## 7 Conclusions  
The overall proposed procedure proved to be a valuable and efficient tool to determine post-fire vegetation recovery and wildfire-burned area analysis. The applied Sentinel-2 RBR index severity classification demonstrated the efficiency of the index in mapping diverse severity classes and burn patterns without reducing the performance for unburned parcels and peripheral areas. Sentinel-2 RBR index multitemporal analysis highlighted the different vegetation response as a function of the fire severity that affected the area, recording both the immediate shrub resprouting following the fire, the phenological resting phase of the vegetation and its development in the years following the fire. The RBRresults tested with the biophysical computed parameters showed concordance regarding the vegetation indices trend. The two year-long analyses allowed us to observe how high severity areas underwent a rapid sprouting phenomenon following the fire, contrasting with the later acquisitions where the same areas suffered from lower fCover and LAI if compared to intermediate severity classes. The hyperspectral satellite adopted embedded challenges in its data-processing arising from the novelty of the platform and data complexity. Nevertheless, despite the proposed method being an exploratory approach, the analysis highlighted the satellite performance and high spectral resolution. PRISMA ability to detect spectral signature changes in fire-affected areas proved to be efficient regarding high severity areas despite the elapsed time, testifying the alteration of soil and vegetation persistence, highlighting a different recovery time depending on the severity the area was affected by. The innovative multiplatform methodology proposed was performed with the correlation between Sentinel-2 RBR fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison, leading to a converging classification. The UAV NDVI index provided high-resolution data that could be integrated with the proposed methodology in study site two to benefit the overall accuracy and completeness of information. All the adopted platforms benefitted from a timely data acquisition for a representative data analysis. Future developments will focus on the integration between drone hyperspectral images and satellite hyperspectral data to create a multiplatform and multiscale scene analysis, increasing the overall accuracy. Indeed, the drone acquired data will provide a hyperspectral \"ground-like\" information useful for hyperspectral satellite acquired data scene classification.  
Conceptualization, G.L., W.F. and S.M.; methodology and data analysis G.L. and G.R.; writing--original draft preparation, G.L.; writing--review by W.F. and S.M.; supervision, W.F. and S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was carried out in the framework of the following projects: \"Development of knowledge and technical-scientific support for the evaluation of geo-hydrological and volcanic risk scenarios-WP 2.2 Remotely piloted aircraft and WP 5.2 research, development and technology transfer\", funded by the Italian Civil Protection Department-\"Hydrogeological and environmental risk evaluation using hyperspectral sensors\", agreement between ASI and DST-UNIFI.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and amended by the 64 th WMA General Assembly, Fortaleza, Brazil, October 2013.  
Not applicable.  
All data are available on Copernicus open access hub and PRISMA mission data download portal.  
Special acknowledgments to the ESP Team of the Unviersidade de Aveiro for hosting [PERSON] for the internship period, allowing him to acquire knowledge on wildfire-affected areas and sharing with him the Castanheira de Pera study site. Similarly, we acknowledge [PERSON], part of the \"Corpo dei Vigili del Fuoco Italiano\" (fire fighters department).  
The authors declare no conflict of interest.