## 4 Results
### Feature Importance  
Quantifying the importance of individual features of a large variable space enables the better understanding of the dataset itself. This would in turn provide the means for the physical interpretation of results and the proper evaluation of the input data. Importance weights for the entirety of the Sentinel-2 variable space are shown in Figure 6, with features being grouped into thematic divisions with respect to spectral characteristics and acquisition dates. Feature importance, for the Sentinel-2 feature space, is calculated using the MATLAB function _ReliefF_. It is an extension of the original Relief algorithm suggested by [PERSON] and [PERSON] (1992), \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nomenclature Level** & **Kappa RF** & **std. RF** & **Kappa SVM** & **std. SVM** \\ \hline Type & 0.7834 & 0.0070 & 0.8721 & 0.0054 \\ Family & 0.8902 & 0.0030 & 0.9118 & 0.0053 \\ Season & 0.8856 & 0.0092 & 0.8729 & 0.0115 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum Kappa coefficient values for all nomenclature levels; values are averaged over 20 classification iterations, with the standard deviation of the mean accuracy values (std.d.) also listed. Figure 6: Feature importance bar graph of Sentinel-2 features, for the crop type classification. Features are grouped with respect to their spectral characteristics by position, and to their acquisition dates by color. The order for the 10 m resolution bands is B02-B03-B04-B08 and for the 20 m resolution bands is B05-B06-B07-B08A-B11-B12. which estimates the quality of attributes with respect to the distinguishability they offer among classification entities near each other [37]. Further, the ReliefF extension enables feature importance estimation even in multi-class scenarios, whilst being more resilient to incomplete and noisy data [38]. The algorithm randomly selects entities (Ei) and thereafter searches for the k nearest neighbors coming from the same class, known as nearest hits (Hij), and the k nearest neighbors from different classes, known as nearest misses (Mj). Feature weights are estimated based on the Ei, Hij, and Mj values. The contribution of all hits and misses is averaged, with a contribution weight for each class of the misses being dependent on the prior probability of that class [39]. The function was set to search for 80 nearest neighbors, as trialing with larger values did not produce better data modeling. May and July appear to attain the highest weights of importance. May coincides with the inflorescence emergence period for the winter crops, while July matches the winter crop senescence and summer crop leaf development (refer to Figure 3). PSRI is the most consistent of the VIs, having high weights of importance for nearly all scenes. In Figure 7 the highest ranked features, based on the feature importance estimations, are grouped into two thematic categories that relate to (a) month of image acquisition and (b) their spectral characteristics. Figure 7 quantifies the proportion of the top ranked features that stem from each month of image acquisition. In the same fashion, Figure 7 quantifies the proportion of top ranked features coming from (a) the visible and NIR bands of 10 m spatial resolution, (b) the vegetation red-edge and SWIR bands of 20 m resolution, and (c) the three VIs. The number of total top ranked features, for which the proportion values are calculated against, differ for each level of the nomenclature hierarchy. This refers to the least number of top ranked features that offers the first near-maximum classification accuracy; and amounts to 46, 25, and 21 features for the \"type\", \"family\" and \"season\" classifications, respectively. For the crop type level in Figure 7a, the most important features are distributed among May, June, July, and August. These months cover winter crops' flowering to senescence stages and summer crops' leaf development to flowering stages.