Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

• Published in: Scientific Reports
• Main Authors: Murguzur, Francisco Javier Ancin, Bison, Marjorie, Smis, Adriaan, Bohner, Hanna, Struyf, Eric, Meire, Patrick, Bråthen, Kari Anne
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Research 2019
• Subjects: VDP::Mathematics and natural science: 400::Zoology and botany: 480; VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480; Arctic; Svalbard; Fennoscandia
• Online Access: https://hdl.handle.net/10037/16706; https://doi.org/10.1038/s41598-019-44558-9

Source at https://doi.org/10.1038/s41598-019-44558-9. Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R2 validation = 0.94 and RMSEP = 0.20% for N, R2 validation = 0.76 and RMSEP = 0.05% for P and R2 validation = 0.82 and RMSEP = 1.16% for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.