Predicting leaf traits across functional groups using reflectance spectroscopy

Abstract

Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 >0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 =0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

Plant phenotyping relevance

葉の反射スペクトルから構造・化学的形質を推定する分光センシングとPLSRモデルを構築し、外部データで検証しており、植物形質取得法が研究の中心です。

Code and data availability

The paper's fresh-leaf spectral data are publicly available via the CABO data portal, and the authors' analysis scripts are on GitHub. EcoSIS/EcoSML uploads are promised only upon publication and are not yet actionable.