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Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density

Broge, N.H., Leblanc, E.
Remote sensing of environment 2001 v.76 no.2 pp. 156
vegetation cover, vegetation structure, hyperspectral imagery, image analysis, leaf area index, reflectance, data analysis, canopy, chlorophyll, density, mathematical models, equations, simulation models, soil, atmosphere, environmental factors, prediction, estimation
Hyperspectral reflectance data representing a wide range of canopies were simulated using the combined PROSPECT+SAIL model. The simulations were used to study the stability of recently proposed vegetation indices (VIs) derived from adjacent narrowband spectral reflectance data across the visible (VIS) and near infrared (NIR) region of the electromagnetic spectrum. The prediction power of these indices with respect to green leaf area index (LAI) and canopy chlorophyll density (CCD) was compared, and their sensitivity to canopy architecture, illumination geometry, soil background reflectance, and atmospheric conditions were analyzed. The second soil-adjusted vegetation index (SAVI2) proved to be the best overall choice as a greenness measure. However, it is also shown that the dynamics of the VIs are very different in terms of their sensitivity to the different external factors that affects the spectral reflectance signatures of the various modeled canopies. It is concluded that hyperspectral indices are not necessarily better at predicting LAI and CCD, but that selection of a VI should depend upon (1) which parameter that needs to be estimated (LAI or CCD), (2) the expected range of this parameter, and (3) a priori knowledge of the variation of external parameters affecting the spectral reflectance of the canopy.