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

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Title: Parsing the Relative Contributions of Leaf and Canopy Traits in Airborne Spectrometer Measurements.
Authors: Sullivan, Franklin B.1 (AUTHOR) franklin.sullivan@unh.edu, Hastings, Jack H.1,2 (AUTHOR), Ollinger, Scott V.1,2,3 (AUTHOR), Ouimette, Andrew1,3,4 (AUTHOR), Richardson, Andrew D.4,5 (AUTHOR), Palace, Michael1,6 (AUTHOR)
Source: Remote Sensing. Jan2026, Vol. 18 Issue 2, p355. 17p.
Subjects: Near infrared reflectance spectroscopy, Leaf area index, Leaf morphology, Optical properties, Plant canopies, Leaf physiology
Abstract: Highlights: What are the main findings? We validated a model of potential canopy reflectance representing a structurally simplified canopy using LAI-weighted optical properties, and showed a strong positive relationship with canopy %N. We derived an index of relative reflectance to quantify the effect of canopy structural complexity on whole-canopy reflectance and found that complexity reduces potential canopy NIR reflectance more in low %N stands than in high %N stands. What are the implications of the main findings? The positive correlation between canopy %N and LAI-weighted leaf NIR reflectance suggests that the relationship between canopy %N and canopy NIR reflectance arises from the integrated effects of canopy complexity acting on differences in leaf-level optical traits. The physical mechanisms underlying the relationship between canopy complexity and canopy %N require further study, but implies existing links between ecosystem biochemistry, leaf traits, and canopy growth patterns. 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. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Parsing the Relative Contributions of Leaf and Canopy Traits in Airborne Spectrometer Measurements.
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  Data: <searchLink fieldCode="AR" term="%22Sullivan%2C+Franklin+B%2E%22">Sullivan, Franklin B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> franklin.sullivan@unh.edu</i><br /><searchLink fieldCode="AR" term="%22Hastings%2C+Jack+H%2E%22">Hastings, Jack H.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ollinger%2C+Scott+V%2E%22">Ollinger, Scott V.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ouimette%2C+Andrew%22">Ouimette, Andrew</searchLink><relatesTo>1,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Richardson%2C+Andrew+D%2E%22">Richardson, Andrew D.</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Palace%2C+Michael%22">Palace, Michael</searchLink><relatesTo>1,6</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jan2026, Vol. 18 Issue 2, p355. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Near+infrared+reflectance+spectroscopy%22">Near infrared reflectance spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Leaf+area+index%22">Leaf area index</searchLink><br /><searchLink fieldCode="DE" term="%22Leaf+morphology%22">Leaf morphology</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+properties%22">Optical properties</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+canopies%22">Plant canopies</searchLink><br /><searchLink fieldCode="DE" term="%22Leaf+physiology%22">Leaf physiology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? We validated a model of potential canopy reflectance representing a structurally simplified canopy using LAI-weighted optical properties, and showed a strong positive relationship with canopy %N. We derived an index of relative reflectance to quantify the effect of canopy structural complexity on whole-canopy reflectance and found that complexity reduces potential canopy NIR reflectance more in low %N stands than in high %N stands. What are the implications of the main findings? The positive correlation between canopy %N and LAI-weighted leaf NIR reflectance suggests that the relationship between canopy %N and canopy NIR reflectance arises from the integrated effects of canopy complexity acting on differences in leaf-level optical traits. The physical mechanisms underlying the relationship between canopy complexity and canopy %N require further study, but implies existing links between ecosystem biochemistry, leaf traits, and canopy growth patterns. 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/rs18020355
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 355
    Subjects:
      – SubjectFull: Near infrared reflectance spectroscopy
        Type: general
      – SubjectFull: Leaf area index
        Type: general
      – SubjectFull: Leaf morphology
        Type: general
      – SubjectFull: Optical properties
        Type: general
      – SubjectFull: Plant canopies
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      – SubjectFull: Leaf physiology
        Type: general
    Titles:
      – TitleFull: Parsing the Relative Contributions of Leaf and Canopy Traits in Airborne Spectrometer Measurements.
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              M: 01
              Text: Jan2026
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              Y: 2026
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