Dense time series of optical satellite imagery describing vegetation activity provide essential information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal resolution of optical sensors is strongly affected by cloud cover, resulting in significant missing information. The use of complementary acquisitions, such as Synthetic Aperture Radar (SAR) data, opens the door to the development of new multi-sensor methodologies aiming at the reconstruction of missing information. However, the joint exploitation of new radar and optical missions, such as the Sentinel, raises new challenges given the different nature and response of the two data sources. In this work, the SenRVM methodology is proposed as a new multi-sensor approach to regress SAR time series towards Normalized Difference Vegetation Index (NDVI). A deep Recurrent Neural Network architecture which integrates SAR acquisitions and ancillary data is adopted. The regression task permits a continuous optical temporal resolution of 6 days. Multiple experiments are carried out to assess the SenRVM framework by studying two large-scale areas in France. Through an extensive interpretation of the results, SenRVM is evaluated on three main vegetation types (grasslands, crops, and forests). High accurate results (R 2 > 0.83 and MAE < 0.05) over more than 140, 000 vegetation polygons are obtained, both for multi-class and class-specific models. The importance of the SenRVM input features is discussed through an ablation study, highlighting that the relevance of the features differs for the various classes. The good performances of SenRVM are assessed by evaluating the quality of the reconstruction over short-and long-term data gaps. Results are compared to five state-of-the-art methodologies. Finally, some preliminary experiments are first carried out to show how SenRVM results could be used to improve the existing cloud & shadow masks. The recovering of time series breaks caused by vegetation cover changes is also illustrated.