## 5 Discussion  
### Sentinel-2 MSI and Landsat-8 OLI  
We tested the performance of our processing workflow using Landsat data as input, as well. The aim is to highlight the performance improvement, if any, achieved with Sentinel-2 data, based on the unique spatial and temporal resolution, and the spectral characteristics in utilizing parts of the spectrum of key significance in discriminating between types of vegetation. The values in Table 7 have been averaged over 20 iterations of random training sample splits. Landsat crop-type classification proves to perform significantly worse than the Sentinel equivalent (Table 7). A combination of Landsat and Sentinel feature spaces marginally increases the overall accuracy and thus does not justify the higher complexity introduced. This is expected as the two feature spaces are highly correlated. We used [PERSON]'s test to evaluate the statistical significance in the classification accuracy between (i) the two classifiers in the Sentinel scenario and (ii) Sentinel-Landsat pairs in the SVM scenario. This is an interpretable statistical test that quantifies the superiority between two thematic maps [40]. [PERSON]'s test is essentially a standardized normal chi-square statistic, computed from a two by two matrix based on correctly and incorrectly classified parcels in both classifications, as shown in Equation (6). \[\chi^{2}=\frac{\left(\mathrm{n}_{ab}-\mathrm{n}_{ba}\right)^{2}}{\mathrm{n}_ {ab}+\mathrm{n}_{ba}} \tag{6}\]  
where \(\mathrm{n}_{ab}\) is the number of parcels correctly classified by classifier one but incorrectly classified by classifier two; \(\mathrm{n}_{ba}\) is the number of parcels correctly classified by classifier two but incorrectly classified by classifier one [36]. The difference between all pairs, shown in Table 8, proves to be statistically significant at a 99.99% confidence level. In this table, higher \(\chi^{2}\) values indicate better accuracy performance for the first item in the \"pairs\" column. These results further support the argument of SVM's dominance over RF (pair 5) and similarly MSI's dominance over OLI (pairs 1, 2 and 4). Relative \(\chi^{2}\) differences among the pairs can in ways determine the importance of parameters such as spatial resolution, spectral characteristics, and choice of classifier. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Features** & **Total Time RF** & **Total Time SVM** & **Difference** \\ \hline
65 & 1.32 s & 1.46 s & 10.6\% \\
90 & 1.42 s & 1.80 s & 26.7\% \\
117 & 1.55 s & 2.19 s & 41.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Total RF and SVM (C = 1) execution times, including both training and implementation, as number of features increases (Sentinel-2 feature space). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Space** & **Kappa RF** & **Kappa SVM** \\ \hline Landsat-8 & 0.5991 & 0.7011 \\ Sentinel-2 & 0.8055 & 0.8830 \\ Landsat-8 \& Sentinel-2 & 0.8022 & 0.8908 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Maximum K\({}_{\mathrm{c}}\) values for SVM crop type classifications based on the Sentinel-2 MSI, pan-sharpened Landsat-8 OLI and combined feature spaces. OLI's data pan-sharpening and MSI's data down-sampling, in pairs (1) and (4) respectively, attempt to reduce the effect of the spatial resolution difference and thus isolate and quantify the importance of other sensor attributes. The difference in accuracy is significant for both scenarios, which can be attributed to Sentinel-2's superiority in spectral characteristics, specifically having four vegetation red-edge bands (B05, B06 and B07). The difference for pair (3) is statistically significant but, nonetheless, implies a marginal increase in accuracy. In other words, pan-sharpening proves indeed useful for Landsat-8 but cannot be compared with the higher spatial resolution, in which Sentinel-2 multispectral bands sense directly. Moreover, pair (2) depicts the maximum difference between the two sensors, accounting for the impact of both spatial resolution and spectral characteristics.