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Objective <p>The study aimed to evaluate the potential of multispectral satellite images in soil salinity assessment using linear multiple regression and the M5 decision tree regression method. Therefore, 96 soil samples were collected and correlated with 15 independent spectral information and Landsat 8 satellite image indices.</p> Results <p>Due to the nonlinear relationship between EC and spectral bands, linear regression results were unsatisfactory, with the highest correlation coefficient of 58% and an RMSE of 0.78. The M5 decision tree regression model provided better results, with a correlation coefficient of 73% and an RMSE of 0.29 after establishing 9 regression relationships, successfully estimating the natural logarithm of EC. The B64, NDII, and S2 indices are the most influential in remotely sensed soil salinity estimation. Furthermore, the M5 model, utilizing six regression equations, demonstrates a 37.18% improvement in accuracy compared to a multivariate linear regression approach. Factors such as vegetation cover, soil moisture, and uneven moisture content of samples during collection contributed to errors in assessing soil salinity using satellite images.</p>

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Regional soil salinity analysis using stepwise M5 decision tree

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

查看全文
  • Khalil Ghorbani,
  • Soraya Bandak,
  • Laleh Rezaei Ghaleh,
  • Saeed Mehri,
  • Aynaz Lotfata

摘要

Objective

The study aimed to evaluate the potential of multispectral satellite images in soil salinity assessment using linear multiple regression and the M5 decision tree regression method. Therefore, 96 soil samples were collected and correlated with 15 independent spectral information and Landsat 8 satellite image indices.

Results

Due to the nonlinear relationship between EC and spectral bands, linear regression results were unsatisfactory, with the highest correlation coefficient of 58% and an RMSE of 0.78. The M5 decision tree regression model provided better results, with a correlation coefficient of 73% and an RMSE of 0.29 after establishing 9 regression relationships, successfully estimating the natural logarithm of EC. The B64, NDII, and S2 indices are the most influential in remotely sensed soil salinity estimation. Furthermore, the M5 model, utilizing six regression equations, demonstrates a 37.18% improvement in accuracy compared to a multivariate linear regression approach. Factors such as vegetation cover, soil moisture, and uneven moisture content of samples during collection contributed to errors in assessing soil salinity using satellite images.