The influences of vegetation and its spatial and temporal heterogeneity on the detection of soil moisture can be significant and may limit the applicability of satellite passive microwave sensors. Sensitivity analysis of an applied soil moisture algorithm using ground-based measurements can show where problems can arise and how they may be circumvented. This paper investigates a method of retrieving a one-dimensional soil moisture profile and the surface and canopy temperature, under the influence of different vegetations and dynamics, by integrating numerical models and passive microwave, visible, and near-infrared measurements via a novel application of data assimilation. The land surface scheme (LSS), which is at the heart of the present land data assimilation scheme (LDAS), is a biophysically based model (simplified biosphere model 2: SiB2) of soil, vegetative and atmospheric interactions. The ground-based microwave radiometer (GBMR) measurements, gathered over the soil moisture experiments in 2002 (SMEX02) in Iowa, were assimilated into the LSS via a radiative transfer model (RTM) using LDAS. Compared to open loop simulations, the results of LDAS are in better agreement with observations.