28 citations as recorded by crossref.

1. Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone S. Azamat et al. 10.1016/j.geodrs.2024.e00855
2. Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning Y. Dong et al. 10.1016/j.geoderma.2025.117225
3. Understanding the risks of peri-urbanization to food systems to help establish sustainable agriculture near cities X. Wang et al. 10.1016/j.eiar.2024.107777
4. Biotic Predictors of Carbon Stock Variation in Light-Textured Forest Soils A. Kuznetsova et al. 10.1134/S1062359024613211
5. Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach J. Rohmer et al. 10.5194/soil-10-679-2024
6. Mapping of cropland humus content of the Bryansk region using machine learning methods L. Konoplina et al. 10.55959/MSU0137-0944-17-2024-79-4-130-140
7. Mapping soil organic carbon sequestration potential in croplands using a combined proximal and remote sensing approach L. Qi et al. 10.1016/j.still.2025.106733
8. Soil organic carbon retrieval using a machine learning approach from satellite and environmental covariates in the Lower Brazos River Watershed, Texas, USA B. Tikuye & R. Ray 10.1016/j.acags.2025.100252
9. Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
10. Unraveling drivers of maize (Zea mays L.) yield variability in Ghana: A machine learning approach A. Kouame et al. 10.1016/j.compag.2025.110647
11. A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling W. Cui et al. 10.1016/j.jag.2025.104542
12. Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica R. Siqueira et al. 10.1016/j.catena.2023.107677
13. Digitally mapping soil health at regional scale: disentangling drivers and predicting spatial land use effects V. Rubio et al. 10.1016/j.geoderma.2025.117401
14. Mapping of Cropland Humus Content of Bryansk Oblast Using Machine-Learning Methods L. Konoplina et al. 10.3103/S0147687424700479
15. Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland J. Skála et al. 10.1016/j.jenvman.2025.125035
16. Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits J. Rukaitė et al. 10.1016/j.jafr.2024.101434
17. Biplots for understanding machine learning predictions in digital soil mapping S. van der Westhuizen et al. 10.1016/j.ecoinf.2024.102892
18. Artificial intelligence in soil science A. Wadoux 10.1111/ejss.70080
19. Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al. 10.1016/j.catena.2024.108635
20. Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) H. Li et al. 10.1016/j.scitotenv.2024.170330
21. Assessment of the spatial and temporal heterogeneity of available phosphorus content in arable soil on the scale of one field (using the example of the Republic of Tatarstan, Russia) I. Sakhabiev et al. 10.1051/e3sconf/202562302014
22. Exploring the monthly contribution of drivers on European summer wildfires with explainable artificial intelligence (XAI) H. Li et al. 10.1016/j.ecolind.2025.113605
23. Impact of Updating Vegetation Information on Land Surface Model Performance M. Ruiz‐Vásquez et al. 10.1029/2023JD039076
24. Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands A. Helfenstein et al. 10.1038/s43247-024-01293-y
25. Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al. 10.3390/su17073173
26. Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin 10.1016/j.ejrs.2023.07.007
27. Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach A. Suleymanov et al. 10.1134/S1064229324602014
28. Enhanced soil organic carbon mapping in Gannan’s alpine meadows: A comparative analysis of machine learning models and satellite data X. Liu et al. 10.1016/j.ecolind.2025.113800