JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Predicting Soil Chemical Properties with Regression Rules from Visible-near Infrared Reflectance Spectroscopy
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Predicting Soil Chemical Properties with Regression Rules from Visible-near Infrared Reflectance Spectroscopy
Hong, Suk Young; Lee, Kyungdo; Minasny, Budiman; Kim, Yihyun; Hyun, Byung Keun;
  PDF(new window)
 Abstract
This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.
 Keywords
Soil chemical properties;visible-near infrared reflectance;spectroscopy;PCA;PSLR;Cubist;
 Language
English
 Cited by
 References
1.
Bellon-Maurel, V. and A. McBratney. 2011 Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - Critical review and research perspectives. Soil Biology and Biochemistry. 43: 1398-1410. crossref(new window)

2.
Dalal, R. C., and R. J. Henry. 1986. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Sci. Soc. of America J. 50: 120-123. crossref(new window)

3.
Minasny, B., and A. B. McBratney. 2008. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems. 94:72-79. crossref(new window)

4.
Morra, M. J., M. H. Hall., and L. L. Freeborn. 1991. Carbon and nitrogen analysis of soil fractions using near infrared reflectance spectroscopy. Soil Sci. Soc. of America J. 55:288-291. crossref(new window)

5.
NIAST. 2000. Methods of soil and crop plant analysis. National Institute of Agricultural Science and Technology. Suwon, Korea.

6.
Pozdnyakova, L., D. Gimenez,. and P. Oudemans. 2005. Spatial analysis of cranberry yield at three scales. Agronomy J. 97:49-57. crossref(new window)

7.
Reeves III, J. B. 2010. Near versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: Where are we and what needs to be done? Geoderma. 158:3-14. crossref(new window)

8.
Reeves III, J. B., G. W. McCarty,. and T. Mimmo. 2002. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soil. Environmental Pollution. 116: 264-277.

9.
Ryu, K. S., R. K. Cho, W. C. Park, and B. J. Kim. 2001. Use of NIR analyzer for measuring chemical properties of field soil. Korean J. of Soil Science and Fertilizer. 34(4):278-283.

10.
Wetzel, D. L. 1983. Near-infrared reflectance analysis: Sleeper among spectroscopic techniques. Anal. Chemistry. 55:1165-1176. crossref(new window)