TThrough a collaborative effort, three different data assimilation algorithms (direct insertion, extended Kalman filter-EKF and ensemble Kalman filter-EnKF) have been implemented in NASA's Land Information System (LIS) to assimilate observations of land surface state variables such as surface soil moisture and temperature. In a version of LIS for future release, each of these algorithms can be selected to assimilate observations of one or more of these land surface state variables into any LIS-compliant land surface model (e.g. the Mosaic model, the Noah model, or the Community Land Model-CLM) without major modification to model- or variable-specific subroutines. NASA has collected more than four years of global soil moisture retrieval data from the AMSR-E microwave radiometer onboard the Aqua satellite. Since this data product may have significant bias relative to the model simulations in LIS and Kalman filter data assimilation requires the observations to have Gaussian error, how to pre-process the observational data before assimilation has become a recent research topic. In this presentation, we will use three different approaches to rescale the AMSR-E soil moisture data for the contiguous US and assimilate them into different land surface models using the Kalman filters in LIS. The assimilated results will be analyzed against each other and compared to the field observations from field campaigns and ground-based networks. The presentation will discuss the findings of this analysis.