# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 5 Conclusions  
The Sentinel-2 constellation of Earth-observing satellites provides similar or even improved estimates of burn severity compared to Landsat 8, further validating its use for mapping fire effects across a diversity of forested ecosystems. Atmospherically correcting Sentinel imagery to Bottom of Atmosphere noticeably improved correlations with field measures of burn severity using the paired scene approach. The image-compositing approach, which required the use of uncorrected Top of Atmosphere Sentinel imagery due to data access limitations, still performed well relative to corrected Landsat imagery, suggesting that use of the composite approach with corrected Sentinel imagery may improve accuracy even further. Developing products that consistently process imagery from both missions to ensure compatibility for time series analyses, such as the Harmonized Landsat and Sentinel-2 [79], will ultimately streamline the process of integrating both data sources. This will have major benefits for burn severity mapping globally, increasing both the number of cloud-free scenes to choose from for the paired scene approach and the number of cloud-free pixels available to integrate for the image compositing method. Yet we caution against ignoring the increased spatial resolution that Sentinel provides. Mapping burn severity at true 10 m resolution with an NDVI-derived index, or pseudo-10 m with NBR, helps resolve some of the fine-scale heterogeneity of fire effects. There are opportunities to test Sentinel's ability to delineate unburned areas within a fire footprint, potentially improving estimates of burned area. Detection of fire refugia occurring at the sub-30 m pixel scale could aid in interpreting the ecological resilience of post-fire landscapes [34; 95], as well as reduce uncertainty in the amount of tree mortality for a given burn severity estimate [31; 87]. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14205249/s1](https://www.mdpi.com/article/10.3390/rs14205249/s1)]([https://www.mdpi.com/article/10.3390/rs14205249/s1](https://www.mdpi.com/article/10.3390/rs14205249/s1)), Figure S1: NLS model fits (\(R^{2}\)) for Landsat 8 and Sentinel-2 derived burn severity indices to field-based composite burn index scores using the image compositing approach. N = 40 models for Landsat and for Sentinel (5 indices \(\times\) 2 compositing statistics \(\times\) 2 calculation or not of an offset \(\times\) 2 composite imagery seasons). The 5 indices compared are (1) differenced normalized vegetation index (dNDVI); (2) relativized difference normalized vegetation index (RdNDVI); (3) differenced normalized burn ratio (dNBR); (4) relativized burn ratio (RBR); and (5) relativized differenced normalized burn ratio (RdNBR) (A). The compositing statistics are the method used to calculate the pixel value from the pre- and post-fire imagery stacks in Google Earth Engine (B). The offset calculates the mean index value from a 180 m band outside the fire perimeter for the pre- and post-fire image composites and uses the difference between the two as a correction value for all pixels inside the burn perimeter (C); Table S1: Paired scene approach scene acquisition dates. **Author Contributions:** Conceptualization, A.A.H., S.A.P. and L.L.Y. ; data curation, B.J.H., S.A.P., J.A.L. and L.L.Y. ; formal analysis, A.A.H. ; funding acquisition, A.A.H., B.J.H., J.A.L. and L.L.Y. ; investigation, A.A.H. and S.A.P. ; methodology, A.A.H., S.A.P. and L.L.Y. ; software, A.A.H. ; supervision, L.L.Y. ; visualization, A.A.H. ; writing-original draft, A.A.H. ; writing-review and editing, A.A.H., S.A.P., B.J.H., S.J.S., J.A.L. and L.L.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Utah State University Quinney College of Natural Resources Quinney Fellowship; the USU Office of Research; the USU Climate Adaptation Science Traineeship Program (NSF Grant No.