## 4 Results
### Matching Prescribed Burning Records in FINN with Permits  
We extracted prescribed burns from FINN and permits by the burn-type differentiation algorithm. To understand the relationship between the burned area from FINN and permits, we matched records at different spatial scales. Statewide matching showed a relatively strong correlation (R\({}^{2}\) > 0.55) (Figures 3 and S4). Of note, the slope was higher than 1 in Florida and was lower than 1 for South Carolina and Georgia for both linear regression models, indicating that FINN underestimated the prescribed burned area in Florida and overestimated it in Georgia and South Carolina. The positive intercept in the linear regression model showed that FINN underestimated burned area when the daily burned area was small. Additionally, the temporal pattern of prescribed burns from FINN was consistent with permits (Figure S5). The peaks of the burned area were around February to May for these three states. Although the regression model performed well when we matched FINN and permits in a coarse spatial scale, FINN and permits had poor correlation when we conducted a fire-to-fire matching with or without distance and date relaxation (Algorithms S2 and S3). For the nearest distance matching algorithm, we changed the selected distance threshold and evaluated the matching performance by R\({}^{2}\) (Figure S6). The R\({}^{2}\) was less than 0.05 even when we only considered a distance of less than 500 m between the FINN record and permit as a matched pair. The poor performance was partly due to uncertainty of location and start date for the permits, especially in Georgia, where coordinates of burns were estimated from ambiguous descriptions of addresses. Additionally, using distance as the single metric for matching had high uncertainty when there was more than one fire in FINN close to a permit record, and vice versa. Hence, we allowed some relaxations on the start date and included differences in the burned area as another metric for matching (Algorithm S3). By tuning the distance and start date, the matching performance was better than the previous method but was still poor (Figure S7). The disparity of performance between statewide matching and fire-to-fire matching showed the matching performance was sensitive to spatial resolution. The \"segmentation\" of fires where FINN may detect several fire points for a single prescribed burning event, especially for larger burns, can help to explain why the matching performance is sensitive to spatial resolution. The burned area detected by FINN should be calculated by summing all burned areas from all fire points related to a specific fire event. Grid-based burned area matching was considered since it can partly mitigate the issues due to the segmentation of FINN and the uncertainty of FINN or permit locations. Additionally, grid-based emissions, which are derived from the grid-based area, can still be  
Figure 3: Statewide matching between FINN and permits in Florida, South Carolina, and Georgia. A linear regression with an intercept was conducted to fit the FINN burned area and permits burned area. The numbers of matching days were indicated as N values. Uncertainty of linear regression parameters was reported with a 95% confidence interval. Florida and South Carolina permits cover 2013–2020. Georgia permits cover 2015–2020. (State abbreviations: GA: Georgia; FL: Florida; SC: South Carolina). utilized in chemical transport models. We aggregated point-wise FINN and permit records to a 4 km grid definition. Burns that were detected from both FINN and permits at the same date and the same grid were considered in the grid-based matching. Additionally, a sampling method that used an average value of 3 by 3 grids to represent the value of the center grid was employed to mitigate the impact of different grid definitions. The grid-based burned areas from FINN and permits had a similar spatial pattern in Florida, South Carolina, and Georgia (Figures 4 and S9).