The quantitative estimation of watershed Evapotranspiration (ET) has been an international frontier in water sciences for a long time. Hydrological models and remote sensing ET models are usually used to estimate regional ET at different spacetime scales, but these two methods are obviously insufficient to obtain precise and continuous regional ET. The hydrological models have the capability to simulate time-continuous daily or monthly ET processes, but the accuracy is not high compared with remote sensing ET models. The applicability of remote sensing ET models based on surface energy balance is restricted by the lack of high frequency and high resolution thermal data. A compromise between these two methodologies is represented by improving the optimization of hydrological models on the basis of a new ET series, which are produced by Data Assimilation (DA) scheme combining sparse remote estimates into the hydrological model. This study aimed to integrate the advantages of the two models to simulate the daily ET processes in Shahe River basin, Beijing. For this progect, the distributed hydrological model was fist constructed and the daily hydrological processes of 19992007 simulated. Then, the Ensemble Kalman Filter (EnKF) was used to assimilate the ET series calculated by remote sensing retrieval into the hydrological model to adjust the simulation. The results show that the ET estimation accuracy is improved after the data assimilation, and the MAPE between the DSMbased ETs and LASbased ETs in the study area is reduced. The integrated method is proved better, and improves the hydrology modeling accuracy. Therefore, the project successfully develops a new land surface ET mode with the advantages of hydrological model and remote sensing ET model, and the study founds the new method could simulate regional ET with high accuracy and continuous time series. The new land surface ET model not only follows the surface energy balance, but also meets the regional water balance, and has more perfect water thermal coupling mechanism. The study will further enrich the content of ET estimation disciplines, and provide a scientific basis for better understanding of the laws of regional water cycle.