# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Evaluating Remotely Sensed Indices of Burn Severity  
We compared all burn severity indices to CBI field data of burn severity for the 26 fires. For each CBI plot, we extracted burn severity values from the computed severity imagery using the area-weighted values of pixels within a 15 m radius of each plot center (the size of a CBI plot). We evaluated the correlation between each remotely sensed measure of burn severity and field-measured CBI using Nonlinear Least Squares (NLS) regression of the form:  
\[y=a+b\times\exp(CBI\times c) \tag{8}\]  
where \(y\) is the remotely sensed severity index being evaluated, \(CBI\) is the field-based measure of burn severity, and \(a\), \(b\), and \(c\) are the coefficients being estimated. NLS models were fit to all burn severity indices, except for two models, which failed to converge and a linear model was instead fit to the data [19]. We quantified the strength of the relationship between each burn severity index and CBI by calculating the coefficient of determination for each NLS or linear model (i.e., the \(R^{2}\) of the linear regression between the predicted and observed values of CBI), as well as the normalized Root Mean Squared Error (\(RMSE\)). Each NLS or linear model was fit to the full dataset of 912 CBI plots across all 26 fires, as the number of CBI plots was highly variable between fires (Table 1), limiting the ability to compare burn severity indices on a per-fire basis. Models and model statistics were computed in the statistical environment R version 4.1.2 [71]. Assessing the Effects of Imagery Resolution on Estimates of Categorical Burn Severity Classes and High-Severity Patch Interiors within Fires  
We explored the difference between Landsat- and Sentinel-derived burn severity maps classified into categorical fire severity groupings of low, moderate, and high severity for the forested pixels within each fire. To isolate the effects of imagery resolution, we additionally compared Sentinel burn severity maps at 10 m spatial resolution to the same maps at 30 m resolution using mean resampling. We used RdNBR calculated with the extended composite method (Table 3), as it showed relatively high correlation with field-based CBI plots for both Landsat and Sentinel (Table 4). Threshold values of RdNBR for each severity category were estimated based on the NLS models of CBI data fit to the Landsat and Sentinel burn severity maps, with commonly used severity breakpoints in the CBI scale as follows: 0 to 1.25 for low severity, 1.26 to 2.25 for moderate severity, and 2.26 to 3 for high severity [62]. We estimated the burn severity class predictive accuracy derived from Sentinel and Landsat imagery with a Kappa statistic calculated from a confusion matrix using the R package _caret_[72]. In addition to the percentage of each fire burned by severity class, we estimated the percentage of forested area within each fire that was more than 90 m from the likely surviving forest edge (low or moderate severity pixels), hereafter referred to as 'high-severity patch interior'. These regions within fires have received increasing attention due to concerns for natural conifer regeneration being limited by the distance to the nearest seed source [2; 35; 73]. Though seed dispersal distance is highly variable by species and environmental context, approximately 100 m from a seed source is a common threshold beyond which conifer seedling regeneration density is sparse following disturbance [74; 75; 76]. We chose a 90 m distance threshold because it was a common multiple of both Landsat and Sentinel pixel sizes (i.e., 30 m and 10 m, respectively). Because not all regions mapped as low or moderate severity within a fire perimeter are potential conifer seed sources (i.e., forested), we applied a 'forest mask' to exclude pixels unlikely to be forested pre-fire before calculating high-severity patch interiors and severity class percentages for all fires. Our forest mask was similar to that used in [77], which used a combination of pre-fire canopy cover and NDVI [1].