Single-source missions that provide VHR imagery are often associated with high cost and long revisit times. However, the recently introduced small-satellite constellations, such as Planet's flock of Doves, can offer global coverage at high revisit frequency and unprecedentedly low costs. Nonetheless, the inexpensive, off-the-shelf sensor components used imply suboptimal noise resilience, radiometric performance, and cross-sensor uniformity [11]. Time-series of multispectral imagery that covers the agricultural cycle, from seeding to harvest, is necessitated for accurate crop identification [12]. Mature crops are more distinct and carry varying textures, water absorbency, and colors, having greater chances of being detected with remote sensing [13]. With respect to image processing, literature clearly points to object-based image analysis (OBIA) methods, assuming VHR and HR imagery is in use. Alternative pixel-based analyses often lead to misclassifications due to the canopy's spectral variability, bare soil background reflectance, and mixed pixels. Grouping pixels into spectrally consistent objects can alleviate such issues [10]. Non-parametric supervised classifiers such as Support Vector Machines (SVM), decision tree ensembles and artificial neural networks have been successfully employed in crop identification schemes [14; 15]. In the last ten years, SVM and Random Forest (RF) classifiers have become increasingly popular in land cover mapping publications, doubling their appearances since 2012 [16]. Their success is attributed to effectively describing the nonlinear relationships between crops' spectral characteristics and their physical condition [17]. Both classifiers are resilient to noise and over-fitting and are therefore able to effectively cope with unbalanced data [16]. SVMs can ably handle small training datasets [18], while ensemble classifiers like RF are characterized by high computational efficiency [19]. These constitute ideal classification features for accurate and efficient crop type mapping and specifically for the purposes of the CAP monitoring, where data are big and ground truth information is scarce. The Sentinel-2 mission introduces a paradigm shift in the quality and quantity of open access Earth Observation (EO) data, opening a new era for operational terrestrial monitoring systems, a fortiori in the agriculture sector. The mission offers unprecedented 10 m and 20 m spatial resolution data at a 5-day revisit time with twin satellites. This, however, introduces the notion of big data and the challenge of their efficient handling. EO data are freely and systematically received over very large areas, which range beyond the limits of one region or a country, as they are nowadays required by the on-going and planned large scale initiatives and funded projects (e.g., GEOGLAM, GEO-CRADLE). In this regard, significant attention has been raised to Sentinels' potential for agriculture monitoring and particularly in the context of the CAP control scheme [20; 21; 22; 23]. One indicative example would be the European Space Agency's (ESA) Sentinel-2 Agriculture project, aiming at the simplification of crop management. The system achieves an overall mapping accuracy of 85% for five major crop types, making up 75% of the regional agricultural zone [24]. [PERSON] et al. (2017) have been among the first to use a scheme of mixed OBIA and RF approach for smallholding crop type classification, achieving 64.4% accuracy for 14 crop classes. In both publications, the authors tested their Sentinel specific methods, by simulating Sentinel-2 data using SPOT 5 and SPOT 4 (Take 5 experiment) as proxies. [PERSON] et al. (2017) additionally incorporated Landsat-8 and VHR imagery, with the latter proving inconsequential in the classification process [7]. This study attempts to capitalize on the most promising research paradigms discussed above, with a clear operationalization potential.