title: Robustness of analyses of imaging data.
abstract: Successful classifications of reflectance and vibrational data are to a large extent dependent upon robustness of input data. In this study, a well-known geostatistical approach, variogram analysis, was described and its robustness was assessed through comprehensive evaluation of 3,200 variogram settings. High-resolution hyperspectral imaging data were acquired from greenhouse maize plants, and the robustness (radiometric repeatability) of three variogram parameters (nugget, sill, and range) was examined when generated from imaging data collected from two different sets of plants and with imaging data collected on seven different days in two years. Robustness of variogram parameters was compared with average reflectance values in six spectral bands, three standard vegetation indices (NDVI, SI, and PRI), and PCA scores from principal component analysis.
authors.affiliation: Texas AgriLife Research, 1102 East FM 1294, Lubbock, TX 79403, USA. cnansen@ag.tamu.edu
authors.forename: Christian
authors.initials: C
authors.lastname: Nansen
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018515:Plant Leaves / Q000502:physiology; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013057:Spectrum Analysis / Q000379:methods; D013997:Time Factors; D003313:Zea mays / Q000502:physiology*