22 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. Artificial intelligence in soil science A. Wadoux 10.1111/ejss.70080
6. 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
7. 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
8. 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
9. Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) H. Li et al. 10.1016/j.scitotenv.2024.170330
10. Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
11. 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
12. Impact of Updating Vegetation Information on Land Surface Model Performance M. Ruiz‐Vásquez et al. 10.1029/2023JD039076
13. 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
14. 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
15. 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
16. Mapping of Cropland Humus Content of Bryansk Oblast Using Machine-Learning Methods L. Konoplina et al. 10.3103/S0147687424700479
17. Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al. 10.3390/su17073173
18. 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
19. Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach A. Suleymanov et al. 10.1134/S1064229324602014
20. 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
21. 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
22. Biplots for understanding machine learning predictions in digital soil mapping S. van der Westhuizen et al. 10.1016/j.ecoinf.2024.102892