Remote sensing of sagebrush canopy nitrogen

Jessica J. Mitchell, Nancy F. Glenn, Temuulen T. Sankey, De Wayne R. Derryberry, Matthew J. Germino

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands - a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R 2 value of 0.72 and an R 2 predicted value of 0.42 (n=35). Subsetting the dataset to minimize the influence of bare ground (n=19) increased R 2 to 0.95 (R 2 predicted=0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

Original languageEnglish (US)
Pages (from-to)217-223
Number of pages7
JournalRemote Sensing of Environment
StatePublished - Sep 2012
Externally publishedYes


  • Continuum removal
  • Derivative analysis
  • Hyperspectral
  • Nitrogen
  • Sagebrush

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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