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Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth. [ABSTRACT FROM AUTHOR]

The soil surface albedo decreases with an increasing biochar application rate as a power decay function, but the net impact of biochar application on soil temperature dynamics remains to be clarified. The objective of this study was to assess the potential of infrared thermography (IRT) sensing by monitoring soil surface temperature (SST) with a high spatiotemporal and thermal resolution in a scalable agricultural application. We monitored soil surface temperature (SST) variations over a 48 h period for three treatments in a vineyard: bare soil (plot S), 100% biochar cover (plot B), and biochar-amended topsoil (plot SB). The SST of all plots was monitored at 30 min intervals with a tripod-mounted IR thermal camera. The soil temperature at 10 cm depth in the S and SB plots was monitored continuously with a 5 min resolution probe. Plot B had greater daily SST variations, reached a higher daily temperature peak relative to the other plots, and showed a faster rate of T increase during the day. However, on both days, the SST of plot B dipped below that of the control treatment (plot S) and biochar-amended soil (plot SB) from about 18:00 onward and throughout the night. The diurnal patterns/variations in the IRT-measured SSTs were closely related to those in the soil temperature at a 10 cm depth, confirming that biochar-amended soils showed lower thermal inertia than the unamended soil. The experiment provided interesting insights into SST variations at a local scale. The case study may be further developed using fully automated SST monitoring protocols at a larger scale for a range of environmental and agricultural applications. [ABSTRACT FROM AUTHOR]