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Soil Salinity Classification Using Machine Learning Algorithms and Radar Data in the Case from the South of Kazakhstan
 
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1
Institute of Information and Computational Technologies CS MES RK, 050010, 28 Shevchenko str., Almaty, Kazakhstan
2
S. Seifullin Kazakh Agro Technical University, 010011, 62 Zhenis ave., Nur-Sultan, Kazakhstan
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Lublin University of Technology, 20-618, ul. Nadbystrzycka 38D, Lublin, Poland
CORRESPONDING AUTHOR
Timur Merembayev   

Institute of Information and Computational Technologies CS MES RK, 050010, 28 Shevchenko str., Almaty, Kazakhstan
 
 
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ABSTRACT
Soil salinity is one of the major impact factors on agriculture in the South of Kazakhstan. Prediction and estimation of soil salinity before planting a season usually helps to plan for the leaching of the salt. In the paper, satellite data, such as radar data and machine learning algorithms, were used to classify soil salinity. Numerical results were presented for the Turkestan region, which contains more than 102 points. The machine learning algorithms, including Gaussian Process, Decision Tree, and Random Forest, were compared. The evaluation of the model score was realized by using metrics such as accuracy, Recall, and f1. In addition, the influence of the dataset features on the classification was investigated using machine learning algorithms. The research results showed that the Gaussian Process model has the best score among considered algorithms. In addition, the results are consistent with the outcome of the Shapley Additive exPlanations (SHAP) framework.