This paper addresses a conceptual approach to hyperspectral image assessment that uses physics-based models to constrain multiparameter inversion algorithms aimed at quantitative measurement of material properties. The new millennium will see a host of new sensing approaches that allow us to acquire many measurements of a target. These measurements may be in the form of; a single hyperspectral image with hundreds of spectral samples, a sequence of many images acquired over time from sensors with a few channels or widely different types of measurements from a range of sensor types (e.g., thermal, polarimetric, multispectral, radar ...). We propose a conceptual approach for merging these data into a common framework to allow simultaneous exploitation of these multiparameter data sets. The approach uses physics-based models to predict or constrain the range of observable parameters associated with a target or material condition and then applies model matching or optimization methods to invert a measurement set to a target type or condition. In this paper, two examples are presented for this approach using hyperspectral data sets. The first involves characterizing atmospheric constituents (pressure depth, column water vapor amount and aerosol visibility) using the MODTRAN Code to constrain the solutions. The second involves using a radiation propagation model that includes atmospheric and aquatic propagation to drive an inversion of hyperspectral data to multiple water quality parameters (chlorophyll, suspended materials and yellowing organics). Finally, we close with a discussion of how more involved three-dimensional physics-based synthetic image models may hold a key to image exploitation algorithms in the new millennium.