The appearance of operation hyperspectral imaging spectrometers in both solar and thermal regions has lead to the development of a variety of spectral detection algorithms. The development and testing of these algorithms requires well characterized field collection campaigns that can be time and cost prohibitive. Radiometrically robust synthetic image generation (SIG) environments that can generate appropriate images under a variety of atmospheric conditions and with a variety of sensors offers an excellent supplement to reduce the scope of the expensive field collections. In addition, SIG image products provide the algorithm developer with per-pixel truth, allowing for improved characterization of the algorithm performance. To meet the needs of the algorithm development community, the image modeling community needs to supply synthetic image products that contain all the spatial and spectral variability present in real world scenes, and that provide the large area coverage typically acquired with actual sensors. This places a heavy burden on synthetic scene builders to construct well characterized scenes that span large areas. Several SIG models have demonstrated the ability to accurately model targets (vehicles, buildings, etc.) Using well constructed target geometry (from CAD packages) and robust thermal and radiometry models. However, background objects (vegetation, infrastructure, etc.) dominate the percentage of real world scene pixels and utilizing target building techniques is time and resource prohibitive. This paper discusses new methods that have been integrated into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to characterize backgrounds. The new suite of scene construct types allows the user to incorporate both terrain and surface properties to obtain wide area coverage. The terrain can be incorporated using a triangular irregular network (TIN) derived from elevation data or digital elevation model (DEM) data from actual sensors, temperature maps, spectral reflectance cubes (possible derived from actual sensors), and/or material and mixture maps. Descriptions and examples of each new technique are presented as well as hybrid methods to demonstrate target embedding in real world imagery.