Huang, D, Knyazikhin, Y, Dickinson, RE, Rautiainen, M, Stenberg, P, Disney, M, Lewis, P, Cescatti, A, Tian, YH, Verhoef, W, Martonchik, JV, Myneni, RB (2007). Canopy spectral invariants for remote sensing and model applications. REMOTE SENSING OF ENVIRONMENT, 106(1), 106-122.
The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral transmittance and reflectance become wavelength independent and determine a small set of canopy structure specific variables. This set includes the canopy interceptance, the recollision and the escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to the incident solar radiation at the leaf and the canopy scale and allow for a simple and accurate parameterization for the partitioning of the incoming radiation into canopy transmission, reflection and absorption at any wavelength in the solar spectrum. This paper presents a solid theoretical basis for spectral invariant relationships reported in literature with an emphasis on their accuracies in describing the shortwave radiative properties of the three-dimensional vegetation canopies. The analysis of data on leaf and canopy spectral transmittance and reflectance collected during the international field campaign in Flakaliden, Sweden, June 25-July 4, 2002 supports the proposed theory. The results presented here are essential to both modeling and remote sensing communities because they allow the separation of the structural and radiometric components of the measured/modeled signal. The canopy spectral invariants offer a simple and accurate parameterization for the shortwave radiation block in many global models of climate, hydrology, biogeochemistry, and ecology. In remote sensing applications, the information content of hyperspectral data can be fully exploited if the wavelength-independent variables can be retrieved, for they can be more directly related to structural characteristics of the three-dimensional vegetation canopy. (c) 2006 Elsevier Inc. All rights reserved.