## 5 Discussion
### Relevance in Operational Scenarios  
The notion of transferability, although addressed in the design process and the selection of input data, remains to be validated by applying the scheme to other agricultural landscapes in the European Union (EU). An evident issue of transferability is the absence of standardization and the dissimilarity of the LPIS data and farmers' declaration among the different EU countries. There are significant differences in the definition and description of cultivation practices and crop types. Dealing with this variability requires manual pre-processing and polishing of the input data and entails the appropriate definition of the crop types to be classified, merging or breaking them down to classes that describe spectrally coherent vegetation types. In this study, it was shown that a time-series of multispectral imagery is required for accurate crop classification, as capturing the growing of crops exposes the most significant differences in their spectral signatures and thus enables their successful discrimination. Nonetheless, it was also exhibited that only a handful of images, appropriately distributed in time, is required to offer high thematic accuracy. The twin Sentinel-2 satellites offer a combined revisit time of five days, amounting to more than 70 images throughout the year. It is expected that in most cases this is a sufficient number of images to produce several quality cloud-free images within the year. However, in northern countries, where cloud-free imagery is scarce, Landsat-8 OLI data can be employed to enrich the feature space. Alternatively, fusion techniques with weather independent Sentinel-1 SAR data could also be explored. When applying the scheme over large areas, the corresponding Sentinel-2 tiles can cover multiple adjacent satellite tracks, thus having different sensing dates. To overcome this issue, all tracks should be resampled with respect to a common temporal frame of reference, as proposed in [24]. Starting from the first image acquisition, a sampling step of five days, namely the Sentinel-2 revisit time, is set to create a grid of virtual sensing dates [24; 42]. Then all images from all tracks are linearly interpolated to these predefined sample instances. The temporal interpolation was shown to marginally affect the classification accuracy [24]. Since the methodology introduced was designed to be fully transferable by making use of predominantly open access data and data provided by the targeted end-users, the concept of scalability in regional or national scales is of great interest. Scaling up comes against certain trade-offs, mainly regarding the computational complexity, overall accuracy performance, and geographic scale--which in ways can be addressed based on this study's results. Processing should be performed in adequately small regional extents, such as the one exhibited in the present study, in order to avoid the geographic variability of the crop type spectral signatures. Thereby, the difference in processing time between SVM and RF application, as presented in Section 4.4, can be thought of as linearly increasing in scaling up scenarios. SVM usage would indeed compromise computational economy, but not significantly. In that respect, accuracy is favored over computational complexity, as the relevant differences are less important. Nevertheless, in cases where the lowest level of nomenclature is not required, RF functions as an excellent alternative. It performs exceptionally for crop family and season of cultivation classifications, where it deals with fewer classes, of more distinct spectral profiles. All in all, it could be argued that both classifiers provide excellent results for a large number of classes, under an overall computationally efficient scheme. Finally, the dependability of the methodology relies on the assumption that farmers' declarations are truthful. In this study, it was shown that declarations were correct in their vast majority (99%), which might not be the case for every relevant scenario.