## 5 Discussion
### Burned Area Misclassification  
The use of dense multitemporal series of S2 products should provide post-fire observation close to the wildfire occurrence date and consequently reduce errors due to the SWIR variability resulting from charcoal removal [39] and to vegetation restoration [2]. Nevertheless, the numerical results exhibit a commission error of around 25% and a high omission error of around 40%. There are two main reasons that explain the large omission errors: i) the overdone removal of small areas by sieve filtering and ii) understated threshold values. Many small areas corresponding to burned areas were removed by sieve filtering and consequently were not used as seed regions when region growing was later applied, slightly increasing the omission error. Such drawback highlights the need for defining a better tuning of threshold values to handle the number of small areas, often representing small crop parcels that could significantly slow down the region-growing algorithm performances (which is computationally demanding). The comparison of the generated product against the reference products, created using different methodologies and scopes, highlighted some inconsistencies. In fact, numerical results demonstrate that the reference products could be useful for the evaluation of procedure performances, but they may not always be consistent with the generated product due to the different spatial resolution or the examined time spawns. Indeed, the EFFIS \"MODIS burnt areas\" product represents only a fraction (about 75-80%) of the total area burned [37] and contains only areas with a surface equal or larger than approximately 30 hectares in size. Rather, the Copernicus EMS fire grading product provides maps at finer spatial resolution with good geolocation accuracy but does not give a precise indication about the single wildfire occurrence dates; since it is generated from a single pair of pre-fire and post-fire observations, it represents only a small portion of the total burned areas in the European continent, and the methodology used to produce it does not allow a reasonable processing of large spatial extents. The Copernicus EMS and MODIS products represent in some cases the only available datasets to be used as references for the validation of burned area detection algorithm performances, although they may not ensure the objective information standards required for proper product accuracy reported in [61]. The use of S2 spectral information allows one to map burned areas at a 20 m spatial resolution and to identify small burned areas to large spatial extents. Nevertheless, the drawback of fine scale mapping is the generation of a considerable number of false positives, especially in ploughed lands that exhibit relatively high spectral index values. Misclassification problems at low severity pixels are common and have been reported by various studies [7; 25; 26; 32; 59]. Unshown fields show very similar spectral and temporal behavior to burned areas, especially the dark soils. Previous works report that more than half of the commission error was concentrated in agricultural lands due to the presence of unsown fields which are misclassified as burned pixels [25; 26; 59]. Here, the comparison of the misclassified pixels (here only misclassified pixels with respect to the EFFIS product are used) with a 2012 CLC map revealed that around 40% of the misclassified pixels correspond to the agricultural land classes and around 20% of the pixels belong to mixed and broad-leaved forest (Figure S5). Such results indicate that there could be pixels corresponding to burned areas in forests regions that were not mapped in the EFFIS product and have rather been correctly classified by the procedure.