This investigation explores how hyperspectral distance metrics may be used as indicators of relative water depth in a coastal region. Spectral reflectance characteristics of near-shore waters imaged by an airborne hyperspectral sensor are examined. Commonly used hyperspectral distance metrics are applied to the data with the goal of distinguishing the spectra derived from various water depths. To improve the separability of the spectra, this study also examines, for one distance metric, the effect of processing only a subset of spectral bands recorded by the sensor. The concept of selecting a subset of bands extends to improving the performance of algorithms that process hyperspectral data for detection, classification, or estimation. An additional benefit is reducing the dimensionality of the dat and, thereby, the computational load. The key to reaching both of these objectives is to understand and match physical processes to appropriate mathematical metrics performance measures in a comprehensive framework. The overall process is driven both by empirical analysis of hyperspectral data and by mathematical examination of the spectra.