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Updated: May 16, 2026
11:37
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
Published on: August 8, 2017
Hyperspectral remote sensing of foliar nitrogen content.
Yuri Knyazikhin1, Mitchell A Schull, Pauline Stenberg
1Department of Earth and Environment, Boston University, Boston, MA 02215, USA. jknjazi@bu.edu
Summary
A previously reported positive correlation between vegetation reflectance and foliar nitrogen is an artifact of canopy structure. Corrected data show a negative relationship, limiting satellite monitoring of foliar nitrogen.
Area of Science:
- Ecology
- Remote Sensing
- Biogeochemistry
Background:
- A positive correlation between near-infrared (NIR) vegetation canopy bidirectional reflectance factor (BRF) and foliar nitrogen concentration (%N) has been suggested for temperate and boreal forests.
- This relationship could imply nitrogen's role in climate regulation via surface albedo and enable satellite-based %N monitoring.
Purpose of the Study:
- To investigate the artifactual nature of the reported positive correlation between vegetation BRF and foliar %N.
- To determine the influence of canopy structure on this relationship and explore alternative methods for remote sensing of foliar %N.
Main Methods:
- Analysis of BRF data from sites with varying needleleaf and broadleaf species proportions.
- Correction of BRF data for canopy structure effects.
- Examination of residual reflectance variations in relation to %N across the 423-855 nm spectrum.
Main Results:
- The previously reported positive correlation between BRF and %N is an artifact of differing canopy structures, not %N itself.
- After correcting for canopy structure, reflectance variations show a negative relationship with %N across the 423-855 nm interval.
- BRF spectra between 710-790 nm are crucial for correcting structural influences in remote sensing of leaf biochemical constituents.
Conclusions:
- The observed positive correlation between BRF and %N provides no reliable information about foliar nitrogen content.
- Leaf surface characteristics significantly impact remote sensing of internal leaf constituents, further complicating canopy foliar nitrogen estimation.
- The findings are broadly applicable to remote sensing of leaf-tissue constituents, not solely foliar %N.
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