title: [Prediction of total nitrogen and alkali hydrolysable nitrogen content in loess using hyperspectral data based on correlation analysis and partial least squares regression].
abstract: Wuqi County of Shaanxi Province, where the vegetation recovering measures have been carried out for years, was taken as the study area. A total of 100 loess samples from 24 different profiles were collected. Total nitrogen (TN) and alkali hydrolysable nitrogen (AHN) contents of the soil samples were analyzed, and the soil samples were scanned in the visible/near-infrared (VNIR) region of 350-2500 nm in the laboratory. The calibration models were developed between TN and AHN contents and VNIR values based on correlation analysis (CA) and partial least squares regression (PLS). Independent samples validated the calibration models. The results indicated that the optimum model for predicting TN of loess was established by using first derivative of reflectance. The best model for predicting AHN of loess was established by using normal derivative spectra. The optimum TN model could effectively predict TN in loess from 0 to 40 cm, but the optimum AHN model could only roughly predict AHN at the same depth. This study provided a good method for rapidly predicting TN of loess where vegetation recovering measures have been adopted, but prediction of AHN needs to be further studied.
authors.forename: Xiu-ying
authors.initials: XY
authors.lastname: Liu
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.forename: Qing-rui
authors.initials: QR
authors.lastname: Chang
authors.forename: Xiao-xing
authors.initials: XX
authors.lastname: Wang
authors.forename: Yan
authors.initials: Y
authors.lastname: Shang
mesh_terms: D000468:Alkalies / Q000737:chemistry; D006868:Hydrolysis; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009584:Nitrogen / Q000737:chemistry*; D012987:Soil / Q000737:chemistry*; D013057:Spectrum Analysis
chemical_list: D000468:Alkalies; D012987:Soil; D009584:Nitrogen