Leaf Bidirectional Transmittance Distribution Function Estimates and Models for Select Deciduous Tree Species

Abstract

Remote sensing increasingly has become an important tool for forest management. In the development of forest metrics from remote-sensing data, currently many models omit the individual leaf bidirectional scattering distribution function (BSDF). Past studies, and the currently available data, often do not adequately incorporate transmission, cover the broader reflective domain, and/or incorporate models to extend to any illumination and view angle combination. We estimated broadleaf bidirectional transmittance distribution functions (BTDFs) in this study using the goniometer of the Rochester Institute of Technology-Two (GRIT-T), which records spectral data in the UV-A through shortwave infrared (SWIR) spectral regions (350-2500 nm). We measured three species of large tree leaves, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We accurately modeled leaf BTDF with extension to any illumination angle, viewing zenith, and azimuthal angle through nonlinear regression to a physically-based microfacet BTDF. The model fit showed a mean of less than 7% normalized root-mean-squared error (NRMSE) spectrally from 450 to 2300 nm (lower and upper wavelength range omitted due to detector noise). The microfacet models provide highly useful physical quantities such as a relative roughness, index of refraction, and absorption, which are all directly related to leaf optical properties. These physical quantities have implications for plant physiology, vegetation remote sensing, and physics-based image generation. Specifically, the accuracy of radiative transfer modeling in forest canopies depends on rigorous representations of leaves, and this increase in accuracy can lead to the development of higher fidelity data processing algorithms for remote sensing. Data and programing scripts are available at- http://dx.doi.org/10.21227/yjek-2059