The multi-agency/university Land Data Assimilation System (LDAS) project is designed to provide enhanced soil and temperature initial conditions for numerical weather/climate prediction models by using real-time observed precipitation and solar insolation data. A simple statistical analysis of the soil moisture fields generated by the four different land surface models (LSMs) currently implemented in the LDAS revealed apparent dissimilarities in the spatial patterns of these fields. In that statistical analysis, all LSMs are driven by the same meteorologic forcing data. However, each LSM was initiated differently. In this study, all LSMs are initiated at the same time with the same relative soil wetness to eliminate some of the uncertainties associated with different initialization. This study examines the degree of correlation between these LDAS soil moisture fields and how this may vary with time. The mean statistical properties as well as the spatial variation of these soil moisture fields is investigated. The results of this study should provide important insights into the similarities and differences of the four LSMs in LDAS. This study should also shed light on the spin-up properties and possibility of using soil moisture states from one model to estimate initial soil moisture states from another model.