Speeding up 3D radiative transfer simulations: A physically based metamodel of canopy reflectance dependency on wavelength, leaf biochemical composition and soil reflectance

Abstract

A physically based metamodel is proposed to describe the dependency of canopy reflectance on the wavelength, leaf and soil optical properties. The four-stream solution is first applied to describe the interaction between the soil background and the vegetation layers. This leads to the calibration of four terms for a given canopy structure, observation configuration and leaf properties. This number can be reduced to two terms by using a linear approximation which shows a slight degradation when the multiple scattering contribution is significant. The dependency of each of the two or four terms on wavelength and leaf properties is described using the leaf total absorption coefficient. Our approach requires only 12 (linear approximation) to 24 (four-stream solution) simulations of a reference model to describe the full canopy reflectance dependency on wavelength, leaf and soil properties. The approach was evaluated against reference canopy reflectance simulations using the ray tracing LuxCoreRender model. LuxCoreRender was first compared against reference radiative transfer models. The reference dataset corresponds to a range of detailed 3D maize canopies showing variation of leaf and background properties and one heterogeneous scene including vegetation elements of different shapes that is classically used for model inter-comparison exercises under different view and sun directions in a set of wavebands. Results demonstrate that our approach provides an accurate description of the dependency of canopy reflectance on wavelength, leaf and soil properties with RMSE = 0.0017 for the four-stream solution and RMSE = 0.0022 for the linear approximation. The proposed approach appears therefore computationally effective and well suited to generate a large number of canopy reflectance simulations with detailed 3D radiative transfer models that can be used to retrieve vegetation characteristics from remote sensing observations.