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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #288980
Title: Hyperspectral remote sensing of foliar nitrogen content

Author
item KNYAZIKHIN, Y - Boston University
item Schull, Mitchell
item STENBERG, P - University Of Helsinki
item MOTTUS, M - University Of Helsinki
item RAUTIAINEN, M - University Of Helsinki
item YANG, Y - Boston University
item MARSHAK, A - National Aeronautics And Space Administration (NASA)
item CARMONA, P - Collaborator
item KAUFMANN, R - Boston University
item LEWIS, P - Collaborator
item VANDERBILT, V - National Aeronautics And Space Administration (NASA)
item DAVIS, F - Collaborator
item BARET, J - Collaborator
item JACQUEMOUD, S - Collaborator
item LYAPUSTIN, A - National Aeronautics And Space Administration (NASA)
item MYNENI, R - National Aeronautics And Space Administration (NASA)

Submitted to: Proceedings of the National Academy of Sciences (PNAS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/30/2012
Publication Date: 12/4/2012
Citation: Knyazikhin, Y., Schull, M.A., Stenberg, P., Mottus, M., Rautiainen, M., Yang, Y., Marshak, A., Carmona, P.L., Kaufmann, R., Lewis, P., Vanderbilt, V.C., Davis, F., Baret, J., Jacquemoud, S., Lyapustin, A., Myneni, R.B. 2012. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences. 110(3):E185-E192.

Interpretive Summary: The importance of nitrogen in terrestrial ecosystem carbon dynamics and related potential climatic feedbacks is well known. The interaction between carbon and nitrogen at the leaf level is among one of the fundamental mechanisms controlling the dynamics of terrestrial carbon cycle. The angular patterns of reflected radiation by vegetation, also known as Bidirectional Reflectance Factor (BRF), that is measured by satellite-borne sensors describes surface reflective properties of the vegetation and is a standard product from a new generation of earth observing satellites. A strong positive correlation between vegetation canopy BRF and canopy nitrogen concentration (%N) has been reported in some temperate and boreal forests. However, based on an analysis of the same data for the said study, the reported correlation is an artifact – it is a consequence of variations in canopy structure affecting BRF rather than %N. This study finds that the three-dimensional (3D) radiative transfer theory provides the most physically-consistent linkage between leaf scattering and canopy reflectance and therefore should be an integral part of processing remote sensing data to account for the effects of canopy structure on relationships between remotely-sensed BRF and plant nitrogen and carbon concentrations. This will lead to significant advances in understanding climate-vegetation feedbacks and potential impacts of climate change on agricultural crop production.

Technical 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 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 %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'to'855'nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that in order to infer leaf biochemical constituents, e.g. N content, from remotely sensed data, BRF spectra in the interval 710'to'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.