Optimized Estimation of Leaf Mass per Area with a 3D Matrix of Vegetation Indices

Abstract

Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was constructed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, respectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.

Plant phenotyping relevance

植物機能形質LMAを可視・近赤外観測から推定する3D植生指数行列を開発し、合成データおよび大規模実験データで検証しており、表現型取得手法が中心である。

Code and data availability

The paper's two experimental phenotyping datasets (LOPEX leaf spectra/LMA and the Madison, WI leaf spectra dataset) are explicitly stated to be publicly available on EcoSIS with direct URLs in the Data Availability Statement. No author analysis code or trained model is shared.