Soil moisture is an important hydrosphere state variable, because it controls the partitioning of sensible and latent heat fluxes, and constrains local runoff production during storm events. Accurate knowledge of the spatial distribution of soil moisture at hillslope scales (e.g., 10's to 100's of meters) can significantly enhance applications that require high-resolution soil moisture information. The spatial distribution of soil moisture is controlled across a range of scales by variability in topography, soils, vegetation, and precipitation. Although the ground resolutions of planned sensors, such as the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautic and Space Agency's Soil Moisture Active-Passive (SMAP) missions, are too coarse to capture hillslope-scale variation in soil moisture, these data are nevertheless useful for hillslope-scale estimation in the context of a data assimilation system. We describe efforts to construct an ensemble Kalman Filter to fuse simulated noisy L-band microwave brightness and radar backscatter observations with uncertain hillslope scale soil moisture estimates derived from a physically-based ecohydrology model. Uncertainty in the spatial distribution of soil moisture arises from imperfect knowledge of the hydrometeorological forcings and soil hydraulic and thermal properties input to the model. In a series of synthetic experiments we demonstrate that assimilation of successive observations leads to a gradual improvement in the forecast spatial distribution of soil moisture in the near-surface and throughout the soil profile, relative to the synthetic true soil moisture. The critical components of realizing improved soil moisture knowledge through this data assimilation strategy are: (1) process model that represents the processes responsible for moisture redistribution across a range of scales finer than those captured by the observation, (2) formulation of an observing system that relates the modeled states to the observation, and (3) adequate characterization and representation of uncertainty in inputs to the model. This work demonstrates the potential importance of planned satellite observations for improving soil moisture knowledge at hillslope-scales, potentially benefitting applications requiring information in such detail and supporting ongoing efforts to couple hillslope-scale ecohydrologic models with weather models.