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Sciences in Cold and Arid Regions ›› 2016, Vol. 8 ›› Issue (6): 507-515.doi: 10.3724/SP.J.1226.2016.00507

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Quantitative retrieval of soil salt content based on measured spectral data

HanChen Duan, Tao Wang, Xian Xue, CuiHua Huang, ChangZhen Yan   

  1. Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2016-04-28 Revised:2016-07-29 Published:2018-11-23
  • Contact: Ph.D., Xian Xue, Professor of Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. No. 320, West Donggang Road, Lanzhou, Gansu 730000, China. E-mail: xianxue@lzb.ac.cn E-mail:xianxue@lzb.ac.cn
  • Supported by:
    This study was financially supported by the National Natural Science Foundation of China (No. 41401109), the Foundation for Excellent Youth Scholars of CAREERI, CAS (No. Y551D21001), and the Open Fund Project of the Key Laboratory of Desert and Desertification, CAS (No. Y452J71001). The authors would like to thank Enago (www.enago.cn) for the English language review.

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Abstract: Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the characteristics of saline soil spectral reflectance and its transformation in the area, and elucidated the relations between the soil spectral reflectance, reflectance transformation, and soil salt content. In addition, we screened sensitive wavebands. Then, a multiple linear regression model was established to predict the soil salt content based on the measured spectral data, and the accuracy of the model was verified using field-measured salinity data. The results showed that the overall shapes of the spectral curves of soils with different degrees of salinity were consistent, and the reflectance in visible and near-infrared bands for salinized soil was higher than that for non-salinized soil. After differential transformation, the correlation coefficient between the spectral reflectance and soil salt content was obviously improved. The first-order differential transformation model based on the logarithm of the reciprocal of saline soil spectral reflectance produced the highest accuracy and stability in the bands at 462 and 636 nm; the determination coefficient was 0.603, and the root mean square error was 5.407. Thus, the proposed model provides a good reference for the quantitative extraction and monitoring of regional soil salinization.

Key words: spectral reflectance, soil salt content, salinization, multiple linear regression, Minqin Oasis

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