Institute of Astrophysics and Atmospheric Physics, Estonian Academy of Sciences, EE2444 Tõravere, Estonia
Remote Sens. Environ. 1994, 50(2): 75-82.
The leaf optical model PROSPECT by Jacquemoud and Baret (1990), the soil reflectance spectrum representation with basis functions by Price (1990), and the skylight ratio spectral representation by McCartney (1978) have been integrated into Kuusk's (1993) fast canopy reflectance model. The resulting new multispectral canopy reflectance model describes the directional reflectance of a homogeneous vegetation canopy over 400 to 2500 nm with a high spectral resolution. The number of the input parameters of the model does not depend on the number of spectral bands used. The model has high computational efficiency, and thus it can rather easily be inverted to determine the vegetation parameters from remote optical measurements. The model is tested with the corn and soybean reflectance data of Ranson et al. (1984, 1985).
The new multispectral canopy reflectance model permits calculation of the directional reflectance of an homogeneous vegetation canopy with a high spectral resolution for the whole optical spectral region. The set of model input parameters includes 4 structural parameters, 4 geometrical and illumination parameters and 5 (to 8) parameters representing the optical properties of leaves and soil. The number of model input parameters does not depend on the number of spectral channels under consideration. The model may be used both for the theoretical analysis of the stand reflectance as a function of the biophysical and structural characteristics of the canopy (Baret et al., 1992), and for inference of vegetation parameters such as leaf area index, leaf inclination distribution, soil reflectance spectrum, leaf water and chlorophyll content using remotely sensed data. Due to limitations of the PROSPECT model we cannot analyse the influence of other pigments on the canopy reflectance spectra.
The model is computationally efficient so that calculations can be performed on a personal computer. Naturally the results of the model inversion depend on the amount of information available. We cannot expect to determine all the input parameters of the model with high accuracy from a few measurements of canopy reflectance in a small number of spectral channels.