Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance. (November 2021)

• Record Type: Journal Article
• Authors: Helsen, Kenny; Bassi, Leonardo; Feilhauer, Hannes; Kattenborn, Teja; Matsushima, Hajime; Van Cleemput, Elisa; Somers, Ben; Honnay, Olivier
• Is Part Of: Ecological indicators. Volume 130(2021)
• Publication Date: 2021-11
• Subjects: Intraspecific trait variation -- Leaf dry matter content -- Leaf mass per area -- Leaf water content -- Equivalent water thickness -- Partial least squares regression (PLSR) -- PROSPECT; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.108111
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Hyperspectral data is used to collect leaf traits across species. We show that hyperspectral data can also quantify traits at the intraspecific level. We explore 3 methods across 3 traits and 2399 leaves of 2 herb and 2 shrub species. Species-specific models were accurate for all traits (R 2 > 70%). Model transferability across species seemed relatively limited. Abstract: 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 (R 2 > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R 2 < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R 2 < 70%, NRMSE = 16–43%), and unsuccessful in retrieving LDMC through combining LMA and EWT inversion results (R 2 < 10%, NRMSE = 9–192%). The highest correlation accuracy was obtained for all three traits with the species-specific PLSR models (R 2 > 70%, NRMSE < 10%). If high predictive accuracy is needed, we thus suggest the use of species-specific PLSR models. The training of species-specific PLSR models comes at the cost of a needed sample size of 100–160 leaves however, depending on the trait. Although transferability of species-specific PLSR models seems limited overall, our results suggest potentially high transferability across herbaceous species.