 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 SWIR bands were found to be important for vegetation mapping while the blue band was also important. The study also compared object-based image analysis, OBIA, with classical pixel-based classification and found that they achieved comparable results, mainly for cropland. However, only single-date acquisitions were available for this study, and the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution. This article was authored by Marcus Emitzer, Francesco Vualo, and Clement Atzberger. We are article.tv, links in the description below.