Table of Contents Author Guidelines Submit a Manuscript
Journal of Spectroscopy
Volume 2017, Article ID 1375158, 9 pages
Research Article

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

1College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China
2Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China

Correspondence should be addressed to Tashpolat Tiyip; moc.361@ujx_hsat

Received 16 February 2017; Accepted 11 April 2017; Published 13 June 2017

Academic Editor: Tino Hofmann

Copyright © 2017 Jingzhe Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg−1) and RMSEP (2.70 g kg−1) and highest (0.92), (0.91), and RPD (3.42 > 3.0). This model had excellent predictive performance of estimating SOM content in the study area.