Abstract: Abstract: Soil moisture in unsaturated zone connects the water and energy exchange process between surface water and groundwater, which has great influence to rainfall infiltration and surface evapotranspiration and hence has important meaning to agriculture, hydrology and meteorology. In agricultural study, accurate estimation of soil water content has significant importance to agricultural water management, irrigation regime determination and agricultural output increase. Soil water content can be quantified by surface observation, model estimation and remote sensing retrieval. Due to the large heterogeneity of soil property, surface observation at small scale can be hardly extended to large scale; while spatial distribution retrieved by remote sensing data can only obtain instantaneous value at satellite over-passing time. Land surface model is treated as a powerful tool in continuous estimation of soil water content, which is continuous in spatial and temporal dimension. However, the error tends to accumulate in the process of model simulation due to the inevitable uncertainty of forcing data and intrinsic error in model. Data assimilation technique can consider the uncertainty of the model and observation, update model states during the simulation period, and thus improve the accuracy of soil water content estimation, and exploit the advantages of both land surface model and remote sensing measurement. The concept and algorithm of data assimilation were first proposed by oceanologists and meteorologists, and have been gradually introduced to hydrology in recent years, such as soil water data assimilation and surface temperature data assimilation. As the development of remote sensing technique, more and more surface parameters can be obtained by remote sensing retrieval, which provides the available data sources for data assimilation system. The purpose of this study was to validate the data assimilation technique in improving soil water content estimation. To this end, an ensemble Kalman filter (EnKF) technique was coupled to a hydrologically-enhanced land process (HELP) model to update model states including soil water content and surface temperature. Random disturbance was added to the input data to generate ensemble model states and background error covariance matrix. The latent heat flux derived by MODIS data and surface energy balance system (SEBS) was used as the observation value of assimilation system to update the model states in HELP model. We chose a typical cropland in Weishan irrigation area (36°8′-37°1′N, 115°25′-116°31′E) as the study area, where located an eco-hydrological station (36°38′55.5″N, 116°3′15.3″E, average sea altitude of 30 m) with long series of flux data and meteorological measurements. The observation data used in this study were composed of flux observation data including soil heat flux, sensible and latent heat flux, meteorological observation data including rainfall, sunshine duration, air temperature and humidity, wind direction and speed, upward/downward longwave/shortwave radiation and infrared surface temperature, and vegetation observation data including canopy height and leaf area index. The model was firstly validated by the observation data in 2006, in which the open-loop estimation without state updating was treated as the benchmark run. The root mean square error (RMSE) of soil water content in surface, root and deep layer was 0.055, 0.053 and 0.053 m3/m3 respectively. After data assimilation update, the surface temperature estimation of both wheat season and maize season was improved to a large extent, with an effectiveness coefficient of 94.8% and 73.0% respectively. Data assimilation also improved the estimation accuracy of soil water content, with a reduction of RMSE by 30%-50% compared to the benchmark run. In wheat season, the effectiveness coefficient of soil water content estimation of data assimilation in surface, root and deep layer ranged from 9.31% to 74.17%. Compared to wheat season, data assimilation showed better results in maize season, the relative error of soil water content in surface, root and deep layer was reduced to 3.70%, 3.62%, and -5.45%, respectively, and the effectiveness coefficient of all 3 layers was over 60%. These results demonstrate that the effect of data assimilation on improving soil water states is positive, which provides a new approach in continuous estimation of soil water content.