Spatial-spectral feature extraction algorithms – such as those based on spatial descriptors applied to selected spectral bands within a hyperspectral image – can provide additional discrimination capability beyond traditional spectral-only approaches. However, when attempting to detect a target with such algorithms, an exemplar target signature is often manually derived from the hyperspectral images representation in the spatial-spectral feature space. This requires a reference image in which the targets location is known. Additionally, the scenebased signature captures only the representation of the target under certain collection conditions from a specific sensor, namely, illumination level and atmospheric composition, look angle, and target pose against a specific background. A detection algorithm utilizing this spatial-spectral signature (or the spatial descriptor itself) that is sensitive to changes in these collection conditions could suffer a loss in performance should the new conditions significantly deviate from the exemplars case. To begin to overcome these limitations, we formulate and evaluate the effectiveness of a modeling technique for synthesizing exemplar spatial-spectral signatures for solid targets, particularly when the spatial structure of the target of interest varies due to pose or obscuration by the background, and when applicable, the target temperature varies. We assess the impact of these changes on a group of spatial descriptors responses to guide the modeling process for a set of two-dimensional targets specifically designed for this study. The sources of variability that most affect each descriptor are captured in target subspaces, which then form the basis of new spatial-spectral target detection algorithms.