Because of the high spatial variability of soil moisture and sparsity of in situ observations, the use of satellite soil moisture estimates is potentially of great benefit in land surface modeling. X-band (10.65 GHz) observations from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on the NASA Aqua satellite are used to estimate soil moisture near the surface. We assimilate these soil moisture estimates into the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) land surface hydrology model with an Ensemble Kalman Filter, within the Land Information System (LIS) software. The goal is to improve the representation of soil moisture profiles in the land surface model. To alleviate the discrepancies between modeled and observed distributions, bias correction is done using a cumulative density function (CDF) matching technique with different corrections based on vegetation type and for day and night. We will present results showing the impact of the data assimilation on SHEELS soil moisture and temperature profiles, validating against in situ soil temperature moisture measurements during summer 2003 in the US Great Plains.