This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R^2>0.93) and good accuracies (RMSE<0.83, rRMSE_m<23.6% and rRMSE_r<16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.