Soil Salinity Classification Using Machine Learning Algorithms and Radar Data in the Case from the South of Kazakhstan

Merembayev T. Amirgaliyev Y. Saurov S. Wójcik W.
2022 Polskie Towarzystwo Inzynierii Ekologicznej (PTIE)

Journal of Ecological Engineering
2022 #23 Issue 10 61 - 67 pp.

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.

Environmental correlation , Machine learning , Remote sensing , Soil salinity

Text of the article Перейти на текст статьи

Institute of Information and Computational Technologies CS MES RK, 28 Shevchenko Str., Almaty, 050010, Kazakhstan
S. Seifullin Kazakh Agro Technical University, 62 Zhenis Ave., Nur-Sultan, 010011, Kazakhstan
Lublin University of Technology, 38D Nadbystrzycka Str., Lublin, 20-618, Poland

Institute of Information and Computational Technologies CS MES RK
S. Seifullin Kazakh Agro Technical University
Lublin University of Technology

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026