## 4 Discussion
Contrary to LIDAR variables, the NDVI does not provide information on vegetation structure but qualitatively describes the photosynthetic and phytosanitary activity of plants [68; 69]. Furthermore, NDVI is not able to detect the undergrowth, especially when canopy layers are dense and stratified. For these reasons, results show that NDVI and the land-cover variable have the same weight in terms of importance within the RF model. The results also suggest that VH polarization had less importance in fuel estimation. Based on the findings of similar studies, a more plausible explanation is that the Sentinel-1 satellite radar images have a short wavelength and consequently low canopy penetration [70]. For example, [PERSON] et al. [71] found an insignificant correlation between VH polarization acquired by Sentinel-1A and above-ground biomass estimated in the field. Conversely, a significant correlation between VH polarization acquired by ALOS PALSAR and biomass was found by the authors due to the fact that the L-Band, compared to the C-band, of Sentinel 1-A is more sensitive to backscatter values and has a greater penetration of the canopy. The geomorphological variables (Slope, Elevation, and Easternness) showed lesser importance in estimating the 1-h fuel load, probably due to their relationship to the landscape topography rather than to vegetation. According to [PERSON] et al. [41], fire ignition probability is high at an altitude of about 500 m, which is the mean value of elevation in Apulia. This confirms the poor predictive power of these variables in discriminating the fire occurrence and the fine dead fuel production. Numerous studies have shown that geomorphological variables have a greater influence on the likelihood of fire occurrence, ignition and spread [72; 73]. For example, [PERSON] et al. [74] found that elevation and slope significantly influence the probability of wildfire occurrence in Italy. However, there is no evidence to support the fact that geomorphological variables influence the production of fuel load. Our results may be applied regardless of the specific landscape context since our study has suggested a method to understand where the fine dead fuel is more likely to accumulate. According to the map in Figure 6, the areas associated with a high quantity of 1-h fuel load were found in forests, while this quantity radically decreased in meadow and pasture areas. This is most likely related to the fact that if the ecosystem complexity increases, in terms of biomass and vertical structure, the 1-h fuel load also increases and, hence, the likelihood of ignition. In this regard, quantitatively estimating the fuel load and knowing its spatial distribution could have great practical significance, thus allowing the prioritization of silvicultural interventions within fire management and mitigation plans [75; 76]. In a context where resources are often limited and local and national authorities focus their efforts solely on fire suppression [77], our approach could create the basis for planning new preventive policies and budgeting financial resources. Although the RF model has shown a reliable performance, our study presents some limitations. For example, LIDAR is a valuable tool for estimating 1-h fuel load, but not all areas are covered by this data source, as it is extremely expensive to run. Another limitation of this study was the use of the C-band images of Sentinel-1, which are less sensitive to fine dead fuel load compared to the L-band images of the ALOS PALSAR satellite. However, the L-band images of the ALOS PALSAR satellite were only available until 2018. Future studies are needed to improve the knowledge on which predictors determine and influence the distribution of fine dead fuel. For example, in fuel load estimation models, future research should include new variables such as those related to climate or landscape heterogeneity [78]. According to [PERSON] et al.