## 3 Results
### Validation  
In order to validate the usefulness of these methods, the following procedure contains two phases. First, the Feature Core is analyzed as a result of merging the rankings of Tables 4 and 10. Indeed, as an intersection of the feature ordering and groupings concluded from the methods \(M1\) and \(M2\), the cornerstone feature combination of the most performant models is as follows:  
Feature Core: _accumulated precipitation, maximal air temperature, latitude_ and _sea level pressure._  
This feature grouping is present in the 0.3% percentage of the data set, a total amount of 529 feature combinations listed in Table 2. A visual performance comparison between this reduction against the total data set is illustrated in Figure 4. More concretely, Figure 3(a) contains the normalized efficiency distribution of all feature sets of Table 2, while Figure 3(b) depicts the normalized efficiency distribution of the limited combinations characterized by their common Feature Core. The normalization of the results facilitates analyzing the impact of the data set reduction, resulting in a change in the distribution trend in Figure 3(a), transferring the main concentration from higher RMSE values to lower ones. The conclusion is that this reduction strategy provides a more competent collection. This reduction can be limited to concrete feature groupings considering the analysis performed in Section 3. Two specific feature combinations **G1** and **G2** arise from both association metrics, \(M1\) and \(M2\), respectively. Both feature combinations keep the same Feature Core, and two additional distinct features as detailed as follows:  
**G1**: Feature Core, _slope, minimal air temperature_  
**G2**: Feature Core, _devepoint temperature, accumulated RED._  
Figure 5 illustrates the competency of feature groups **G1-G2** in comparison to the efficiency distribution of the reduced data set according to the Feature Core criteria. The RMSE of 0.555 for the **G2** collection is slightly better than the RMSE of 0.579 for **G1**. Therefore, from an efficiency point of view, the feature set **G2** is better. On the other hand, the physical meaning of _accumulated RED_ is harder to explain rather than _slope_ or _minimal air temperature_ features. Consequently, for the sake of interpretability, the feature collection **G1** may be more interesting from a biological point of view. Figure 4: Normalized efficiency distribution comparison between all feature combinations from Table 2 versus reduced selection. The main characteristic of the limited ensemble is that all feature combinations contain the following core: _accumulated precipitation, maximal air temperature, latitude_ and _sea level_. **G3**: _GDD, NIRcum, EVIcum, REDcum, NDVIcum, precip cum_  
**G4**: _GDD, NIRcum, EVIcum, solar zenith, NDVIcum, mean temperature_  
**G5**: _sea level pressure, GDD, slope, wind u, blue surface reflectance, dewpoint temperature_  
In addition, in order to compare the capacity of this feature processing, the following standard methodologies have been applied to the original data set: Pearson Correlation Coefficient, Mutual Info Regression, Sequential Feature Selector. The best scored features according to the first two techniques are listed in Tables 10 and 11 and grouped, respectively, in feature groups **G3** and **G4**. The Sequential Feature Selector outputs in a Boolean manner, and the selected features are grouped in **G5**. Figure 6 contains the accuracy comparison between all considered feature priorization techniques. The RMSE mean in almost all cases is similar, being slightly lower in the **G3** and **G5** groupings, and slightly better in the **G4** grouping. However, if we look at the variability of the data, the results with the least variability, and therefore the most consistent, correspond to the **G2** cluster. In the case of the **G1** grouping, it also shows a lower variability than **G4**, the difference being negligible.