## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel
### Landsat-8 and Sentinel-2 Burned Area Mapping  
Mapping the spatial extent of fire-affected areas on a systematic basis is needed in support of numerous science applications, and in particular for estimation of pyrogenic emissions of greenhouse gasses and aerosols. Landsat data have been used for burned area mapping since the availability of the first Landsat 1 data [33; 34]. However, Landsat-based burned area mapping is significantly limited by the low Landsat temporal revisit, combined with cloud cover, and, in many regions, by rapid post-fire vegetation regrowth and dissipation of char and ash [35; 36]. In the last two decades global burned products have been produced using coarse spatial resolution data [37], notably, using change detection algorithms that take advantage of the near daily MODIS 500 m observation record [38; 39]. The Sentinel-2 MSI has spectral bands that are well suited for burned area mapping [40] and with Landsat 8 OLI provide the opportunity for medium-resolution burned area mapping using change detection algorithms, particularly as both Sentinel-2 data streams with Landsat 8 provide a global median average revisit interval of 2.9 days [41]. As noted for the HLS project, there are a number of pre-processing issues required before Landsat-8 OLI and Sentinel-2 MSI data can be used seamlessly together. Significant progress has been made, however, although there are still ongoing issues. This is illustrated briefly below in support of a NASA-funded project to develop burned area products using Collection 1 Landsat 8 OLI and Sentinel-2 MSI data. Rather than use the Sentinel-2 MSI tiling system, that is complex to use for large area applications as adjacent tiles from the same MSI swath overlap spatially and may be defined in different UTM zones [42], both sensor's data are reprojected into the MODIS sinusoidal projection [43]. In this way, the reprojected data are straightforward to compare with the standard MODIS land products (e.g., [44]) and burned area estimates are defined without areal bias as the MODIS projection is an equal area projection. The Sentinel-2 and the Landsat-8 data are registered to sub-pixel precision using affine transformations derived with a robust matching algorithm developed for this purpose [45] and via a least-squares adjustment among different orbits to reduce sensitivity to missing and cloudy data typically found when registering only individual tiles and images [46]. Research to downscale Landsat 8 30 m data to 20 m Sentinel 2 MSI resolution is underway [47] but currently the MSI 20 m data are upscaled to 30 m by bilinear resampling and the Landsat 8 Collection1 data are also bilinear resampled. Both sensor's data are atmospherically corrected using the LaSRC algorithm as described above for the HLS data. The Landsat-8 Collection 1 cloud mask [48] is used to discard cloud-contaminated pixels. The Sentinel-2 L1C cloud mask currently performs poorly, and instead the SEN2 COR cloud mask [49] is used for Sentinel-2 application. Research to evaluate shadow masks in the Landsat-8 Collection 1 and Sentinel-2 L1C products is underway. Bi-directional reflectance variations imposed by variations in the viewing and solar geometry occur over most terrestrial surfaces and, for many applications, are considered as a source of noise. Surface reflectance anisotropy, has been observed to cause NIR reflectance variations up to 0.06 (reflectance units) across the Landsat swath [27] and up to 0.08 across the Sentinel-2 swath [28]. Although these variations are much smaller than observed for MODIS data,where they can be greater than reflectance changes due to biomass burning [50], they are still not insignificant. Therefore, the surface reflectance data are corrected to nadir BRDF-adjusted reflectance (NBAR) using a semi-empirical \(c\)-factor approach that provides a first-order BRDF correction [27; 28].