Abstract: Abstract: The spatiotemporal distribution of soil moisture plays an important role in many fields including hydrological processes, agricultural management and climate change. Due to the limitation in in-situ measurement of soil moisture and its dynamic process, accurately estimating it at different soil depth by assimilating remote sensing data into soil hydraulic model has received increased attention. Combining the ensemble Kalman filter (EnKF) method with the hydrological simulation model HYDRUS-1D, this paper simulated soil moisture dynamics at soil profile scale and evaluated its precision by assimilating soil moisture retrieved from spatial resolution of 1 km and soil moisture downscaled from microwave sensor of the advanced microwave scanning radiometer 2 (AMSR2). The downscaled soil moisture was calculated using a scale-independent multi-parameter linear model by combining the multi-kinds of the optical MODIS image data including land surface temperature, albedo, and the normalized difference between vegetation index products. Using field measurement data and the downscaled soil moisture at resolution of 1km as the initial condition to the HYDRUS-1D model, we designed six assimilation schemes and compared them with the associated results simulated from the HYDRUS-1D model. The soil moisture at different depth from April 1 to August 31 in 2013 at Yushe and Yincheng in Shanxi province was simulated using the six designed schemes, and the simulated results were compared with the data measured on 1, 11 and 21 of each month at soil depth of 10, 20 and 40 cm, respectively. The results indicated that the precision of the estimated soil moisture at the two location was comparable, and the assimilated downscaled AMSR2 data can effectively improve soil moisture estimation, especially in the surface soil. When there were not enough field measurement data or remotely sensed soil moisture to drive the HYDRUS-1D model as initial condition, the HYDRUS-1D simulation could give rise to significant errors and assimilation results were more precise. Compared with the simulation schemes S1, S2, the root mean square error (RMSE) of the assimilation schemes A1 and A2 was low, and the effectiveness coefficients of A1 and A2 at different soil depth are higher than 19% and 13% respectively. Compared to S3, the effectiveness coefficients of A3 are negative due to some uncertain errors associated with the assimilated AMSR2 soil moisture. For schemes S4, S5 and S6 simulated directly from the HYDRUS-1D model using the AMSR2 monitored soil moisture, their effectiveness coefficients at different depth are all positive and greater than that of schemes A1, A2 and A3. For temporal change in soil moisture, the correlation between different schemes after assimilation are higher than that simulated directly from the HYDRUS-1D model, with the correlation decreasing with soil depth because the AMSR2 only captured the soil moisture in top soil. Sensitivity analysis reveals that the precision is impacted mostly by observation frequency and its errors, and it was insensitive to the background errors and the model simulation errors.