TOA reflectance, of the blue band is used, which enhances potential contrast between smoke and non-smoke areas because it reduces considerable impacts from Rayleigh scattering but keeps important information from aerosol scattering for the wavelength spectrum corresponding to smoke aerosol particulate sizes. 3. Using multiple criteria (spectral and spatial) in a stepwise way (4 steps) in the SSDA reduces potential commission errors and large uncertainties (see following paragraphs) as much as possible, since no unique features allow separation of smoke from other surface types. 4. The detection of thin smoke released from small fires is greatly improved, which could help people monitor fire development at early stages, and then take relevant actions to prevent major fire events from developing. 5. Detection of dense smoke emitted from large fires is also greatly enhanced with lower commission and omission errors in North America and Siberia, which could improve the accuracy of biomass burning emissions estimates by combining it with AOD or carbon monoxide observations using chemistry-transport models. 6. The method of smoke detection can be applied efficiently at a global scale. Potential uncertainties remain for SSDA smoke detection under such unfavorable conditions as extremely thin smoke, smoke-cloud mixtures, smoke-haze mixtures, or coarse particulate dominated smoke. Identification of extremely thin (semitransparent) smoke is difficult as the spectral signal becomes a variable mixture from both the smoke and the underlying Earth surface background. Semitransparent smoke plumes from diffuse, small grassland and savanna fires and around the edges of denser smoke plumes are consistently contaminated by background spectral signals, especially bright surfaces. Smoke is, therefore, likely to be misclassified as the bright surface if the optical spectral contribution from the bright surface is higher in the pixel. This is the main factor behind high omission errors for smoke detections of small plumes, as shown in southern South America and Australia (Figure 61,c1, and Figure 10d). In addition, detection of extremely thin smoke from crop fires is also challenging because the extent of such smoke is generally less than one VIIRS pixel size (750 \(\times\) 750 m\({}^{2}\) at nadir) and the returned optical signal is from a pixel mixed smokes and other cover types. Detection of smoke in pixels mixed with clouds is another challenge, which is a common phenomenon in tropical regions. Although smoke from fires passing above clouds is detectable, it is hard to separate them when plumes are below or mixed with clouds that contribute significantly to the spectral feature. This is a major reason for large omission errors in the tropics (Figure 10d). Moreover, differentiating haze, such as in Pakistan, northern India, and eastern China, from smoke (Figure 11) is also challenging because they both contain high concentrations of fine-size particles, which may lead to similar scattering effects in VIIRS blue and green bands [27].