Characterizing foliar phenolic compounds and their absorption features in temperate forests using leaf spectroscopy

Rui Xie* (Corresponding Author), R. Darvishzadeh, A.K. Skidmore, F.D. van der Meer

*Corresponding author for this work

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Abstract

Phenolic compounds constitute an essential part of the plant's secondary metabolites and play a crucial role in ecosystem functioning, including nutrient cycling and plant defence against biotic and abiotic stressors. Quantifying the phenolic compounds across global biomes is important for monitoring the biological diversity and ecosystem processes. However, our understanding of foliar phenolic compounds remains limited, particularly regarding how they vary among temperate tree species and whether their variation and absorption features can be assessed using spectroscopy at the leaf level. In this study, we examined the relationships between the spectral properties of fresh leaves from temperate tree species and two ecologically important phenolic compounds (i.e., total phenol and tannin). We sampled the leaves of four dominant tree species (i.e., English oak, European beech, Norway spruce, and Scots pine) across two European temperate forest sites. Continuum removal was applied to the leaf spectra to enhance the assessment of the subtle absorption features that correlate with the phenolic content. Total phenol and tannin concentrations were estimated by comparing the performance of two empirical methods, namely partial least squares regression (PLSR) and Gaussian processes regression (GPR). Our results showed a large range of variation in total phenol and tannin between temperate tree species (p < 0.05). Spectral analysis revealed persistent and distinct phenolic absorption features near 1666 nm in the spectra of English oak, Norway spruce and European beech, whereas Scots pine exhibited a weaker absorption feature near 1653 nm. Regression results showed that both PLSR and GPR accurately estimated total phenol and tannin across temperate tree species, with informative bands for predicting these two traits well-corresponded between the two models utilised. Our results also suggested that total phenol was overall more accurately predicted than tannin regardless of employed methods. The most accurate estimations were achieved using PLSR with the continuum-removed SWIR spectra (total phenol: R 2=0.79, NRMSE=9.95%; tannin: R 2=0.59, NRMSE=14.53%). Testing the models established for individual species or forest types revealed variability in their prediction performances, with these specific models demonstrating lower accuracy (R 2=0.47–0.69 and 0.34–0.54 for total phenol and tannin, respectively) compared to the cross-species model. Our study extends the understanding of absorption features of phenolic compounds in common temperate tree species and demonstrates the potential for a generalised spectroscopy model to predict foliar phenolic compounds across temperate forests. These findings provide a foundation for mapping and monitoring phenolic compounds in temperate forests at the canopy level using airborne and spaceborne imaging spectroscopy.

Original languageEnglish
Pages (from-to)338-356
Number of pages19
JournalISPRS journal of photogrammetry and remote sensing
Volume212
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Plant phenolics
  • Foliar biochemistry
  • Temperate forests
  • Hyperspectral remote sensing
  • Partial least square regression (PLSR)
  • Machine learning
  • ITC-HYBRID
  • UT-Hybrid-D
  • ITC-ISI-JOURNAL-ARTICLE

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