Recognition of Soil Profile Stratification Using Machine Learning and Remote Sensing Methods

Aitimov M. Muratkhan R. Mukhametzhanova B. Yussupova G. Bayegizova A. Akhmetzhanov M. Beldeubayeva Z. Uzakkyzy N.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2025 #13 193667 - 193682 pp.

This study presents a novel machine learning–based framework for recognizing soil profile stratification and estimating key soil properties in arid and semi-arid regions of Kazakhstan. A multi-task stacking ensemble integrating Random Forest, Gradient Boosting, and XGBoost models was developed to jointly predict quantitative indicators (clay and sand content, bulk density) and categorical variables (textural classes and soil horizon classes) down to 200 cm depth. The predictor space combines Sentinel-2 multispectral reflectance, ERA5-Land climate parameters, Digital Elevation Model (DEM)-derived topographic attributes, and static soil–environmental covariates. Independent ground-truth validation across the Aral, Bozaigyr, and Atyrau regions demonstrates high predictive reliability (R² = 0.86–0.97 for quantitative properties and 94–98 % accuracy for classification tasks). Shapley Additive exPlanations (SHAP) analysis revealed that topography, Short-Wave InfraRed (SWIR) bands, and hydrological gradients are the dominant predictors governing vertical soil differentiation. The integration of topographic and spectral features within the SCORPAN framework ensures both methodological transparency and pedological interpretability. The proposed approach advances digital soil mapping by providing a scalable and transferable solution for stratified soil profile recognition in data-sparse environments.

Machine learning , multi-task stacking , remote sensing , soil profile recognition , soil stratification

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Korkyt Ata Kyzylorda University, Kyzylorda, 120014, Kazakhstan
Karaganda Buketov University, Department of Applied Mathematics and Informatics, Karaganda, 100024, Kazakhstan
L.N. Gumilyov Eurasian National University, Faculty of Information Technology, Department of Information Security, Astana, 010008, Kazakhstan
ALT University, Department of Radio Engineering and Telecommunications, Almaty, 050013, Kazakhstan
L.N. Gumilyov Eurasian National University, Department of Radio Engineering, Electronics and Telecommunications, Astana, 010008, Kazakhstan
M.Kh. Dulaty Taraz Regional University, Taraz, 080012, Kazakhstan
S. Seifullin Kazakh AgroTechnical Research University, Department of Information Systems, Astana, 010011, Kazakhstan
L.N. Gumilyov Eurasian National University, Faculty of Information Technology, Department of Computer and Software Engineering, Astana, 010008, Kazakhstan

Korkyt Ata Kyzylorda University
Karaganda Buketov University
L.N. Gumilyov Eurasian National University
ALT University
L.N. Gumilyov Eurasian National University
M.Kh. Dulaty Taraz Regional University
S. Seifullin Kazakh AgroTechnical Research University
L.N. Gumilyov Eurasian National University

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