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Plant traits are often measured in the field or laboratory to characterize stress responses. However, direct measurements are not always cost effective for broader sampling efforts, whereas indirect approaches such as reflectance spectroscopy could offer efficient and scalable alternatives. Here, we used field spectroscopy to assess whether (1) existing vegetation indices could predict leaf trait responses to heat stress, or if (2) partial least squares regression (PLSR) spectral models could quantify these trait responses. On several warm, sunny days, we measured leaf trait responses indicative of photosynthetic mechanisms, plant water status, and morphology, including electron transport rate (ETR), photochemical quenching (qP), leaf water potential (Ψleaf), and specific leaf area (SLA) in 51 urban trees from nine species. Concurrent measures of hyperspectral leaf reflectance from the same individuals were used to calculate vegetation indices for correlation with trait responses. We found that vegetation indices predicted only SLA robustly (R2 = 0.55), while PLSR predicted all leaf trait responses of interest with modest success (R2 = 0.36 to 0.58). Using spectral band subsets corresponding to commercially available drone-mounted hyperspectral cameras, as well as those selected for use in common multispectral satellite missions, we were able to estimate ETR, qP, and SLA with reasonable accuracy, highlighting the potential for large-scale prediction of these parameters. Overall, reflectance spectroscopy and PLSR can identify wavelengths and wavelength ranges that are important for remote sensing-based modeling of important functional trait responses of trees to heat stress over broad ranges.
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Spectral wavelength range influences the performance of chemometric models estimating various foliar functional traits
Abstract Hyperspectral reflectance can potentially be used to non‐destructively estimate a diverse suite of plant physiochemical functional traits by applying chemometric approaches to leverage absorption features related to chemical compounds and physiological processes associated with these traits. This approach has considerable implications in advancing plant physiological and chemical ecology. For complex functional traits, however, there is a lack of well‐defined absorption features and features may be unevenly distributed across the reflectance spectrum, suggesting that the influence of wavelength ranges on the performance of chemometric models is potentially important for accurately estimating foliar functional traits.Here, we investigate the influence of spectral ranges on the performance of models estimating six tree functional traits: CO2assimilation rate, specific leaf area, leaf water content and concentrations of foliar nitrogen, sugars and gallic acid. Using data collected from multiple different experiments, we quantified plant functional trait responses using standard reference measurements and paired them with proximal leaf‐level hyperspectral reflectance measurements spanning the wavelength range of 400–2400 nm. A total of 100 different wavelength range combinations were evaluated using partial least squares regression to determine the influence of wavelength range on model performance.We found that the influence of starting or ending wavelength on model performance was trait specific and better model outcomes were achieved when the starting and ending wavelengths encompassed absorption features associated with the specific leaf trait modelled. Interestingly, we found that including shortwave‐infrared wavelength ranges (1300–2500 nm) improved performance for all trait models.Collectively, our findings underscore the importance of optimal spectral range selection in enhancing the accuracy of chemometric models for specific foliar trait estimates. An emergent outcome of this work is that the approach can be used to (1) identify the important spectral features of traits that currently lack known absorption features or have multiple or weak absorption features, (2) expand the current suite of plant functional traits that can be estimated using spectroscopy and (3) ultimately advance the integration of a spectral biology approach in ecological research.
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- Award ID(s):
- 1916587
- PAR ID:
- 10659991
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 16
- Issue:
- 8
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 1703 to 1722
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/2041-210X.70100
