In this work a methodology aimed at land cover mapping over geographically wide regions, leveraging multitemporal Sentinel-1 SAR data, is presented. The paper describes an effective way to process SAR multitemporal data in order to obtain a set of spatio-temporal features, which well-summarize the temporal patterns of different land cover classes. Moreover, in this paper an innovative approach to smartly select training points from an existing Medium Resolution Land Cover (MRLC) map is presented. Both qualitative and quantitative results over four regions of interest, with the geographical extension of 100x100 square kilometre, confirm the validity of the proposed procedure and the potential of SAR data for land cover mapping purposes. These regions, located in Siberia, Italy, Brazil and Africa, were selected to test the methodology in completely different climate environments. The experimental results show that the proposed approach allows to increase the overall accuracy by 16%, on average, with respect to existing global products.