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Prediction of Soil Organic Matter Content Under Moist Conditions Using VIS-NIR Diffuse Reflectance Spectroscopy

Wang, Chang-kun1,2; Pan, Xian-zhang1; Wang, Miao3; Liu, Ya1,2; Li, Yan-li1,2; Xie, Xian-li1; Zhou, Rui1; Shi, Rong-jie1,2

doi: 10.1097/SS.0b013e3182986735
Technical Article

Soil moisture is known to influence the accuracy of predictions of soil organic matter (SOM) by visible and near-infrared diffuse reflectance spectroscopy. However, the predictions may still be sufficient for analysis under specific moisture conditions with acceptable accuracy. Our study aimed to assess the accuracy of predicting SOM under various moisture conditions and to explore the appropriate soil moistures for reliable predictions. In this study, reflectance spectra (380–2,400 nm) of 62 soil samples were measured in the laboratory under various moisture conditions. Partial least-squares regression was used to build the calibration model between the first-derivative spectra and Log (SOM). The results showed that visible and near-infrared method was capable of predicting SOM content under moist conditions and that the prediction was reliable when soil moisture was less than 22% (wt/wt). The results of this study identify the potential to predict SOM under wet soil conditions in areas with loam soils and should help determine the range of soil moisture appropriate in SOM predictions for future research.

1Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.

2University of Chinese Academy of Sciences, Beijing, China.

3Chengdu Center for Food and Drug Control, Chengdu, China.

Address for correspondence: Dr. Xian-zhang Pan, Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, No. 71, East Beijing Rd., Nanjing 210008, China. E-mail:

Financial Disclosures/Conflicts of Interest: This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA05050509), the National Natural Science Foundation of China (No. 41071140), and funds from the Institute of Soil Science (No. Y112000016).

Received September 12, 2012.

Accepted for publication April 23, 2013.

© 2013Wolters Kluwer Health | Lippincott Williams & Wilkins