## 4 Methodological Approach
### Study Site 1
#### 4.1.1 Relativized Burn Ratio (RBR) Computation  
Sentinel-2 data was used to assess the wildfire burned area severity and extent with a particular focus on vegetation. A multitemporal approach has been adopted to compare the evolution of vegetation recovery and resprouting, and to assess the RBR index temporal variation. Multitemporal observations gather better results compared to singular observations as they rely on a non-affected data as a baseline to further asses change. The computation of the index has been handled on SNAP software. Analysis was performed on twelve Sentinel-2 L2A images ranging from 6 July 2017 (pre-fire acquisition) to 3 August 2019 (Table 3), with a time step of three months. Atmospheric correction was not necessary as Level 2A data were already calibrated for TOA and BOA reflectance. Nevertheless, to obtain a more robust result, the data were filtered to remove clouds and aerosol, elements that influence the index sensitivity and reliability. To avoid any excessive data corruption,  
Figure 2: Case study 1: (**a**) location of the study site in Portugal; (**b**) digital elevation model of the study area 25 \(\times\) 25 m [73], (**c**) Copernicus sentinel hub burned area [74], (**d**) 2012 Corine land cover [75], legend accessible at [76]. Case study 2: (**e**) location of the study site in Italy; (**f**) digital elevation model, (**g**) Google earth view of wildfire burned area, (**h**) Tuscan database for landcover, legend accessible at [77]. the Sentinel images were selected according to a cloud cover threshold, and only data with under 5% cloud cover were used. The same procedure would have been implemented for PRISMA data; however, the possibility for the users to schedule acquisitions on specific areas does not allow a multitemporal analysis without a previously planned area of interest. Therefore, being the only available image for the area being acquired almost two years after the analyzed fire occurrence, and being the sensor extremely new in the spatial sensing constellation, it was decided to assess its capability to detect burned area spectral signature evidence despite the elapsed time. This objective was reached via the application of EnMap Toolbox [78]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Acquisition Date** & **Couplet** \\ \hline
04/06/2017 * & J\_J\_17 \\
04/07/2017 & J\_J\_17 \\
03/08/2017 & A\_S\_17 \\
12/09/2017 & A\_S\_17 \\
21/12/2017 & D\_J\_18 \\
30/01/2018 & D\_J\_18 \\
25/04/2018 & A\_J\_18 \\
29/07/2018 & A\_J\_18 \\
07/10/2018 & O\_J\_19 \\
25/01/2019 & O\_J\_19 \\
05/05/2019 & M\_A\_19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sentinel-2 images acquisition date and couplets (* pre-fire acquisition). Figure 3: Flowchart of the methodology adopted for Sentinel-2, PRISMA, and UAV data analysis. The burned area and fire severity computation procedure was adapted from the methodology proposed by [63] and followed these steps:  
1. Creation and subtraction to the acquisition of a layer constituted by the pixels classified as clouds and cirrus by Sentinel-2 preprocessed metadata;
2. Resampling of the bands to homogenize the spatial resolution to 10 m;
3. Computation of \(NBR\) index for each image as it is needed for RBR index computation: \[NBR=\frac{NIR-SWIR}{NIR+SWIR}\] (1)
4. Computation of the normalized difference water index (\(NDWI\)) [79] to reduce the noise arising from water bodies light scattering and subtraction from the images. The computation of this index was performed in the optic of improving the data quality or further \(RBR\) index computation: \[NDWI=\frac{Green-NIR}{Green+NIR}\] (2)
5. Computation of the \(dNBR\) index via subtraction among two adjacent acquisitions starting with the pre-fire image (couplets, Table 3): \[dNBR=NBR_{pre-fire}-NBR_{post-fire}\] (3)
6.