Agricultural land is strongly involved in the provision of ecosystem services given its spatial extent and impacts of agriculture on ecosystem functioning. Therefore, mapping ecosystem services require spatially explicit data on agricultural land uses. However, available data on agricultural land use usually have coarse spatial and temporal resolution, thus contributing significantly to uncertainties in ecosystem services quantification. Our goal is to develop an approach that produces high-resolution data on agricultural land uses. We propose a remote sensing approach using freely available MODIS images coupled with RapidEye images. Based on the phenological specificities of the different crops, we produced annual maps of agricultural land uses. We tested this approach over the spatially heterogeneous and mountainous region of Grenoble, France, where we mapped agricultural successions over 5 years. Maps produced at the scale of the agricultural parcel describe agricultural land uses through 17 classes. Our results suggest that coupling MODIS and RapidEye data can be used to map agricultural land use at high temporal and spatial resolution even in complex mountain landscapes. The comparison of the agricultural production service estimated by our method and from the Corine Land Cover database highlights the value of incorporating interannual variability in agricultural land use. This will also be facilitated in the future by the availability of new products from Sentinel 2.