Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

The nitrogen content in plant foliar tissues (foliar N) regulates photosynthetic capacity and has a major impact on global biogeochemical cycles. Despite its importance, robust, time, cost-effective methodology to estimate variation N concentration across globally represented terrestrial systems does not exist. Although advances remote sensing data have enabled landscape-scale predictions, improved accuracy is needed effectively capture ecosystems. Airborne imagery was analyzed conjunction with ground-sampled chemistry (n = 692), provided by the NEON, predict at sites United States covering variety of communities climate types. We developed indices from novel two-band combinations that predicted more accurately than existing (≈8% improvement all 45% arid sites). Compared indices, we increased decreased bias predictions using full-spectrum reflectance information partial least squares regression (PLSR) models (R2 0.638; root mean square error 0.440). Significant wavelengths included red edge (720–765 nm), near infrared (NIR) 1125 nm, shortwave (SWIR) 2050 2095 which are regions indicative traits such as growth type (e.g., leaf area index NIR) parameters chlorophyll Rubisco SWIR reflectance, respectively). With confluence rapid increases computing power, several forthcoming or recently launched hyperspectral missions, development large-scale environmental research observatories worldwide, an exciting opportunity larger spatial areas diverse biomes ever before. anticipate these will prove be invaluable helping constrain model uncertainties range