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ZHANG Chong，SUN Zhijian，PENG Borui，et al. Remote sensing inversion model and spatial distribution characteristics of soil salinity in the Kongque River irrigation area[J]. Bulletin of Geological Science and Technology，2026，45（4）：1-10 doi: 10.19509/j.cnki.dzkq.tb20250185
Citation: ZHANG Chong，SUN Zhijian，PENG Borui，et al. Remote sensing inversion model and spatial distribution characteristics of soil salinity in the Kongque River irrigation area[J]. Bulletin of Geological Science and Technology，2026，45（4）：1-10 doi: 10.19509/j.cnki.dzkq.tb20250185
shu

Remote sensing inversion model and spatial distribution characteristics of soil salinity in the Kongque River irrigation area

doi: 10.19509/j.cnki.dzkq.tb20250185
  • 1.

    School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan 430078, China

  • 2.

    The Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China

More Information
  • Author Bio:

    E-mail：20181004366@cug.edu.cn

  • Corresponding author: E-mail：liuyf@cug.edu.cn
  • Received Date: 23 Apr 2025
  • Accepted Date: 30 Jun 2025
  • Rev Recd Date: 26 Jun 2025
    • Available Online: 30 Jun 2025
    Abstract
  • Objective 

    Soil salinization is a prominent environmental issue in arid regions, and rapid and accurate monitoring of soil salinity is critical for regional ecological conservation and sustainable agricultural development.

    Methods 

    To improve the accuracy of remote sensing inversion of soil salinity in arid regions, the Kongque River irrigation area of the Xinjiang Uygur Autonomous Region was selected as the study area. Soil samples were collected from field survey points. Based on measured soil hyperspectral data acquired with the ASD FieldSpec 4 spectroradiometer, Landsat 8 satellite remote sensing data were calibrated, and the calibrated spectral indices were then used to construct a soil salinity remote sensing inversion model with the random forest algorithm to estimate surface soil salinity in the Kongque River irrigation area.

    Results 

    The results showed that soil spectral reflectance increased with increasing salinization degree. After ASD hyperspectral calibration, the correlations between some salinity indices and soil salinity were significantly improved. The random forest was used to construct a remote sensing inversion model for soil salinity. The R2 was 0.847 for the training set and 0.713 for the validation set, both significantly higher than the R2 values of the training and validation sets obtained from the original data. Soil salinization was most severe in the western part of the Kongque River irrigation area and gradually decreased from Southwest to Northeast. Slightly and moderately saline soils were mainly distributed in the southern part, while non-saline soils were mainly distributed in the central and northern parts.

    Conclusion 

    The hyperspectrally calibrated random forest inversion model shows good accuracy and can provide reliable technical support for dynamic monitoring of soil salinization in the Kongque River irrigation area and similar arid regions.

     

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