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| Derivation of salt content in salinized soil from hyperspectral reflectance data: A case study at Minqin Oasis, Northwest China |
Tana QIAN1,*( ), TSUNEKAWA Atsushi1, Fei PENG1, TsugiyukiMASUNAGA2, Tao WANG3, Rui LI4 |
1 Arid Land Research Center, Tottori University, Tottori 680-0001, Japan
2 Life and Environmental Science, Shimane University, Matsue 690-8504, Japan
3 Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
4 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
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Abstract Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-aridregions. 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 relationshipsbetween 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 themodel. 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.
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Received: 29 December 2017
Published: 10 February 2019
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