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Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan

Author

Listed:
  • Yasin ul Haq

    (Department of Computer Science, University of Engineering and Technology, Lahore 54000, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Shahbaz

    (Department of Computer Science, University of Engineering and Technology, Lahore 54000, Pakistan
    These authors contributed equally to this work.)

  • H. M. Shahzad Asif

    (Department of Computer Science, University of Engineering and Technology, Lahore 54000, Pakistan)

  • Ali Al-Laith

    (Computer Science Department, Copenhagen University, 2100 Copenhagen, Denmark)

  • Wesam H. Alsabban

    (Information Systems Department, Faculty of Computer and Information Systems, Umm Al-Qura University, Makkah 24231, Saudi Arabia)

Abstract

The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R 2 ), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R 2 of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R 2 of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation.

Suggested Citation

  • Yasin ul Haq & Muhammad Shahbaz & H. M. Shahzad Asif & Ali Al-Laith & Wesam H. Alsabban, 2023. "Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan," Sustainability, MDPI, vol. 15(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12943-:d:1226815
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    References listed on IDEAS

    as
    1. 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.
    2. Yao, Rongjiang & Yang, Jingsong, 2010. "Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method," Agricultural Water Management, Elsevier, vol. 97(12), pages 1961-1970, November.
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