The Use of Two-Stream Approximations for the Parameterization of Solar Radiative Energy Fluxes through Vegetation

Abstract

Two-stream approximations have been used widely and for a long time in the field of radiative transfer through vegetation in various contexts and in the last 10 years also to model the hemispheric reflectance of vegetated surfaces in numerical models of the earth-atmosphere system. For a plane-parallel and turbid vegetation medium, the existence of rotational invariance allows the application of a conventional two-stream approximation to the phase function, based on an expansion in Legendre Polynomials. Three conditions have to be fulfilled to nuke this reduction possible in the case of vegetation. The scattering function of single leaves must be bi-Lambertian, the azimuthal distribution of leaf normals must be uniform, and the azimuthally averaged Leaf Area Normal Distribution (LAND) must be either uniform or planophile. The first and second assumptions have been shown to he acceptable by other researchers and. in fact, are usually assumed explicitly or implicitly when dealing with radiative transfer through canopies. The third one, on the shape of the azimuthally averaged LAND, although investigated before, is subjected to a detailed sensitivity test in this study, using a set of synthetic LAND's as well as experimental data for 17 plant canopies. It is shown that the radiative energy flux equations are relatively insensitive to the exact form of the LAND. The experimental Ross functions and hemispheric reflectances lie between those for the synthetic cases of planophile and erectophile LANDS. However, only the uniform and planophile LANDs lead to canopy hemispheric reflectances, which are markedly different from one another. The analytical two-stream solutions for the either the planophile or the uniform LAND cases may be used to model the radiative fluxes through plant canopies in the solar spectral range. The choice between the two for any particular case must he made on the basis of experimental data.