## 5 Discussion
### Burned Area Detection Procedure  
In order to ensure the robustness to the results, a two-phase procedure [25; 26] was adopted in order to generate low commission errors when finding core burned areas in the first phase while reducing the omission errors using a region-growing algorithm in the second phase. In fact, Step A was able to generate around a 21% omission error, while Step C decreased the commission error by around 25%. A similar approach has been implemented as a service on the Geohazard Exploitation Platform of the European Space Agency and makes use of Sentinel-1 SAR data [40]. Main differences in the two-phase algorithms described in literature [25; 26] are the use of multitemporal threshold-based classification criteria for the seed region detection, without the need of a large database of burned and unburned reference pixels, a sieve filtering step, or an OBIA step to further refine mapped pixels and to reduce commission errors.  
Step B, the sieve filtering, was more critical in the data analysis, since there were many small patches of misclassified pixels identified at Step A that needed to be removed. While removing around only 2% of the correctly classified pixels, Step B helped to remove around 45% of core burned area patches, most of them not representing burned areas. Though the sieve filtering helps to largely reduce false positives identified in Step A, the following procedure step could not apply the region-growing algorithm to those burned areas that resulted in a small number of core burned area pixels in the first phase, as shown in previous studies [57]. Step D, the OBIA filtering, was not able to significantly reduce the commission errors and in some cases removed correctly classified patches, highlighting the need for further investigation in order to identify new procedures for improving the classification results.  
Based on a comparison with performance results obtained in previous research studies [24; 25], which reported an omission error of around 10-15%, it should be said that the present work did not use any training set and made use of the entire time series of S2, including large temporal periods characterized by ploughed land cover. Further investigations are needed to improve the procedure and reduce the omission error by integrating, for example, various spectral indices and by performing an optimal tuning of the threshold values for the required parameters.  
The comparison of the identified post-fire dates with respect to the reference products showed that the procedure could provide burned area maps on average one day before the products generated by Copernicus EMS rapid mapping activations could. Moreover, results suggest that an operational service based on the developed procedure could provide medium-high resolution burned area maps on average five days after the post-fire dates reported in the EFFIS product, which is a relatively small time span if one considers that the S2 revisit time is five days with a two-satellite constellation.  
The proposed procedure is composed of four steps and mainly relies on expert-derived rules and thresholds. While this practice ensures the robustness of results, it might limit the exportability of the procedure to other geographical areas and ecosystems. On the one hand, threshold adaptation to different regions requires extra effort [11]; on the other hand, empirical thresholding provides more generalization than other methodologies and does not require a large set of training areas. Expert-derived rules and thresholds adopted in this research study therefore should be considered applicable to the Mediterranean ecosystem, while their reliability should be evaluated when applied to different geographical areas and a variety of ecosystems. Further development of the methodology may investigate the use of dynamic adaptive thresholding procedures, as already attempted by previous studies [11; 59], taking also into account the plant functional types existing in different geographic regions.