# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Calculating Burn Severity: Composite Approach  
In addition to the paired scene approach for burn severity mapping, we also used an image compositing approach in the cloud-based remote sensing platform Google Earth Engine (GEE) [67]. Image compositing uses an imagery stack from a user-specified date range before and after the fire to compute a per-pixel mean value from the imagery time series [50; 53]. We additionally tested two modifications to the compositing approach, including: (1) computing the per-pixel median, rather than mean, and (2) calculating a severity offset to account for phenological or moisture differences between pre- and post-fire years [11], which was calculated as the difference between the pre- and post-fire mean burn severity of unburned pixels 180 m outside of the fire perimeter. The offset value for each fire was then subtracted from the burn severity raster of each fire following the methods of [50]. However, we found no significant difference in correlation with CBI field data using the median instead of the mean (Figure S1B), while using the offset resulted in decreased correlation with CBI values compared to no offset (Figure S1C). Thus, we chose to exclude the offset and median composite from further analyses. One complication of comparing Landsat and Sentinel using the composite imagery approach arises from differential access to atmospherically corrected imagery collected by each sensor. We used the Landsat 8 Surface Reflectance Tier 1 dataset and the Sentinel-2 Level-1C TOA dataset, which combines imagery collected by both Sentinel-2A and -2B. Although the European Space Agency (ESA) now produces an atmospherically corrected BOA product, Level-2A, this dataset was not processed globally until approximately the fall of 2018, inhibiting its use in this study for the compositing approach to burn severity mapping. We instead tested the Level-2A product using the paired scene approach (see Section 2.3), as the reduced number of scenes requiring atmospheric correction compared to the composite approach made this feasible to accomplish, given computation limitations. In order to filter out pixels with cloud, snow, shadow, or water, we used the quality assessment band of Landsat 8 to mask contaminated pixels from each image before computing each pixel's composite value. Although the Sentinel-2 Level-2A product's quality assessment band contains flags for all the contaminants listed above, the Level-1C product's quality assessment band only allowed for masking of cloud and cirrus covered pixels. We tested two different \"image season\" date ranges for the pre- and post-fire imagery compositing (Table 3). The \"extended composite\" method uses a static date range starting on 1 June and ending on 30 September for both the year before and the year after fire. This choice of date range reflects the timeframe when scenes were selected for the paired scene assessment of these fires by MTBS (see Table S1 for scene dates used in the paired scene analysis), and is the range used by [PERSON] et al. [50] for fires occurring in the same regions. The pre-fire composite image also includes imagery occurring during the year of fire between 1 June and the start date of the fire, which was done to ensure the availability of Sentinel scenes for 2016 fires. In contrast to the extended composite imagery season, the \"hybrid composite\" combines post-fire imagery from both the year of fire (i.e., an immediate assessment) with imagery from the year after the fire (i.e., an extended assessment) [11]. The goal of the hy