Parsing the Relative Contributions of Leaf and Canopy Traits in Airborne Spectrometer Measurements

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involved points to structural properties that influence scattering and covary with %N. Despite this, efforts that have focused on commonly measured structural properties such as leaf area index (LAI) have failed to identify a causal mechanism. Here, we sought to understand how lidar-derived canopy traits related to additional properties of foliar arrangement and structural complexity modulate the effects of leaf spectra and leaf area index (LAI) on canopy reflectance. We developed a leaf layer spectra model to explore how canopy reflectance would change if complex foliage arrangements were removed, compressing the canopy into optically dense, uniform stacked layers while maintaining the same leaf area index. Model results showed that LAI-weighted leaf reflectance saturates at a leaf area index of approximately two for needleleaf species and four for broadleaf species. When upscaled to estimate plot-level canopy reflectance in the absence of structural complexity (NIRrLAI), results showed a strong positive relationship with canopy %N (r2 = 0.86), despite a negative relationship for individual leaves or “big-leaf” canopies with an LAI of one (NIRrL, r2 = 0.78). This result implies that the relationship between canopy near-infrared reflectance and canopy %N results from the integrated effects of canopy complexity acting on differences in leaf-level optical properties. We introduced an index of relative reflectance (IRr) that shows that the relative contribution of structural complexity to canopy near-infrared reflectance (NIRrC) is related to canopy %N (r2 = 0.55), with a three-fold reduction from potential canopy near-infrared reflectance observed in stands with low %N compared to a two-fold reduction in stands with high %N. These findings support the hypothesis that the correlation between canopy %N and canopy reflectance is the result of interactions between leaf traits and canopy structural complexity.

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Publication Details

Journal
Remote Sensing
Published
2026-01-21
DOI
https://doi.org/10.3390/rs18020355
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Parsing the Relative Contributions of Leaf and Canopy Traits in Airborne Spectrometer Measurements

Franklin B. Sullivan, Michael Palace, Jack Hastings, Scott V. Ollinger et al.
Remote Sensing
Remote Sensing in Agriculture
article

Parsing the Relative Contributions of Leaf and Canopy Traits in Airborne Spectrometer Measurements

Franklin B. Sullivan, Michael Palace, Jack Hastings, Scott V. Ollinger, Andrew P. Ouimette, Andrew D. Richardson
article en

Abstract

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involved points to structural properties that influence scattering and covary with %N. Despite this, efforts that have focused on commonly measured structural properties such as leaf area index (LAI) have failed to identify a causal mechanism. Here, we sought to understand how lidar-derived canopy traits related to additional properties of foliar arrangement and structural complexity modulate the effects of leaf spectra and leaf area index (LAI) on canopy reflectance. We developed a leaf layer spectra model to explore how canopy reflectance would change if complex foliage arrangements were removed, compressing the canopy into optically dense, uniform stacked layers while maintaining the same leaf area index. Model results showed that LAI-weighted leaf reflectance saturates at a leaf area index of approximately two for needleleaf species and four for broadleaf species. When upscaled to estimate plot-level canopy reflectance in the absence of structural complexity (NIRrLAI), results showed a strong positive relationship with canopy %N (r2 = 0.86), despite a negative relationship for individual leaves or “big-leaf” canopies with an LAI of one (NIRrL, r2 = 0.78). This result implies that the relationship between canopy near-infrared reflectance and canopy %N results from the integrated effects of canopy complexity acting on differences in leaf-level optical properties. We introduced an index of relative reflectance (IRr) that shows that the relative contribution of structural complexity to canopy near-infrared reflectance (NIRrC) is related to canopy %N (r2 = 0.55), with a three-fold reduction from potential canopy near-infrared reflectance observed in stands with low %N compared to a two-fold reduction in stands with high %N. These findings support the hypothesis that the correlation between canopy %N and canopy reflectance is the result of interactions between leaf traits and canopy structural complexity.

Remote SensingVol. 18(2)
University of New Hampshire (US), Northern Arizona University (US), Northern Research Station (US), Louisiana Department of Natural Resources (US)
Life in Land
Openalex Percentile: Top 84%
Remote Sensing in Agriculture
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