Land surface remote sensing and soil moisture data assimilation studies rely extensively on radiative transfer models to simulate observations of microwave radiobrightness temperature, given the spatial distribution of surface moisture and vegetation. The purpose of this modeling study is to assess the sensitivity of commonly-used radiative transfer schemes to hillslope-scale variation in topography. The effects of topography on modeled radiobrightness temperatures are twofold: (1) topography controls the local incidence angle the observing sensor makes with the local land surface, and (2) the spatial distribution of moisture, surface and canopy temperature, and vegetation depends on topography. Hillslope-scale incidence angles can be explicitly computed at every element in a computational domain knowing the local terrain slope and aspect, and the relative sky position of the sensor. A spatially-distributed, physically-based watershed ecohydrology model is used to account for topography-dependent variation in moisture, surface and canopy temperature, and vegetation biomass. These hillslope-scale heterogeneities affect both the spatial distribution of radiobrightness temperatures at hillslope scales, as well as the aggregate radiobrightness temperature at a spatial scale consistent with observation. Specifically, the spatial organization of channels and hillslopes within a watershed leads to a distribution of local incidence angles that is not well described by an average value or the satellite look angle. Furthermore, because of significant North-South contrasts in land surface states, for particular geomorphic contexts and polarizations the aggregate predicted radiobrightness temperature can exhibit sensitivity to the relative orientation of the sensor and the land surface. Implications for development of soil moisture retrieval algorithms, soil moisture data assimilation, and spatial disaggregation of radiobrightness temperature observations are discussed.