Enabling intercomparison of synthetic aperture radar (SAR) imagery acquired from different sensors or acquisition modes requires accurate modeling of not only the geometry of each scene, but also of systematic influences on the radiometry of individual scenes. Terrain variations affect not only the position of a given point on the Earth's surface but also the brightness of the radar return as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover induced backscatter differences, and comparison of backscatter from multiple satellites, modes, or tracks loses meaning. The ASAR & PALSAR sensors provide state vectors and timing with higher absolute accuracy than was previously available, allowing them to directly support accurate tie-point-free geolocation and radiometric normalization of their imagery. Given accurate knowledge of the acquisition geometry of a SAR image together with a digital height model (DHM) of the area imaged, radiometric image simulation is applied to estimate the local illuminated area for each point in the image. Ellipsoid-based or sigma naught (σ<sup>0</sup>) based incident angle approximations that fail to reproduce the effect of topographic variation in their sensor model are contrasted with a new method that integrates terrain variations with the concept of gamma naught (γ<sup>0</sup>) backscatter, converting directly from beta naught (β<sup>0</sup>) to a newly introduced terrain-flattened γ<sup>0</sup> normalization convention. The interpretability of imagery treated in this manner is improved in comparison to processing based on conventional ellipsoid or local incident angle based σ<sup>0</sup> normalization.