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The red edge of plant leaf reflectance

HORLER, D. N. H., DOCKRAY, M., BARBER, J.·1983·International Journal of Remote Sensing

Abstract

The red edge is the sharp change in leaf reflectance between 680 and 750 nm and has been measured on leaves of a variety of species by first derivative reflectance spectrophotometry. A parameter λre was defined as the wavelength of maximum slope and found to be dependent on chlorophyll concentration (p<0.001), with additional effects of species, developmental stage, leaf layering and leaf water content. The maximum slope parameter was found to be independent of simulated ground area coverage. Often two λre components were identified and both showed a linear relationship with the chlorophyll content of the leaves. The results have been interpreted in terms of Beer's Law and Kubelka-Munk theory. The chlorophyll concentration dependence of λre seems to be explained in terms of a pure absorption effect, and it is suggested that the existence of two λre components arises from the additional factor of leaf scattering properties. In comparison to broad band reflectance, the results indicate that red edge measurements will be valuable for assessment of vegetative chlorophyll status and leaf area index independently of ground cover variations, and are particularly suitable for early stress detection.

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Metadata

Authors
HORLER, D. N. H., DOCKRAY, M., & BARBER, J.
Venue
International Journal of Remote Sensing
Published
1983-01
Volume
4
Issue
2
Pages
273-288
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W1987097445
Cited by
1,187
References
0
FWCI
4.42
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The red edge of plant leaf reflectance. https://app.cochat.ai/papers/10.1080/01431168308948546
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HORLER, D. N. H., DOCKRAY, M., & BARBER, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing. https://doi.org/10.1080/01431168308948546

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.