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Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves

Blackburn, G. A.·1998·International Journal of Remote Sensing

Abstract

The possibility of estimating the concentration of individual photosynthetic pigments within vegetation from reflectance spectra offers great promise for the use of remote sensing to assess physiological status, species type and productivity. This study evaluates a number of spectral indices for estimating pigment concentrations at the leaf scale, using samples from deciduous trees at various stages of senescence. Two new indices (PSSR and PSND) are developed which have advantages over previous techniques. The optimal individual wavebands for pigment estimation are identified empirically as 680nm for chlorophyll a, 635nm for chlorophyll b and 470nm for the carotenoids. These wavebands are justified theoretically and are shown to improve the performance of many of the spectral indices tested. Strong predictive models are demonstrated for chlorophyll a and b, but not for the carotenoids and the paper explores the reasons for this.

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Metadata

Authors
Blackburn, G. A.
Venue
International Journal of Remote Sensing
Published
1998-01
Volume
19
Issue
4
Pages
657-675
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W2098247895
Cited by
685
References
0
FWCI
5.18
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. https://app.cochat.ai/papers/10.1080/014311698215919
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Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing. https://doi.org/10.1080/014311698215919

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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.