# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 3 Results
### High-Severity Patch Interior and Severity Class Estimates between Fires  
Using the NLS models fit to the composite burn index (CBI) and satellite-derived burn severity indices, we calculated burn severity thresholds for RdNBR derived from the extended compositing approach for low (Landsat: \(\leq\)334; Sentinel: \(\leq\)314), moderate (Landsat: 335-664; Sentinel: 315-633), and high severities (Landsat \(\geq\)665; Sentinel \(\geq\)634). A confusion matrix was used to assess the predictive accuracy of the severity categorization compared to the field-based measurement, resulting in an overall accuracy of 66% and a Kappa of 0.485 for Landsat, and an accuracy of 69% and Kappa of 0.518 for Sentinel. In order to estimate high-severity patch interior, we further simplified the severity classes into high and not high (i.e., low and moderate) severity classes. This classification had an accuracy of 81% and a Kappa of 0.607 for Landsat, and an accuracy of 82% and Kappa of 0.631 for Sentinel. Differences between per-fire proportions of the three severity classes made by Landsat and Sentinel were non-significant (\(p>0.05\), Figure 2A). In contrast, the difference in the proportion of a fire mapped as high-severity patch interior (high-severity pixels 90 m or further from the nearest non-high-severity forest pixel) was significantly higher for Landsat as compared to the same fire mapped with Sentinel (mean 5.0%, \(p<0.001\), Figure 2A). This difference resulted in Landsat estimating 24,273 additional hectares (6.6% of forested area) of high-severity patch interior compared to Sentinel across all fires analyzed (Figures 2B and 3). Figure 2: Differences in the proportion of forest-classified pixels among the mapped fires for three severity classes (low, moderate, and high) and as high-severity patch interior high-severity pixels \(>\)90 m (from the nearest low or moderate severity pixel). Severity classes were derived from RdNBR  
calculated using the extended composite approach (see Section 2.4), and the difference in percent estimated for each class is shown between Landsat and Sentinel (**A**), and Sentinel at 10 m resolution and resampled to 30 m resolution (**C**). Each dot represents one of the 26 fires (the two smallest fires were excluded for the high-severity patch interior), and the bars show the mean \(\pm\) 1 standard deviation. The relationship between percent high severity and percent high-severity patch interior for each fire is shown as mapped by Landsat (red circle) and Sentinel (blue triangle) (**B**). Frequency plots of the continuous RdNBR values for the Hayes Two fire are overlaid as mapped by Sentinel at 10 m resolution (blue) and 30 m resolution (red), with the vertical dashed lines depicting the categorical breaks between low/moderate severity and moderate/high severity (**D**). Figure 3: Maps of high-severity patch interior for the 2017 Meyers fire in southwestern Montana (**A**), and the 2018 Trail Mountain fire in central Utah (**B**) derived from Landsat (left column) and Sentinel (right column) imagery. High-severity patch interior (red) was mapped from all high-severity pixels 90 m or further from the nearest low- or moderate-severity forest-classified pixel. Tan areas represent all other forest-classified pixels; white areas were classified as non-forest. For the Meyers fire, Landsat predicts 10.7% more high-severity patch interior than Sentinel (2801 additional hectares). For the Trail Mountain fire, Landsat predicts 11.0% more high-severity patch interior than Sentinel (710 additional hectares). Maps using Landsat imagery are at their native 30 m resolution, while Sentinel maps use 10 m resolution. Severity was derived from RdNBR measured using the extended compositing approach in GEE, with severity thresholds estimated from NLS models of the satellite-derived burn severity fit to field-based CBI measurements.