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Visible and near-infrared hyperspectral indices explain more variation in lower-crown leaf nitrogen concentrations in autumn than in summer

  • Kathryn I. WheelerEmail author
  • Delphis F. Levia
  • Rodrigo Vargas
Highlighted Student Research


Autumn canopy phenological transitions are increasing in length as a consequence of climate change. Here, we assess how well hyperspectral indices in the visible and near-infrared (NIR) wavelengths predict nitrogen (N) concentrations in lower-canopy leaves in the autumn phenological transition as they are generally understudied in leaf trait research. Using a Bayesian framework, we tested how well published indices are able to predict N concentrations in Fagus grandifolia Ehrh., Liriodendron tulipifera L., and Betula lenta L. from mid-summer through senescence, and how related the indices are to autumn phenological change. No indices were able to determine a trend in differences in N in mid-summer leaves. Indices that included wavelengths in the green and NIR ranges were the first indices able to detect a trend and had among the highest correlations with N concentration in both the last green collection and the senescing collection. Models were unique when indices were fit to data from different phenophases. Indices that focused on only the red edge (i.e., the sharp increase in reflectance between the red and NIR wavelengths) had the strongest explanatory power across the autumn phenological transition, but had less explanatory power for individual collections. These indices, as well as those that have been correlated with chlorophyll (CCI) and carotenoids (PRI), were the strongest descriptors of autumn progression. This study provides insights on challenges and capabilities to monitor a leaf’s N concentration throughout and across canopy senescence.


Phenology Leaf traits Resorption Autumn Hyperspectral 



This work was funded through the University of Delaware’s research office. We thank Fair Hill Natural Resource Management Area for graciously providing a location for data collection. We also thank Gerald Poirier, Janice Hudson, Kalmia Kniel, and Alexey Shiklomanov for useful advice during the project. KW also acknowledges support under the National Science Foundation Graduate Research Fellowship (1247312). RV acknowledges support from NASA Carbon Monitoring Systems (80NSSC18K0173) and the National Science Foundation (1652594). Hyperspectral data and leaf trait measurements are available on (

Author contribution statement

KW, DL, and RV conceived and designed the experiments. KW performed the experiments and analyzed the data. KW wrote the manuscript with input and edits from DL and RV. KW, DL, and RV worked on manuscript revisions.

Supplementary material

442_2019_4554_MOESM1_ESM.docx (28 kb)
Supplementary material 1 (DOCX 28 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Geography and Spatial SciencesUniversity of DelawareNewarkUSA
  2. 2.Department of Earth and EnvironmentBoston UniversityBostonUSA
  3. 3.Department of Plant and Soil SciencesUniversity of DelawareNewarkUSA

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