Soil surface characteristics (SSCs) are of high importance for water infiltration processes in crop fields. As SSCs present strong spatiotemporal variability influenced by climatic conditions and agricultural practices, their monitor has already been explored by using UAV images and multispectral remote sensing. However, each technique has encountered difficulties characterizing this spatiotemporal variability. The objective of this work was to explore the potential of Sentinel-2 images to assess three SSCs – the green vegetation fraction, dry vegetation fraction and physical soil surface structure – at several dates. This work explored two approaches for classifying these three SSCs from five Sentinel-2 images acquired from August to November 2016. In the “single-date” approach, a Random Forest Classifier (RFC) model was trained to classify one SSCj from a dataset extracted from one Sentinel-2 image i (model noted RF_sdi,SSCj). In the “multi-date” approach, a RFC model was trained to classify one SSCj from a dataset extracted from the five Sentinel-2 images (noted RF_mdSSCj). The classification analysis showed that i) the RF_sdi,SSCj and RF_mdSSCj models provided accurate performances (overall accuracy > 0.79) regardless of the studied SSCj and the tested Sentinel-2 image, ii) the RF_sdi,SSCj model did not allow the classification of SSC classes that were not observed on the studied date, and iii) the RF_mdSSCj model allowed the classification of all SSC classes observed in the five Sentinel-2 images. This indicated that several Sentinel-2 images can favourably be used to increase knowledge of spatiotemporal representation of SSCs by extending results of infrequent, localized and cumbersome field work.