Poplar is one of the fast-growing and wood producing trees which are increasingly considered as an important resource. In France, the leading European country in terms of plantations, poplar cultivation is a local key industry. However, since the economic slump, wood prices are at their lowest. Planted surfaces are in continuous regression. The future of poplar is mainly based on replanted areas after harvesting. Thus, spatially explicit information of newly-planted and disappeared surfaces provides essential baseline data for industrial and socio-economical dynamics. Accurate and regularly up-to-date maps of poplar plantations are not yet available at the national scale. The update rate of the French National Forest Maps is unsuitable for this species because of its short rotation cycle (from 15 to 20 years). Since the availability of high spatial and temporal resolution Sentinel time series, new opportunities for monitoring poplar plantations over large areas have come up. In the present study, we performed a Random Forest supervised classification on Sentinel-2 time series to identify poplars over a first study site : Tarn Et Garonne department in southwest France (~3730 km²). We also added multi-temporal SAR features (σ0 VV, σ0 VH) extracted from the Sentinel-1 images to examine the complementarity of optical and radar information. In order to deal with the limited number of labelled and confident samples, a semi supervised Active Learning (AL) technique is then applied repeatedly. It consists on selecting new training samples guided by the algorithm needs to enrich the classification model and adapt it to a larger scale (iterative classification of other departments in France). The AL approach is based on entropy measure as a criteria of sample informativeness. In this work, Sentinel time series dated from 2016 and reference samples were grasped from polygon-eroded national forest database (IGN BD Forêt®) and in situ observations with a stratified random sampling. The preliminary results underline that 2 % of additional actively-learned samples from a neighbouring department (Gers) are good enough to enrich the previously developed model. The achieved classification performance (OA ~ 78 %) is equivalent to that of a random model built with only department-specific samples (OA ~ 80 %). Moreover, the new model is adapted to both study areas. In this case, Radar information has not improved significantly the classification accuracy of poplar plantations but it will be considered for stand age distinction due to its sensitiveness to the vegetation structure. Further work will be conducted to extend the developed model, with the AL approach, to a more contrasting study sites in terms of poplar variability, climatic conditions and silvicultural management practices.