## 4 Discussion  
### Model Performance Results and Classification of AOI  
The spectral analysis reveals clear separability among land cover types. Specifically, NPS surfaces exhibit spectral dissimilarity compared to arboreal vegetation but demonstrate proximity to arable or plowed fields (AL), likely influenced by post-harvest periods affecting the spectral signature, akin to artificial surfaces [90]. The MAA cluster shows closer proximity to VEG and HZ, attributed to prevalent olive groves with irregular planting patterns. This results in significant exposed bare ground, influencing pixel spectral signatures while retaining arboreal vegetation coverage. VEG and HZ, characterized by compact and uniform tree coverage with substantial canopy development, exhibit higher similarity. Differences in phenological phases may significantly impact spectral-temporal signature similarities between VEG, HZ, and MAA [91]. The performance of the RF models and the hierarchical workflow employed is excellent, both during cross-validation and testing on independent samples in the training process. The models consistently achieve average accuracies, precisions, recalls, and F1-scores exceeding 0.99. The hierarchical approach highlights how optimal hyperparameter search and cross-validation lead to specific and generalized models, effectively discriminating nested categories, such as the HZ class within VEG. The study highlights the crucial influence of summer months on spectral-temporal classification. The NIR and Red-Edge bands show limited significance in RF1, aligning with prior research [92]; on the other hand, significant importance is attributed to the B12 \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Comparison** & \(n\) & **Statistic** & \(p\) \\ \hline Cluster 1 vs. Cluster 2 & 3400 & 1283 & \(5.01\times 10^{-281}\) \\ Cluster 1 vs. Cluster 3 & 3400 & 506 & \(3.90\times 10^{-112}\) \\ Cluster 2 vs. Cluster 3 & 3400 & 55.6 & \(8.81\times 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of the Kruskal–Wallis test for altitude comparisons among clusters. Figure 18: Sampling points elevation values in different clusters (meters above sea level). band (SWIR). The combined NPS \(\cup\) AL clusters exhibit heightened reflectance in the visible band, attributed to high reflectivity from soil and artificial surfaces, distinguishing them from vegetated regions [93]. In RF1, radar-derived parameters, particularly VH, strongly influence outcomes, reflecting the high entropy scattering of vegetated tree surfaces [94] with respect to flat surfaces as AL. RF2 also underscores the importance of visible bands, especially during summer, in distinguishing MAA from VEG \(\cup\) HZ. The spectral-temporal signature of MAA, which includes olive groves and vineyards, appears influenced by visible soil due to inter-row spacing in olive groves and due to biomass reduction during summer thinning in vineyards. SWIR, DEM, and SAR variables show limited importance in this classification step. RF3 underscores the significance of SWIR variables during summer, indicating a lower leaf water content in hazelnut groves compared to spontaneous vegetation (Figure 19). This could be attributed to the hazelnut's limited ability to regulate stomatal conductance during thermal stress [89]. The discernible difference among SWIR variables during the summer period might indicate a distinctive characteristic for distinguishing hazelnuts from natural vegetation. The importance of SAR variables in the classification process is limited to the initial stage (RF1), and in the month of May for RF3, where hazelnut showed a lower VH backscattering compared to the spontaneous vegetation (Figure 20). This is probably due to moving and weeding operations that take place in this month in the inter-row spaces. However, the general importance across all variables and classifiers is consistently low, typically ranging from 0 to 0.1.