Recent studies on the assimilation of Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture retrievals focus on extracting anomaly signals embedded in the sensor data to improve the anomaly detection of land surface models. Utilizing only anomaly information does not address the issue of model bias, an issue which poses a challenge for most assimilation methods. Models are often biased due to uncertainties in the input parameters and deficiencies in model physics. In particular, the bias in the simulated soil moisture fields has a substantial impact on the estimation of land surface processes such as evapo-transpiration (ET) and runoff since they are calculated based on the absolute value of soil moisture. In contrast to anomaly information, the actual value of AMSR-E retrievals, in theory, represents the spatial mean and therefore, the unbiased state of the true soil moisture field in a footprint area. Recognizing the potential of using sensor data to reduce model bias, an ensemble Kalman filter with a mass conservation constraint was developed to assimilate the actual value of AMSR-E retrievals without any scaling or preprocessing. Results using the Noah land surface model in the Little Washita watershed of Oklahoma show that the mass conservation scheme reduced the bias of estimated soil moisture fields in the entire soil profile. Impacts on ET and runoff as well as potential issues in applying an ensemble Kalman filter in a Noah-like model are discussed.