title: Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches.
abstract: Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325-1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R(2) (LL R(2)) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.
authors.affiliation: Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Research Unit for Cropping System in Dry Environments, Bari, Italy. annamaria.stellacci@entecra.it.
authors.forename: A M
authors.initials: AM
authors.lastname: Stellacci
authors.affiliation: Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Research Unit for Cropping System in Dry Environments, Bari, Italy. annamaria.castrignano@entecra.it.
authors.forename: A
authors.initials: A
authors.lastname: Castrignanò
authors.affiliation: Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Cereal Research Centre, Foggia, Italy. antonio.troccoli@entecra.it.
authors.forename: A
authors.initials: A
authors.lastname: Troccoli
authors.affiliation: Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University, 13 East Lansing, 48823, MI, USA. basso@msu.edu.
authors.forename: B
authors.initials: B
authors.lastname: Basso
authors.affiliation: National Research Council of Italy, Institute for Agriculture and Forest Systems in the Mediterranean (CNR-ISAFOM), Rende (CS), Italy. gabriele.buttafuoco@cnr.it.
authors.forename: G
authors.identifier: http://orcid.org/0000-0002-0280-8658
authors.initials: G
authors.lastname: Buttafuoco
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D004784:Environmental Monitoring / Q000379:methods*; D016018:Least-Squares Analysis; D016014:Linear Models; D008962:Models, Theoretical; D009584:Nitrogen / Q000032:analysis*; D018515:Plant Leaves / Q000737:chemistry; D014908:Triticum / Q000737:chemistry*
chemical_list: D009584:Nitrogen
keywords: Feature selection; Hyperspectral proximal sensing; Multiple linear regression (MLR); Nitrogen stress detection; Partial least square (PLS) regression; Principal component analysis (PCA)