Search for: All records

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. 

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes.With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America.Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently.Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.