Nancy F. Glenn Temuulen T. Sankey DeWayne R. Derryberry Matthew J. Germino Jessica J. Mitchell 2012 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 <i>R²</i> value of 0.72 and an <i>R²</i> predicted value of 0.42 (<i>n</i> = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased <i>R²</i> to 0.95 (<i>R²</i> 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. application/pdf 10.1016/j.rse.2012.05.002 en Elsevier Remote sensing of sagebrush canopy nitrogen article