Application of machine learning and remote sensing in soil science: current state and practical opportunities

Sergey Ogorodnikov; Victor Blynsky

doi:10.55186/25876740_2026_69_6_847

VAK Russia 4.1.3 UDC 631.4 UDC 004.8 UDC 528.8

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APPLICATION OF MACHINE LEARNING AND REMOTE SENSING IN SOIL SCIENCE: CURRENT STATE AND PRACTICAL OPPORTUNITIES Application of machine learning and remote sensing in soil science: current state and practical opportunities

АА Аа

Published in INTERNATIONAL AGRICULTURAL JOURNAL · Issue 6, 2026 · Pages 847–851 · Rubric: Scientific support and management of agrarian and industrial complex

DOI: https://doi.org/10.55186/25876740_2026_69_6_847

Received: 13.07.2026 Accepted: 18.08.2026 Published: 14.10.2026

Authors

Sergey Ogorodnikov

Victor Blynsky

Sergey Ogorodnikov

Academic degree: candidate of sciences in biology

Science rang: docent

Lomonosov Moscow State University; Moscow Aviation Institute Russian Federation · Moscow

Victor Blynsky

Lomonosov Moscow State University postgraduate student · Russian Federation · Moscow

ABSTRACT

The article examines current approaches to the assessment and mapping of soil properties based on the integration of remote sensing data, ground observations, laboratory measurements, and machine learning methods. The aim of the study is to systematize data sources, preprocessing stages, and analytical algorithms used to improve the accuracy of soil property prediction under conditions of limited field measurements. The methodological basis of the study includes a review and comparative analysis of recent scientific publications, a synthesis of information on multispectral, hyperspectral, radar, and unmanned aerial vehicle data, and an assessment of the practical use of random forest, support vector machines, gradient boosting, neural networks, and probabilistic models. It is shown that the highest stability of soil models is achieved through the combined use of heterogeneous data sources, correct identification of bare soil areas, calculation of spectral, terrain, and climatic predictors, and spatially justified model validation. Ensemble and nonlinear algorithms are effective for digital soil mapping, deep learning methods are promising for processing spatial and temporal data structures, and probabilistic approaches additionally allow prediction uncertainty to be taken into account. It is substantiated that the application of machine learning and remote sensing in soil science enables a transition from point-based measurements to spatially continuous assessments of soil properties and provides an information basis for precision agriculture, agrochemical surveys, and sustainable land resource management.

KEYWORDS

soil science, digital soil mapping, soil properties, precision agriculture

Funding

This study was supported by the Russian Science Foundation, grant No. 23-74-01050.

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1. Padarian J., Minasny B., McBratney A.B. Machine learning and soil sciences: a review aided by machine learning tools // SOIL. 2020. Vol. 6. P. 35-52. DOI:https://doi.org/10.5194/soil-6-35-2020

2. Lamichhane M., Mehan S., Mankin K.R. Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities // Remote Sensing. 2025. Vol. 17. No. 14. Article 2397. DOI:https://doi.org/10.3390/rs17142397

3. Shin S.K., Lee S.J., Park J.H. Prediction of Soil Properties Using Vis-NIR Spectroscopy Combined with Machine Learning: A Review // Sensors. 2025. Vol. 25. No. 16. Article 5045. DOI:https://doi.org/10.3390/s25165045

4. Radočaj D., Jug D., Jug I., Jurišić M. A Comprehensive Evaluation of Machine Learning Algorithms for Digital Soil Organic Carbon Mapping on a National Scale // Applied Sciences. 2024. Vol. 14. No. 21. Article 9990. DOI:https://doi.org/10.3390/app14219990

5. Padarian J., McBratney A.B., Minasny B. Game theory interpretation of digital soil mapping convolutional neural networks // SOIL. 2018. Vol. 4. P. 389-397. DOI:https://doi.org/10.5194/soil-4-389-2018

6. Azad N., Sheikhbaglou A., Zvomuya F., He H., Moulin A.P., Zeleke T.B., Gameda S. Applying LSTM to Model Multi-Depth Soil Moisture Under Various Land Covers, Climates and Soils // European Journal of Soil Science. 2025. DOI:https://doi.org/10.1111/ejss.70142

7. Wang J., et al. Gaussian process regression for three-dimensional soil mapping over multiple spatial supports // Geoderma. 2024. Vol. 446. Article 116899.

