## 4 Discussion  
Wildfires in Europe are becoming increasingly extreme and, as a result, fire management must dedicate more effort to improve the prevention phase [58]. An effective way to start managing wildfire risk is to characterize and estimate fuel loads by adopting multiple and novel approaches [40]. This study intended to corroborate the knowledge on fuel load estimations and to understand which remote sensing variables are the most important in predicting them. Although previous studies have attempted to describe and map the properties of fuel load types [59; 60; 61], our study is the first to integrate multiple remote sensing predictors and several machine learning techniques in a Mediterranean landscape of Southern Europe. In this study, the 1-h fuel load estimation was conducted by comparing machine learning techniques (RF and SVM) with the MLR model. Based on performance metrics, we found that the RF model showed greater predictive power in estimating the response variable compared to the other models. The prediction error and correlation between the predicted and observed 1-h fuel load values (i.e., 0.09 and 0.71, respectively) suggest that the RF model has great potential in estimating fine dead fuel loads. These results are in line with those of [PERSON] et al. [25], who used the RF algorithm integrating Landsat 5 imagery, field data, and topographic factors for modeling and mapping forest canopy fuels in Lassen Volcanic National Park, California, USA. Their RF model showed that the remote sensing variables used to predict canopy fuel characteristics had pseudo-R\({}^{2}\) values ranging from 0.55 to 0.68. Conversely, our results are less consistent with the findings of [PERSON] et al. [62], who employed Sentinel-2 data and two machine learning approaches (RF and multivariate adaptive regression splines) to estimate the surface fuel load of pinewood in northwestern Spain. Their results showed a weak model performance with R\({}^{2}\) ranging between 0.02 and 0.12, probably due to the fact that the authors only used the products deriving from Sentinel-2A images as remote sensing variables instead of a multi-source approach like ours. Compared to the RF adopted in our study, the MLR model demonstrated a weaker predictive power. The prediction error and correlation between the predicted and observed 1-h fuel load values were 0.11 and 0.63, respectively. Other authors have confirmed the high performances of RF compared to MLR in similar studies [28]. For example, [PERSON] et al. [61] compared three machine learning techniques with MLR for above-ground biomass estimation in the Sierra Madre Occidental, in Mexico. Their results proved that RF and SVM techniques yielded the best results compared to MLR. Moreover, [PERSON] et al. [63] found that the use of machine learning algorithms in estimating the above-ground biomass of mangrove forests gave better results compared to linear regression. In our study, the remote sensing variables did not have the same weight in terms of importance in the RF model. Indeed, the LIDAR variables showed a higher predictive power than the NDVI and VH polarization. CHM followed by canopy and vegetation cover are the most important variables in estimating 1-h fuel load. This is most likely related to the fact that if the degree of coverage and the height of the canopy increase, then the biomass in terms of leaves, twigs, and small branches (e.g., fine and coarse dead fuel loads) increases as well [64; 65; 66]. These results are in line with those of [PERSON] et al. [67], who applied multiple regression analysis using airborne and terrestrial LIDAR for surface fuel load estimations in the open eucalyptrus forests of Australia. The developed models presented R\({}^{2}\) values up to 0.89. Moreover, [PERSON] et al. [31] used multispectral LIDAR data to apply generalized additive models to coarse woody debris load estimations, yielding a high accuracy of R\({}^{2}\) = 0.72.