## 5 Discussion
Finally, for smoke with pretty coarse particulates, such as part of the smoke released from Indonesia smoldering fires, the strongest Mie scattering may occur at red or longer bands, rather than blue or green band, which is likely to cause omission errors of smoke detection. The SSDA accuracy assessment (Figure 10) may have also been affected through uncertainties induced by evaluation datasets. First, the manually-delineating smoke reference data may propagate some uncertainties because boundaries of some fire smoke plumes are not easily identifiable, even visually, from true-color images. Similarly, mixtures or misidentification of smoke and circumulus clouds in selected reference smoke samples could have occurred, predominately in tropical regions. Although subset regions, instead of whole granules are used to produce smoke reference data (Figure 2), the accuracy assessment could potentially be affected by the difference in sample numbers for smoke and non-smoke classes, especially if the number of smoke pixels is much smaller than that of the non-smoke pixels. Second, TROPOMI smoke is not a mature operational product but produced by ourselves through combing positive UVAI values with smoke DSDI. The UVAI value may bias systematically due to calibration issues [68]. Additionally, we did not use quality control for TROPOMI UVAI to keep more values match with SSDA results. Third, global fire density is not an ideal dataset to evaluate SSDA smoke occurrence frequency because these two variables are impacted by different factors. Specifically, fire density is impacted by satellite detectability, where small and weak fires, understory fires, as well as cloud/smoke contaminated fires are not detectable, whereas, smoke frequency is influenced by smoke emission coefficients, wind conditions, and cloud contaminations. However, fire density is the most relevant variable associated with fire smoke occurrences. Moreover, it is near-impossible to evaluate smoke plumes under all conditions due to the challenges in obtaining high-quality smoke reference data. For example, smoke plumes over the ocean were not quantitatively evaluated due to the challenge of obtaining good reference data over ocean area pixels. However, SSDA smoke detections over water are visually comparable with true-color images (Figure 6h). Finally, the lack of a high-spatial-resolution smoke evaluation dataset is an additional challenge that compounds the complexity of smoke detection evaluation. Although many factors could lead to uncertainties in smoke detection, the improvements of SSDA detection have various implications. First, SSDA smoke detections could improve AOD retrieval accuracy and efficiency by using the SSDA detected smoke extent directly over the smoke area instead of looking for the \"optimal\" aerosol model by looping over five candidate aerosol models [69]. The SSDA smoke detections could also increase effective AOD pixel numbers because currently smoke pixels, close to or mixed with clouds and/or thick smoke, often have no AOD retrieval due to being misclassified as clouds [70]. Second, the SSDA smoke detection information could improve estimates of biomass burning emissions, especially over regions dominated by small and cool fires. Traditionally, biomass burning emissions are calculated based on fire detections, such as fire radiative power [71; 72; 73]. However, small and cool fires are easily missed by sensors due to the weak radiation energy or interceptions from the smoke plume, forest canopy, and clouds. In contrast, smoke plumes from such fires tend to stay near the ground in high concentrations [15; 74] that are observable by existing sensors. Therefore, combining the smoke plume extent and corresponding AOD retrieval may provide a novel way to estimate smoke aerosol emissions from biomass burning [19; 20; 26].