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

Yasin ul Haq (), Muhammad Shahbaz, H. M. Shahzad Asif, Ali Al-Laith and Wesam H. Alsabban
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Yasin ul Haq: Department of Computer Science, University of Engineering and Technology, Lahore 54000, Pakistan
Muhammad Shahbaz: Department of Computer Science, University of Engineering and Technology, Lahore 54000, Pakistan
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

Sustainability, 2023, vol. 15, issue 17, 1-19

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.

Keywords: DVI; machine learning; remote sensing; random forest; spatial mapping; soil salinity; salinity indices; vegetation indices (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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