## 6 Conclusions  
Wildland fires identified from the satellite-derived product FINN have been segregated into different burn types (prescribed, agricultural burns, or wildfires) by considering the land cover type of fire locations and fire durations. The burned area of prescribed burns using FINN-based wildland fire estimates is compared with the burned area of prescribed burning permits in Georgia, Florida, and South Carolina. Matching and comparisons between the burned area from FINN and permits are conducted at various resolutions. FINN burned area estimates have a low correlation (R\({}^{2}\) < 0.05) with permit estimates based on fire-to-fire matching, while the correlation is relatively stronger for grid-based (R\({}^{2}\) > 0.17) and statewide (R\({}^{2}\) > 0.55) matching. A linear regression model, developed using grid-based matching results, determined that the prescribed burned area from FINN needs to be reduced by 34% of the FINN burned area. Using the BlueSky framework with the adjusted burned area, prescribed burning emissions are estimated for the southeastern United States from 2013 to 2020. To understand the emission differences between FINN and BlueSky, we also ran BlueSky with unadjusted FINN burned areas. The comparisons between emissions from FINN and BlueSky indicate that the differences in emissions can be large for single fire events (R\({}^{2}\) > 0.38), but much smaller when considering emission estimates at a 4 km grid resolution (R\({}^{2}\) > 0.55), or when assessing statewide emissions (R\({}^{2}\) > 0.96). We also compare emissions estimated from permit burn areas and the adjusted FINN burned area using BlueSky to understand the uncertainty of prescribed burning emissions stemming from the potential use of different data sources. The linear regression model between adjusted FINN and permit emissions has a slope of around 0.94 with R\({}^{2}\) = 0.17. The result of this comparison indicates that the magnitude of emissions from the adjusted FINN burned area at a 4 km grid resolution agrees with those derived from permit burn areas. The methods we presented here are readily useable for burn-type differentiation, matching and comparison of the burned area between two datasets under various resolutions, and estimation of prescribed burning emissions. This study also benefits health studies related to prescribed burning since type-differentiated and more accurately estimated prescribed burning emissions from satellite products are needed to model the air quality impacts of prescribed fires. Those air quality impacts are essential to smoke exposure evaluation, which can be used for public health research and surveillance. The emissions data we produced can be readily used for air quality simulations and investigations of the health impacts of prescribed burning in the southeastern United States between 2013 and 2020. To improve the algorithms and emission products in this study, two main efforts should be considered. Firstly, the state-level burn permit data systems should be unified since it is difficult to obtain data from each state and the data formats vary from one state to another. Burn information in unified permit databases can be grouped into three categories by decreased level of importance. The first group should include latitude, longitude, start date, and burn area, which are the minimum requirements for most fire emission models. This information should be collected when fire managers apply for permits. Updates of burn area data after the burn with the actual burned areas can improve the understanding of the relationships between satellite-derived and ground-based data. The second group may include the start time, end time, and burn type, which can be recorded by the fire managers when they execute the burnings. Start and end times are valuable for generating diurnal time profiles of emissions in chemical transport models. Burn-type information is important for training and evaluating burn-type differentiation algorithms.