A compelling figure-of-merit for synthetic sensor data generation is target acquisition performance relative to field data of similar scenarios. That is, if one synthesized a sensor data set from the ground truth of an actual field measurement, one would expect a realistic simulation to give rise to probabilities of target detection and identification, and false alarm rates comparable to that of the field data. This correlation would, of course, be expected to extend to both human and machine-based performance. Key to this correlation is realistic background synthesis, providing appropriate spatial and spectral competition with the target signatures for both man and algorithm. Hyperspectral target signature synthesis is fairly mature, while background modeling for target- competitive clutter leaves much to be desired. The authors herein detail a ray-tracing approach for rigorous hyperspectral background signature synthesis that focuses on trees and forest canopies, and synthesis techniques for producing spatial-spectral statistics consistent with field data. In addition, the authors present some of the hyperspectral synthesis components, including the signature model, which can be used in a multi-spectral mode for real- time EO/IR image synthesis.