Journal of Arid Land

, Volume 11, Issue 1, pp 111–122 | Cite as

Derivation of salt content in salinized soil from hyperspectral reflectance data: A case study at Minqin Oasis, Northwest China

  • Tana QianEmail author
  • Atsushi Tsunekawa
  • Fei Peng
  • Tsugiyuki Masunaga
  • Tao Wang
  • Rui Li


Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-arid regions. It is crucial and urgent that advanced technologies are used to efficiently and accurately assess the status of salinization processes. Case studies to determine the relations between particular types of salinization and their spectral reflectances are essential because of the distinctive characteristics of the reflectance spectra of particular salts. During April 2015 we collected surface soil samples (0–10 cm depth) at 64 field sites in the downstream area of Minqin Oasis in Northwest China, an area that is undergoing serious salinization. We developed a linear model for determination of salt content in soil from hyperspectral data as follows. First, we undertook chemical analysis of the soil samples to determine their soluble salt contents. We then measured the reflectance spectra of the soil samples, which we post-processed using a continuum-removed reflectance algorithm to enhance the absorption features and better discriminate subtle differences in spectral features. We applied a normalized difference salinity index to the continuum-removed hyperspectral data to obtain all possible waveband pairs. Correlation of the indices obtained for all of the waveband pairs with the wavebands corresponding to measured soil salinities showed that two wavebands centred at wavelengths of 1358 and 2382 nm had the highest sensitivity to salinity. We then applied the linear regression modelling to the data from half of the soil samples to develop a soil salinity index for the relationships between wavebands and laboratory measured soluble salt content. We used the hyperspectral data from the remaining samples to validate the model. The salt content in soil from Minqin Oasis were well produced by the model. Our results indicate that wavelengths at 1358 and 2382 nm are the optimal wavebands for monitoring the concentrations of chlorine and sulphate compounds, the predominant salts at Minqin Oasis. Our modelling provides a reference for future case studies on the use of hyperspectral data for predictive quantitative estimation of salt content in soils in arid regions. Further research is warranted on the application of this method to remotely sensed hyperspectral data to investigate its potential use for large-scale mapping of the extent and severity of soil salinity.


salinity index soil salt content spectral reflectance waveband pairs arid regions 


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This research was supported by the International Platform for Dryland Research and Education, Tottori University and the National Key R&D Program of China (2016YFC0500909). The authors would like to thank Professor WEI Huaidong, Professor DING Feng and Professor ZHOU Liping from Gansu Desertification and Aeolian Sand Disaster Combating Institute for providing the equipment and assistance in the measurement; Professor XUE Xian, Professor WANG Ninglian, Dr. HUANG Cuihua, Dr. LIAO Jie, Dr. LUO Jun and Dr. DONG Siyang from Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, for providing the equipment and the assistance in the field work; and Professor Kitamura YOSHINOBU and Fujimaki HARUYUKI from Tottori University for their helpful advices and providing the equipment for experiments.


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Copyright information

© China Science Publishing Media Ltd. (Science Press) and Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tana Qian
    • 1
    Email author
  • Atsushi Tsunekawa
    • 1
  • Fei Peng
    • 1
  • Tsugiyuki Masunaga
    • 2
  • Tao Wang
    • 3
  • Rui Li
    • 4
  1. 1.Arid Land Research CenterTottori UniversityTottoriJapan
  2. 2.Life and Environmental ScienceShimane UniversityMatsueJapan
  3. 3.Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina
  4. 4.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina

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