# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 4 Discussion  
In the most comprehensive assessment of burn severity mapping using Sentinel-derived imagery to date, we found that Sentinel generally performed as well or better than Landsat for mapping burn severity in western North American forests, lending further evidence to the complementary nature of both earth-observing missions in this context [25; 47]. However, nuances in the performance between products emerged when comparing different mapping methods, such as the date range used to select imagery for compositing (Table 4), as well as the effect of post-processing Sentinel imagery for atmospheric and terrain correction (Table 5). Emerging products, such as the Harmonized Landsat Sentinel-2 [79], seek to alleviate some of these issues by fully integrating imagery from both sources through a consistent post-processing chain. Although integrated datasets will be invaluable for long-term time series analyses and image selection options, they forfeit one of the major advantages of the Sentinel-2 MSI sensors over the Landsat mission; namely, higher spatial resolution. Our results show that mapping burn severity at higher resolution helps resolve some heterogeneity in fire effects masked at coarser scales e.g., [31; 32; 34], reducing the number of pixels classified as'moderate' severity (Figure 2C). Though the proportional differences in severity classes are small when mapping at Sentinel's higher resolution, these can have much larger impacts on metrics seeking to quantify the spatial configuration of severity patches. This highlights opportunities to assess the ecological interpretations of mapping burn severity at higher resolution at broad scales, but also warrants caution when comparing results to previous work at the commonly used 30 m resolution.  
Though we generally found similar regression model fits between field data and burn severity indices derived from Landsat and Sentinel compared to other studies in western North America [19; 53; 62], some studies reported even stronger correlations [61; 70; 80]. Discrepancies in model fit are likely due in part to the degree of pooling data from fires occurring in different ecoregions and vegetation types. [PERSON] et al. [15] assessed how the scale of the regression model affected burn severity estimate accuracy by comparing a model spanning the continental United States to models subset by increasingly narrow land cover classifications, finding that the more specific models generally improved model fit, particularly in the western US. Thus, it is likely we would observe stronger \(R^{2}\) values in our assessment if the models were further broken down regionally. Furthermore, it is important to note that classifying burn severity should ideally be conducted with ecologically meaningful thresholds calibrated to the vegetation community being analyzed [81]. Though we used a single threshold to classify stand-replacing fire across the fires we studied, this was done to simplify analysis and focus on the effect of imagery resolution. Cross-regional studies of burn severity patterns would ideally use regionally defined thresholds where they show improved fit [15], or use non-parametric methods, such as machine learning, to predict burn severity by integrating multiple spatially varying predictors e.g., [37; 51; 55].