## 4 Discussion  
### Effects of Reflectance Temporal Variation and Field Heterogeneity on Classifier Performances  
In this study, we split the cultivar into three seasonal categories (winter, summer, and perennial). Based on the plant phenology provided by the Sentinel-2 NDVI time series (Figure 5), it is evident that the same-season crops show limited differences in the growing phenology. As an example, the higher accuracy in the detection of cardoon and olive orchards (Figure 8c) occurred in the early season images (related to the earlier growing stage) (Figure 5c), while for peas, the later phenological growing stage led to a one-month delay in comparison with other winter-season species (Figure 5a).  
Among the species, alfalfa showed the lowest accuracy as a direct factor related to the multiple harvesting patterns (Figure 5c) and the difficulties in obtaining PRISMA images suitable to catch its growing behavior. This is also depicted in Figure 6c, where alfalfa shows low spectral similarity with the winter crops while showing high similarity with respect to the summer crops growing in accordance with the harvesting cycle. As mentioned in the study [65], temporal resolution is more important than spectral resolution for alfalfa (the high capability of Sentinel-2 time series data for alfalfa mapping is also highlighted in the literature).  
The spectral signatures of the analyzed crops do not show clearly differences between species (Figure 6e). These high spectral similarities lead to increasing the classification omission and commission errors between barley, wheat, and tritticale species. The reason is related both to the similarity between the growing calendar (Figure 5) and their spectral behavior (Figure 6).  
This result agrees with the work of [PERSON] et al., 2015 [65], that taking advantage of the growing season maximizes the accuracy in the discrimination of soybeans, canola, wheat, oats, and barley using hyperspectral reflectance. Furthermore, in accordance with [PERSON] et al. [66], the crop temporal spectral signatures change according to the growing season, as pointed out by Figure 7. This is connected to the change in the plant's biophysical/biochemical characteristics (e.g., canopy structure, chlorophyll, and water content [67]), in accordance with the different growth stages [68]. Methods like feature selection applied to spectral data [69] could be useful to minimize the influence of growth stages on crop identification accuracy, but this still needs to be further analyzed in accordance with the study of [PERSON] et al. [70] and [PERSON] [71], which observed that reflectance temporal changes pattern should be considered within the algorithm configuration. This issue can be better faced taking advantage of the increasing availability of hyperspectral satellites offered by EnMAP, EMIT, and DESIS, and upcoming missions like CHIME and PRISMA-SG. At this scope, the conventional CNNs or trending sequence models, like recurrent neural networks (RNNs) and transformers, can hardly address (i.e., simultaneously and efficiently) the spectral/temporal variability issues. For future work, the authors' suggestion is to tackle this variability issue by implementing state space models like SpectralMamba [72].