Interpretable Machine Learning and Remote Sensing Data Reveal Soil Biogeochemistry Patterns in Agricultural Systems

Ruslan Suleymanov (), Marija Yurkevich, Olga Bakhmet, Tatiana Popova, Andrey Kungurtsev, Denis Zakirov, Anastasia Vittsenko, Gaurav Mishra and Azamat Suleymanov
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Ruslan Suleymanov: Laboratory of Soil Science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, 450054 Ufa, Russia
Marija Yurkevich: Laboratory for Plant Biotechnology, Department of Multidisciplinary Scientific Research of the Karelian Research Centre, Russian Academy of Sciences, 185910 Petrozavodsk, Russia
Olga Bakhmet: Laboratory for Ecological Monitoring and Modeling, Department of Multidisciplinary Scientific Research of the Karelian Research Centre, Russian Academy of Sciences, 185910 Petrozavodsk, Russia
Tatiana Popova: Laboratory for Plant Biotechnology, Department of Multidisciplinary Scientific Research of the Karelian Research Centre, Russian Academy of Sciences, 185910 Petrozavodsk, Russia
Andrey Kungurtsev: Department of Earth and Space Sciences, Ural Federal University Named After the First President of Russia B.N. Yeltsin, 620002 Yekaterinburg, Russia
Denis Zakirov: Department of Geodesy, Cartography and Geographic Information Systems, Ufa University of Science and Technology, 450076 Ufa, Russia
Anastasia Vittsenko: Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Gaurav Mishra: Centre of Excellence on Sustainable Land Management, Indian Council of Forestry Research and Education, Dehradun 248006, Uttarakhand, India
Azamat Suleymanov: Department of Geodesy, Cartography and Geographic Information Systems, Ufa University of Science and Technology, 450076 Ufa, Russia

Land, 2025, vol. 14, issue 9, 1-16

Abstract: Soil condition represents a critical factor for ensuring sustainable agricultural development and food security. In this study, we examined the content of key soil properties and their patterns using an interpretable machine learning framework in combination with remote sensing data (Sentinel-2A) across several land use types in northwestern Russia. The analyzed soil properties in 64 samples included soil organic carbon (Corg), total nitrogen (N), mobile phosphorus (Pmob), total phosphorus (Ptot), and mobile potassium (Kmob) sampled across three land use types: cropland, hayfield, and forest. For machine learning interpretability, model-agnostic methods were utilized, including permutation and SHapley Additive exPlanations (SHAP) with spatial visualization. Our results revealed the highest concentrations of Corg (6.1 ± 4.3%), Kmob (78.3 ± 42.1%), and N (31.2 ± 14.5 mg/100 g) in forested areas, while both types of phosphorus (Ptot and Pmob) peaked in croplands (0.075 ± 0.024 and 0.023 ± 0.015%, respectively). The lowest values of Corg were observed in hayfields, and the lowest values of Kmob and N in croplands. Model validation demonstrated that Corg and N were predicted most accurately (R 2 = 0.53 and 0.55, respectively), where SWIR bands from Sentinel-2A satellite imagery were key predictors. The generated soil property maps and spatial SHAP values clearly showed distinct patterns correlated with land use types due to distinct biogeochemical processes across landscapes. Our findings demonstrate how land management practices fundamentally alter soil parameters, creating diagnostic spectral signatures that can be captured through interpretable machine learning and remote sensing.

Keywords: land use types; soil properties; Sentinel; digital soil mapping; machine learning; Shapley values; SHAP (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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