We consider the problem of acquiring models for unknown materials in airborne 0.4?m-2.5?m hyperspectral imagery and using these models to identify the unknown materials in image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.