8. Hassall K.L., Zawadzka J., Milne A.E., Corstanje R., et al. Putting numbers to a metaphor: A Bayesian Belief Network with which to infer Soil Quality and Health // European Journal of Agronomy. 2025. Vol. 168. Article 127537. DOI:https://doi.org/10.1016/j.eja.2025.127537

9. Venkateswarlu M., Rallapalli S., Singh A., Chalapathi G.S.S., Kumar S., Katpatal Y.B., Sujatha G. Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques // Scientific Reports. 2025. Vol. 15. Article 24689. DOI:https://doi.org/10.1038/s41598-025-12184-3

10. Alonso-Sarria F., Blanco-Bernardeau A., Romero-Diaz A. Estimation of soil properties using machine learning techniques to improve hydrological modeling in a semiarid environment: Campo de Cartagena (Spain) // Earth Science Informatics. 2025. DOI:https://doi.org/10.1007/s12145-025-01833-w

11. Bohn M.P., Miller B.A. Digital soil mapping via machine learning of agronomic soil survey properties from 0-200 cm in depth in Illinois // Agronomy Journal. 2025. Vol. 117. No. 5. Article e70144. DOI:https://doi.org/10.1002/agj2.70144

12. Xue J., et al. A two-dimensional bare soil separation framework using multi-temporal Sentinel-2 images across China // International Journal of Applied Earth Observation and Geoinformation. 2024. Vol. 134. Article 104181

13. Xu Z., Hou D., Lin N., et al. Satellite remote sensing enables monitoring of soil organic carbon decline in croplands of Jilin, China // Scientific Reports. 2026. Vol. 16. Article 6966. DOI:https://doi.org/10.1038/s41598-026-38386-x

14. Thompson J.A., Bell J.C., Butler C.A. Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling // Geoderma. 2001. Vol. 100. No. 1-2. P. 67-89. DOI:https://doi.org/10.1016/S0016-7061(00)00081-1

15. Savitzky A., Golay M.J.E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures // Analytical Chemistry. 1964. Vol. 36. No. 8. P. 1627-1639. DOI:https://doi.org/10.1021/ac60214a047

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Editor-in-Chief

Aleksandr Fomin

Deputy Editor-in-Chief

Irina Mamontova

Managing Editor

Tat'yana V. Kazennova

Issue Editor

Gilyana Yakushkina

Literary Editor

Marina G. Fomina

Editorial

Redakcii Sotrudnik

info@mshj.ru

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GOST

Ogorodnikov S. S., Blynsky V. A. Application of machine learning and remote sensing in soil science: current state and practical opportunities // INTERNATIONAL AGRICULTURAL JOURNAL. 2026. no. 6. pp. 847-851. DOI: https://doi.org/10.55186/25876740_2026_69_6_847. (Date of access 31.10.2026).

MLA

Sergey Ogorodnikov, Blynsky V. A.. "Application of machine learning and remote sensing in soil science: current state and practical opportunities." INTERNATIONAL AGRICULTURAL JOURNAL.6 (2026): 847-851. DOI: https://doi.org/10.55186/25876740_2026_69_6_847.

APA

Ogorodnikov S. S., Blynsky V. A. (2026). Application of machine learning and remote sensing in soil science: current state and practical opportunities. INTERNATIONAL AGRICULTURAL JOURNAL(6), 847-851. https://doi.org/10.55186/25876740_2026_69_6_847

Chicago

Sergey Ogorodnikov, Blynsky V. A.. "Application of machine learning and remote sensing in soil science: current state and practical opportunities." INTERNATIONAL AGRICULTURAL JOURNAL, no. 6 (2026): 847-851. https://doi.org/10.55186/25876740_2026_69_6_847.

WIKI

{{cite journal | author = Ogorodnikov S. S., Blynsky V. A. | title = Application of machine learning and remote sensing in soil science: current state and practical opportunities | journal = INTERNATIONAL AGRICULTURAL JOURNAL | year = 2026 | issue = 6 | pages = 847-851 | doi = 10.55186/25876740_2026_69_6_847 }}

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APPLICATION OF MACHINE LEARNING AND REMOTE SENSING IN SOIL SCIENCE: CURRENT STATE AND PRACTICAL OPPORTUNITIES Application of machine learning and remote sensing in soil science: current state and practical opportunities

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