Leaf Area Index (LAI) is an important land surface parameter in global carbon cycle studies. Over the past decades, various of algorithms for retrieving LAI from satellite data have been developed. However, the continuity of LAI time series is still need to be improved. In this study, we present an algorithm for retrieving LAI dynamics based on the framework of Dual ensemble Kalman Filter (Dual EnKF), which updates the estimation of LAI and its sensitive parameters in a dynamic model, and gets optimal results simultaneously. The Dual EnKF algorithm can get better estimation results of the dynamic model, and assimilates remote sensing data into the dynamic model to get optimal estimation results of LAI. Three sites with the land cover of cropland, grassland, and forest are employed to validate this algorithm. The validation results show that the LAI temporal profile estimated by Dual EnKF method is very continuous with less fluctuations and abrupt change points; the estimation results of temporal dynamic model is improved, even when high quality remote sensing data is not available. LAI time series is in good agreement with the realistic LAI climatology.