Research Article | Open Access

Volume 2026 |Article ID 100135 | https://doi.org/10.1016/j.plaphe.2025.100135

Leaf bidirectional reflectance distribution function (BRDF) prediction with phenotypic traits in four species: Development of a novel measuring and analyzing framework

Liangchao Deng,^1,2 Leo Xinqi Yu,² Linxiong Mao,² Yanjie Wang,² Xiyue Guo,³ Minjuan Wang,³ Yali Zhang ,¹ Qingfeng Song ,² and Xin-Guang Zhu ²

¹The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi University, Shihezi, 832003, China
²Key Laboratory of Plant Carbon Capture, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
³Key Lab of Smart Agriculture Systems, Ministry of Education, College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

Abstract

Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a “smart” canopy, characterized by erect upper-layer leaves and flat lower-layer leaves, can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture. Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency. In this study, we developed a strategy to estimate the leaf optical properties based on leaf anatomical features. We developed a Directional Spectrum Detection Instrument (DSDI) system and associated Bidirectional Reflectance Distribution Function (BRDF) analysis software to precisely describe leaf light distribution. BRDF parameters were quantified with high accuracy () for adaxial and abaxial surfaces of maize, rice, cotton, and poplar leaves across canopy layers. Leaf phenotypic traits, surface roughness, pigments content, specific leaf weight and thickness were also assessed. Ensemble learning (EL) model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R2 between 0.83 and 0.99. Compared to existing BRDF measurement systems, the DSDI achieves broader angular coverage (-π/36 to 35π/36) via mechanical rotation design, and the ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits. This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties, which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.