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

作     者：QIAN Tana Atsushi TSUNEKAWA PENG Fei Tsugiyuki MASUNAGA WANG Tao LI Rui 

作者机构：Arid Land Research Center Tottori University Life and Environmental Science Shimane University Key Laboratory of Desert and Desertification Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences 

出 版 物：《Journal of Arid Land》 (干旱区科学（英文版）)

年 卷 期：2019年第11卷第1期

页      面：111-122页

核心收录：

学科分类：082802[工学-农业水土工程] 08[工学] 0828[工学-农业工程] 

基　　金：supported by the International Platform for Dryland Research and Education  Tottori University and the National Key R&D Program of China (2016YFC0500909) 

主　　题：salinity index soil salt content spectral reflectance waveband pairs arid regions 

摘      要：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