## 4 Results  
### Accuracy Performance  
PA and UA results presented in Table 4 refer to the quadratic kernel SVM and RF classifications at the crop type nomenclature level. Since the dataset is strongly dominated by soft wheat parcels, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# Evaluation** & **\# of Weak Learners** & **Max \# of Splits** & **10-Fold Loss (\%)** & **Iteration Time (s)** \\ \hline
1 & 479 & 1182 & 10.69 & 206.31 \\
2 & 141 & 783 & 19.86 & 34.19 \\
3 & 30 & 8925 & 11.12 & 14.70 \\
4 & 781 & 2 & 33.87 & 11.81 \\
5 & 11 & 3083 & 16.98 & 3.258 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of weak learners (trees) and maximum number of splits optimization based on 10-fold cross-validation loss minimization. the overall accuracy would be respectively affected. Therefore, PA and UA better depict the classifiers performance for all individual crop classes. The values in Table 4 have been averaged over 20 iterations of random training sample splits. Both classifiers provide excellent results with an overall accuracy 85.59% for RF and 91.30% for SVM. Nonetheless, according to the PAs, SVM considerably outperforms RF for the barley and oats ground truth classes. These are two of the most indicative classes as their proneness to misclassification can be physically interpreted and thus better describe SVM's superiority. Barley, oats, and soft wheat are crops of the cereal family with strong physical and spectral resemblance. Even more, their similar cultivation calendars (Figure 3) minimize the discriminating qualities of the multi-temporal approach. The same holds true for shrub grass and vineyards, which are year-round crops, and thus their spectral profiles would not bear any solid temporal particularities. Shrub grass is also vegetation of a diverse nature, providing generic spectral signatures, and hence misclassifications are evident for both classifiers, particularly for RF. Finally, UA values identify SVM as the more reliable classifier, with very high accuracies for all crop types apart from shrub grass and vineyards. A clip of the SVM crop type classification map is shown in Figure 5, identifying both correctly and incorrectly crop type estimations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop Type** & **RF PA (\%)** & **RF UA (\%)** & **SVM PA (\%)** & **SVM UA (\%)** \\ \hline Soft Wheat & 94.89 (0.6) & 82.88 (0.9) & 95.60 (0.2) & 91.49 (1.0) \\ Corn & 96.35 (2.3) & 93.29 (4.0) & 92.56 (3.2) & 96.48 (3.6) \\ Barley & 81.81 (1.6) & 87.78 (1.0) & 90.10 (0.9) & 90.43 (0.9) \\ Oats & 44.38 (2.3) & 90.36 (2.4) & 78.57 (3.0) & 89.59 (1.9) \\ Sunflower & 85.69 (3.5) & 92.03 (2.7) & 85.36 (3.6) & 95.45 (2.6) \\ Rapeseed & 92.30 (1.7) & 95.03 (1.4) & 90.19 (1.8) & 96.12 (0.7) \\ Broad Beans & 85.41 (1.8) & 92.41 (1.5) & 86.83 (1.7) & 95.08 (1.1) \\ Shrub Grass & 77.93 (4.3) & 76.90 (3.8) & 84.07 (3.8) & 83.48 (2.3) \\ Vineyards & 86.15 (4.0) & 83.72 (3.7) & 87.52 (4.0) & 83.48 (3.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Producer’s accuracy and user’s accuracy for both Random Forest (RF) and Support Vector Machine (SVM) classifications for the crop type nomenclature level. The standard deviation of the mean accuracy values is also shown in parentheses. Figure 5: Farmers’ declarations and quadratic SVM classification at the lowest nomenclature level (crop type). Only a clipped part of the study site is shown, with correctly and incorrectly classified parcels also identified. Performance differences between the two classifiers become even more apparent using K\({}_{\text{c}}\), as RF classification is subject to substantial random agreement. Maximum kappa coefficients of 0.87 and 0.78 for SVM and RF, respectively, showcase more reliably the dominance of the first classifier over the latter (Table 5). The listed accuracy values have been averaged over 20 classification iterations of randomly split training sets. The standard deviation of the accuracies for each classification scenario is also given.