The ensemble Kalman filter is an easy to use, flexible, and efficient data assimilation algorithm widely used in Land Surface Data Assimilation System. It bases on the normality approximation of model error and observational error as well as the linearity assumption of the soil moisture errors between the near-surface observation and other deep layers. However, the soil moisture equation is highly nonlinear and also soil moisture can be highly skewed toward the wet or dry ends. To evaluate the effects of these approximations and the performance of the ensemble kalman filter (EnKF) in estimating soil moisture profile based on the near-surface soil moisture measurements, the results from the EnKF are compared with those obtained from a sequential importance resampling (SIR) particle filter that is one of nonlinear filters . The comparative results show: the EnKF can quickly and accurately obtain the exact soil moisture profile regardless of the small number of ensemble members used (40) or the large number of ensemble members used (800); however, the SIR needs the large number of replicates required to accurately represent the variable conditional probability densities. The near-surface soil moisture forecast densities, the skewness and kurtosis and obtained from the EnKF are completely different from those from the SIR filter; the densities from the EnKF is only one modal during the total assimilation time window but those from the SIR experiences from one mode to two modes and again to one mode process.