Predicting Organic Matter content in Korean Soils Using Regression rules on Visible-Near Infrared Diffuse Reflectance Spectra

Title & Authors
Predicting Organic Matter content in Korean Soils Using Regression rules on Visible-Near Infrared Diffuse Reflectance Spectra
Chun, Hyen-Chung; Hong, Suk-Young; Song, Kwan-Cheol; Kim, Yi-Hyun; Hyun, Byung-Keun; Minasny, Budiman;

Abstract
This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the $\small{1^{st}}$ derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.
Keywords
Soil carbon;Diffuse reflectance spectroscopy;Infrared spectroscopy;
Language
English
Cited by
1.
A global spectral library to characterize the world's soil, Earth-Science Reviews, 2016, 155, 198
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 Biol. Biochem. 43:1398-1410.

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. Am. J. 50:120-123.

3.
Minasny, B. and A. B. McBratney. 2008. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometr. Intell. Lab. 94:72-79.

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. Am. J. 55:288-291.

5.
Pozdnyakova, L, D. Gimenez, and P. Oudemans. 2005. Spatial analysis of cranberry yield at three scales. Agron. J. 97:49-57.

6.
Quinlan, J.R. 1992. Learning with continuous classes, Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, pp. 343-348.

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.

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. Environ. Pollut. 116:264-277.

9.
Wetzel, D.L. 1983. Near-infrared reflectance analysis: Sleeper among spectroscopic techniques. Anal. Chem.55:1165-1176.

10.
Wold, S., A. Ruke, H. Wold and W.J. Dunn. 1984. The collinearity problem in linear regression, the partial least squares (PLS) approach to generalized inverses. SIAM J. Sci. Stat. Comp. 5:735 -743.