Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance

作者：Kenny Helsen, Leonardo Bassi, Hannes Feilhauer, Teja Kattenborn, Hajime Matsushima, Elisa Van Cleemput, Ben Somers, Olivier Honnay · 发表于：Ecological Indicators · 年份：2021 · DOI：10.1016/j.ecolind.2021.108111 · 被引用次数：32 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Species Distribution and Climate Change

Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it remains unclear whether these reflectance-based trait predictions are accurate enough to reliably model trait variation at the intraspecific level (i.e. across individuals of one species). We explored the potential of hyperspectral leaf reflectance-based methods to predict LMA, LDMC and EWT at the intraspecific level for two herbs (Hieracium umbellatum and Jacobaea vulgaris) and two shrubs (Rosa rugosa and Rubus caesius), based on 2400 leaf samples. More specifically we tested i) inversion of the PROSPECT-D radiative transfer model, ii) a generic PLSR approach using the multibiome LMA PLSR model and iii) a data-specific PLSR approach at the species level. For the latter approach we furthermore assessed both model transferability across species and the trade-off between sample size and model accuracy. Although the PROSPECT-D model inversion and the multibiome LMA PLSR model were relatively accurate for intraspecific LMA predictions of shrubs (R2 > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R2 < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R2 < 70%, NRMSE = 16–43%...