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Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China

Author

Listed:
  • Sheshu Zhang

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Jun Zhao

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Jianxia Yang

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Jinfeng Xie

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

  • Ziyun Sun

    (College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China)

Abstract

(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R 2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions.

Suggested Citation

  • Sheshu Zhang & Jun Zhao & Jianxia Yang & Jinfeng Xie & Ziyun Sun, 2024. "Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China," Land, MDPI, vol. 13(6), pages 1-21, June.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:877-:d:1416867
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    References listed on IDEAS

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    1. Yue Zhao & Zhuopeng Zhang & Honglei Zhu & Jianhua Ren, 2022. "Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
    2. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
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