Quantifying leaf optical properties with spectral invariants theory

Abstract

Leaf optical spectra reflect the combination of leaf biochemical, morphological and physiological properties, and play an important role in many ecological and Earth system processes. Radiative transfer models are widely used to simulate leaf spectra by quantifying photon transfer processes of reflection, transmission and absorption within a plant leaf. Recent advances in spectral invariants theory offer a unique and efficient approach for modeling the canopy-scale radiative transfer processes, but remain underexplored for applications at the leaf scale. In this study, we developed a leaf-scale optical property model based on the spectrally invariant properties (leaf-SIP) of plant leaves. Similar to the canopy-scale model, the leaf-SIP model decouples leaf-scale radiative transfer process into two parts: wavelength-dependent contribution from leaf chemical components and wavelength-independent contribution from leaf structures, described by two spectrally invariant parameters (i.e., a photon recollision probability p and a scattering asymmetry parameter q). We implemented the leaf-SIP model by parameterizing p and q with a measurable leaf morphological trait, the leaf mass per area (LMA). We evaluated the performance of the leaf-SIP model with two in situ datasets (i.e., LOPEX and ANGERS) and the widely used PROSPECT leaf optical model. The results show that the leaf spectra simulated by the leaf-SIP model agreed well with in situ datasets and the simulations of the PROSPECT model, with a small root mean squared error (RMSE), bias, and high coefficients of determination (R2) of 0.026, 0.035, 0.95 and 0.037, 0.049, 0.91 for leaf reflectance and leaf transmittance, respectively. Our results also show that the leaf-SIP model can be used with measured leaf spectra to accurately estimate several key leaf functional traits, such as the leaf chlorophyll content, equivalent water thickness, and LMA. The leaf-SIP model provides an efficient and physical way of accurately simulating leaf spectra and retrieving key leaf functional traits from hyperspectral measurements.