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Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape  probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne  Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR  multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models  achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We  hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle  spectral data alone therefore may not provide enough information to retrieve canopy height globally. Citation: Schull, M. A., et al. (2007), Physical interpretation of the  correlation between multi-angle spectral data and canopy height.