Hyperspectral remote sensing of foliar nitrogen content
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Other documents of the author: Knyazikhin, Yuri; Schull, Mitchell A.; Stenberg, Pauline; Mõttus, Matti; Rautiainen, Miina; Yang, Yan; Marshak, Alexander; Latorre Carmona, Pedro; Kaufmann, Robert K.; Lewis, Philip; Disney, Mathias I.; Vanderbilt, Vern; Davis, Anthony B.; Baret, Frédéric; Jacquemoud, Stéphane; Lyapustin, Alexei; Myneni, Ranga B.
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http://dx.doi.org/10.1073/pnas.1210196109 |
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Title
Hyperspectral remote sensing of foliar nitrogen contentAuthor (s)
Date
2012Publisher
National Academy of SciencesISSN
1091-6490Type
info:eu-repo/semantics/articlePublisher version
http://www.pnas.org/content/110/3/E185.full.pdfVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral
region and foliar mass-based nitrogen concentration (%N) has
been reported in some ... [+]
A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral
region and foliar mass-based nitrogen concentration (%N) has
been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in
the climate system via its influence on surface albedo and may
offer a simple approach for monitoring foliar nitrogen using
satellite data. We report, however, that the previously reported
correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar
nitrogen-poor needleleaf and nitrogen-rich broadleaf species,
whose canopy structure differs considerably. When the BRF data
are corrected for canopy-structure effects, the residual reflectance
variations are negatively related to %N at all wavelengths in the
interval 423–855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N.
Wefind that to infer leaf biochemical constituents, e.g., N content,
from remotely sensed data, BRF spectra in the interval 710–790 nm
provide critical information for correction of structural influences.
Our analysis also suggests that surface characteristics of leaves
impact remote sensing of its internal constituents. This further
decreases the ability to remotely sense canopy foliar nitrogen.
Finally, the analysis presented here is generic to the problem of
remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N. [-]
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PNAS 2013 110 (3) E185–E192; published ahead of print December 4, 2012,Rights
Copyright © 2013 National Academy of Sciences
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