 The study presents preliminary results of two classification exercises using Sentinel-2, S2, data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in lower Austria as well as winter crops slash bare soil. In the second case study, seven different deciduous and coniferous tree species were mapped in Germany. The study confirmed that S2 data can produce reliable land cover maps with cross-validated overall accuracies ranging between 65% tree species and 76% crop types. The red edge and shortwave infrared SWR bands were found to be important for vegetation mapping while the near infrared bands were amongst the least important. The study also showed that object-based image analysis, OBIA and classical pixel-based classification achieved comparable results, mainly for cropland. The full potential of S2 data could not be assessed as only single-date acquisitions were available. However, with the twin S2 satellites offering global coverage every five days, unprecedented spectral and temporal information can be concurrently exploited with high spatial resolution in the future. This article was authored by Markus Emitzer, Francesco Vallo, and Clement Atzberger. We are article.tv, links in the description below.