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Physical interpretation of the correlation between multi‐angle spectral data and canopy height
Author(s) -
Schull M. A.,
Ganguly S.,
Samanta A.,
Huang D.,
Shabanov N. V.,
Jenkins J. P.,
Chiu J. C.,
Marshak A.,
Blair J. B.,
Myneni R. B.,
Knyazikhin Y.
Publication year - 2007
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2007gl031143
Subject(s) - canopy , remote sensing , imaging spectrometer , invariant (physics) , correlation , environmental science , crown (dentistry) , spectrometer , vegetation (pathology) , imaging spectroscopy , hyperspectral imaging , mathematics , optics , physics , geology , geometry , geography , materials science , medicine , archaeology , composite material , pathology , mathematical physics
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.