## 4 Results
This reduction is much less apparent for the longer wavelength bands, because, as illustrated in Figure 3, the atmosphere has less effect at longer wavelengths. Residual atmospheric contamination may remain in the SEN2 COR generated surface reflectance, particularly at shorter wavelengths [66]. Figure 3: Example 40 \(\times\) 40 km\({}^{2}\) pixel subset of the second Sentinel-2A MSI Canada image acquisition (12 May 2016) (**a**) true color (b04, b03, b02) 10 m top of atmosphere reflectance; and (**b**) SEN2 COR atmospherically corrected equivalent bands displayed with the same logarithmic contrast stretch; (**c**) false color (b12, b11, b8a) 20 m top of atmosphere reflectance; and (**d**) SEN2 COR atmospherically corrected equivalent bands displayed with the same contrast stretch. Figure 4: Transformed divergence (TD) top of atmosphere (TOA) separability values for the ten MSI bands (Table 2) and the six spectral indices (Table 3). Figure 5: Transformed divergence (TD) separability values as Figure 4 but derived from the surface reflectance data (i.e., from the SEN2 COR atmosprically corrected data). With regard to the unburned to unburned control pixels, the separability for each band and spectral index is generally quite low. This is expected because the images were acquired 10 or 20 days apart and so change in the surface condition (e.g., vegetation state and soil moisture changes) at the unburned pixel locations is likely to be small. With regard to the unburned to burned pixels, a clear TD separability pattern among the different bands and indices is evident. This pattern is similar to the decision tree classification results. As noted previously, the TD is indicative of class separability if probability distribution classification approaches (i.e., maximum likelihood) are used and if the data are normally distributed. As the data are not normally distributed (Figure 2), the decision tree kappa and overall accuracy results provide a more precise depiction of class separability, and these are discussed below. Figures 6 and 7 illustrate the decision tree separability results for the TOA and the SEN2 COR atmospherically corrected data respectively. In these two figures the overall accuracy and kappa values are very similar (across the ten bands and six indices the values are highly correlated with r \(>\) 0.98 for both the illustrated TOA and surface reflectance results). The surface unburned-burned kappa and overall accuracy values are always higher than the control unburned-unburned equivalent values (Figure 7) which is expected. This is also the case for the TOA results (Figure 6) except for the blue band over Australia, due to strong atmospheric effects in the shortest wavelength blue band. Considering the ten different MSI bands, the visible bands (b02, b03, b04) have the lowest unburned-burned separability (both overall accuracy and kappa) for all the sites (Figures 6 and 7). The longest wavelength MSI band (b12: 2190 nm) also has low separability, except for the Canadian site TOA and surface reflectance where the burned reflectance was much higher than the unburned vegetation which has been observed by others for boreal and temperate forested regions [17; 36; 67]. High overall accuracy (>0.8) and kappa (>0.8) values are found for the red-edge (b05, b06), NIR (b07, b08, b08a) and the SWIR (b11: 1610 nm) bands. It is well established that the NIR has high burned-unburned separability due primarily to the greater NIR reflectance of green and dry vegetation compared to black char [15; 23; 68; 69]. The high SWIR band burned-unburned separability is less established, and, for example, has been found to vary geographically [23] and is suggested by some researchers as being related to the removal of water-retaining vegetation post-fire that leads to an increase in SWIR reflectance [70; 71; 72]. The high red-edge band burned-unburned separability, particularly evident for b06 (740 nm), is of great interest as this band is not present on most spaceborne sensors.