# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 1 Introduction  
Mapping the landscape effects of wildfires advanced rapidly over recent decades as technological improvements in remote sensing and computing power synergized with unparalleled data access to enable research and monitoring efforts. Remotely sensed data are used to investigate diverse areas of inquiry, such as fire risk [1], burned area and severity mapping [2, 3, 4], detection of active fires [5, 6], post-fire erosion risk [7], and vegetation recovery [8, 9]. Burn severity, or the degree of fire-induced change due to consumption of organic matter [10], is commonly used by managers and researchers to assess the ecological impacts of fire at fine scales and across broad extents [11]. Remotelysensed metrics of burn severity are commonly derived from multispectral satellite imaging platforms [12], with widespread use of Landsat imagery [13; 14; 15] due to its global coverage, multi-decadal continuity, and open data policy beginning in 2008 [16]. Spectral burn severity indices, such as the differenced normalized burn ratio (dNBR) [11], measure differences in spectral reflectance from pre- and post-fire imagery to detect surface changes, including decreased vegetation cover and moisture, and increased exposure of ash, char, and bare soil [17; 18]. Validations of the dNBR and related indices with a commonly used field-based measure of burn severity, the composite burn index (CBI) [11], generally show good correlation within various regions of North America [19; 20; 21], as well as to independent field measures of burn severity [22], though the strength of the relationship differs between regions and land cover types [15]. Still, the general utility of Landsat-derived burn severity mapping led to its widespread implementation, including the development of national burn severity atlases, such as the Monitoring Trends in Burn Severity (MTBS) program in the United States [23; 24] and the Canadian Landsat Burn Severity (CanLaBS) product [14]. Recently, the addition of the Sentinel-2 constellation (hereafter referred to as Sentinel) to the network of Earth observation satellites brings new opportunities to improve burn severity mapping [25]. Consisting of two platforms sharing the same Multi-Spectral Instrument (MSI) sensor, Sentinel is designed to be complementary to the Landsat mission, but has several notable differences that make it appealing for burn severity mapping. When compared to the Landsat 8 Operational Land Imager (hereafter referred to as Landsat), differences include (1) a higher spatial resolution of the bands used for dNBR and related indices (10 m _NIR_ and 20 m _SWIR_ compared to 30 m for Landsat), (2) an increased temporal resolution (combined constellation revisit time average of 3 to 5 days compared to 16 days for Landsat), and (3) a greater spectral resolution (13 spectral bands compared to 9 bands for Landsat). In particular, the higher return interval of Sentinel offers more scene selection options, improving the ability to match phenology between pre- and post-fire images, which is often a challenge in areas with frequent cloud cover or rapid vegetation regrowth following fire [26; 27]. Additionally, Sentinel's higher spatial resolution offers the potential to improve ecological interpretation of heterogeneous burn severity effects that occur at spatial scales finer than Landsat pixels [28; 29; 30]. These effects include high sub-pixel variability in tree mortality between similar values of remotely sensed burn severity [31], as well as the presence of small unburned areas, termed fire refugia, which are effectively hidden due to the effects of spectral averaging across the scale of a Landsat pixel (i.e., 900 m\({}^{2}\)) [32; 33; 34]. Due to the importance that distance to the nearest seed source can have on post-fire forest recovery [2; 35; 36], detection of small refugia containing surviving seed-bearing trees could alter predictions about recovery following wildfire.