## 1 Introduction
Threshold-based classification of the normalized burn ratio difference (dNBR) [28] has turned into a methodological reference to obtain burn severity maps [29]. The adoption of such difference indices from multitemporal observations has been demonstrated to gather better results when compared to a single temporal observation because they rely on the estimation of changing vegetation cover [2]. The burned area index for Sentinel-2 (BAIS2) [2], the relative differenced normalized burn ratio (RdNBR) [30], and the relativized burn ratio (RBR) [31] have been developed and compared using pre-fire and post-fire satellite acquisitions [2; 32]. However, no agreement exists so far on which index performs best in the detection of fire-affected areas or in the estimation of fire severity, and no clear standards have been provided to indicate which methodologies can be followed in consideration of specific conditions [33]. Operational services based on the methodological references for burned area mapping have been developed to provide near-real-time information on wildland fires. Through an activation request, the Copernicus Emergency Management Service (EMS) delivers high spatial resolution wildfire maps generated from satellite data to determine the perimeter of the fires and the distribution of four fire severity levels [34; 35; 36]. In addition, the EFFIS (European Forest Fire Information System) [37] supports the services in charge of the protection of forests against fires in the European Union countries with updated and reliable near-real-time information on wildland fires at mid-low spatial resolution based on optical and thermal satellite data. The high availability of optical satellite data, using, for example, the MultiSpectral Instrument (MSI) sensor aboard Sentinel-2 (S2) satellites, acquiring information at medium-high spatial resolution, should enable the development of more detailed post-fire mapping. The advent of the S2 MSI sensor, equipped with specific spectral bands to record data in the vegetation red-edge spectral domain, which is one of the best radiance-based descriptors of chlorophyll content [38], allowed for the development and application of new spectral indices to discriminate burn severity. Recent studies have successfully assessed burn severity using S2 data through the comparison of pre-fire and post-fire satellite acquisitions [32; 34; 39; 40; 41; 42]. Other contributions also showed the suitability of already existing red-edge spectral indices for discriminating burn severity [39] and highlighted the added value of the S2 MSI spectral band configuration [41; 43] for burned area detection. Such novel features suggest the potential and need for further research to develop a systematic S2 MSI burned area mapping capability. Furthermore, the fire remote sensing research community has encouraged the exploitation of Earth Observation data available under the Copernicus program for forest fire management [44]. Despite the high availability of freely available satellite data, there are still very few examples of operational services for medium-high resolution wildfire mapping at the national level based on new satellite sensors. Key prerequisites to map areas disturbed by wildfires in an accurate and prompt way comprise, in addition to the exploitation of the entire satellite time series, the development of an automated and transferable procedure that is feasible without any a priori information, which leads to the delivery of rapid and updated information about fire-affected areas. This research study presents a novel procedure for identifying burned areas from dense multitemporal series of S2 data. The procedure has been designed on the basis of state-of-the-art algorithms for burned area detection and tries to cope with issues related to the misclassification of burned pixels, as reported in recent scientific literature.