## 5 Discussion
### Comparison of Spectral Indices for Burned Area Mapping  
Among the three spectral indices used in this research study, the NBR reported the best classification performance based on the accuracy assessment statistics. Nevertheless, it should be noted that the comparison was done against Copernicus EMS reference products that likely use NBR-based approaches for mapping burned areas. For this reason, a proper comparison of the spectral indices performances (and other spectral indices not considered in this research study) should be done with a reference map produced with different methodologies, and in consideration of the small differences among the classification performances of the three indices. Distribution of spectral index values in the identified burned areas with respect to the Copernicus EMS fire grading classes gives an overview of the indices' spectral separability (Figure S3). Estimation of spectral separability of NBR against BAIS2 was similar to that of the two spectral indices in a previous work [2]. Density plots show that BAIS2\(\alpha\) has a higher spectral separability than the other indices. Higher spectral separability of the BAIS2 compared with the NBR was also estimated [2], and a weak separability at low fire severity levels was highlighted. The comparison of difference indices against the fire grading classes shows a lower separability (Figure S3)--likewise for the dLAI (Figure S4). From a comparison with a 2012 Corine Land Cover (CLC) map, the NBR index misclassified around 10% more pixels belonging to the \"non-irrigated arable land\" class (Figure S5) than the other spectral indices, suggesting that the BAIS2 and BAIS2\(\alpha\) could generate fewer commission errors in arable lands. The S2 MSI sensor (see Table S1 for details) records data in the vegetation red-edge spectral domain, which is one of the best radiance-based descriptors of chlorophyll content [38]. Such spectral characteristics make S2 a valuable instrument for post-fire monitoring [34], with a great potential for discriminating burn severity levels in a fire [39]. Further, the adoption of the narrowband NIR (B8A) instead of broadband NIR (B8) in the calculation of spectral indices for post-fire has been already demonstrated not to lead to significant differences [39]. The BAIS2 [2] and BAIS2\(\alpha\) spectral indices used for the identification of burned areas benefit from both vegetation properties described in the red-edge spectral domains and the radiometric response in the SWIR spectral domain, largely recognized to be efficient in the determination of burned areas. The use of a SWIR band positively increasing the BAIS2 index reduces errors due to the saturation of a radiometric signal, found at shortwave wavelengths for pixels corresponding to active fires at the time of image acquisition. In addition, red-edge spectral bands can reduce errors due to the SWIR variability resulting from charcoal removal [39], especially when the first available post-fire satellite acquisition is distant in time from the pre-fire acquisition. In this research study, the average time span between the pre-fire and post-fire satellite observations was 11 days. The spectral indices combine bands from spectral regions with different sensitivities to factors including the vegetation type and condition, the soil background, atmospheric contamination, and the degree of char and ash deposition [43]. The introduction of spectral bands representing portions of the electromagnetic spectrum related to the vegetation properties could reduce errors related to the background or environment. In addition, the adoption of surface albedo as scaling information applied to the BAIS2 index should reduce the ambiguity with the spectral response of plugued land and contributed to decreasing commission errors for arable lands, even if it did not strengthen the detection at low fire severity levels.