PNNL

Abstract

Radiative transfer models (RTMs) describe how light is absorbed, scattered, and transmitted within leaves by simulating the mechanistic light propagation processes. The PROSPECT model series, one of the most widely used leaf-scale RTMs, relies on a structure parameter (N), which cannot be measured directly. This limits the ability of RTMs to explore the impacts of leaf structure on its spectrum. In this study, we leveraged eLeaf, a ray tracing-based 3D rice leaf simulator, to establish relationships between leaf anatomical features and spectral properties, enabling us to replace N in the PROSPECT model with measurable leaf anatomical parameters and develop the RSPECT model. Results showed that leaf thickness at minor vein, leaf thickness at bulliform cells, mesophyll thickness at minor vein, and distance between two minor veins could be used to predict N effectively. The RESPECT model achieved spectral simulation accuracy comparable to PROSPECT and was more suitable for parameter inversion of physical and chemical properties of rice leaves, with relative root mean square errors of 7.4% for chlorophyll content, 5.6% for equivalent water thickness, and 7.5% for dry matter content. In conclusion, RESPECT improves radiative transfer modeling by integrating measurable anatomical features and provides a framework for extending this approach to other vegetation types.