57 citations as recorded by crossref.

1. Spatially heterogeneous controls of soil organic carbon in a karst mountainous area of southern China: Insights from interpretable machine learning H. Xu et al.
2. Biotic Predictors of Carbon Stock Variation in Light-Textured Forest Soils A. Kuznetsova et al.
3. Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach J. Rohmer et al.
4. Mapping soil organic carbon sequestration potential in croplands using a combined proximal and remote sensing approach L. Qi et al.
5. Explainable machine learning models for predicting topsoil metals and oxides A. Suleymanov et al.
6. 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.
7. Environmental variables controlling soil thickness across elevation zones in the eastern Himalayas X. Zhang et al.
8. Digitally mapping soil health at regional scale: disentangling drivers and predicting spatial land use effects V. Rubio et al.
9. Multispectral bare soil composites as a resource for SOC mapping rather than SOC monitoring: A case study in the Walloon region (Belgium) D. De Bièvre et al.
10. Mapping of Cropland Humus Content of Bryansk Oblast Using Machine-Learning Methods L. Konoplina et al.
11. Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning X. Tian et al.
12. Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland J. Skála et al.
13. First insights into soil fauna mapping across Europe using data from multiple data sources for three different taxa F. Marchal et al.
14. Biplots for understanding machine learning predictions in digital soil mapping S. van der Westhuizen et al.
15. A species distribution modelling analysis of Rafflesia pricei (Rafflesiaceae), a parasitic flowering plant endemic to Borneo V. Tytar et al.
16. Artificial intelligence in soil science A. Wadoux
17. Mechanisms and drivers of soil pH assessed by Shapley additive explanation A. Suleymanov et al.
18. Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al.
19. High-Resolution Mapping of Soil Organic Carbon Stocks Using Machine and Deep Learning Approaches Across Mediterranean Land Uses M. Oukhattar et al.
20. High-confidence soil color prediction at multiple depths across Shaanxi Province, China Y. Yang et al.
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.
22. Cover crops drive soil organic carbon sequestration in newly reclaimed saline-alkali land: Altitude and species specificity of improvement effects F. Yang et al.
23. Exploring the monthly contribution of drivers on European summer wildfires with explainable artificial intelligence (XAI) H. Li et al.
24. Machine learning-based analysis of distribution characteristics and sensitivity of soil organic carbon in typical soils of China W. Li et al.
25. Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands A. Helfenstein et al.
26. Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al.
27. Quantitative Analysis of Wind Erosion Drivers Using Explainable Artificial Intelligence: A Case Study from Inner Mongolia, China Y. Mei et al.
28. A Julia toolkit for species distribution data T. Poisot et al.
29. Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin
30. Predicting Biochar‐Induced Changes in Soil Organic Carbon With Ensemble Machine Learning A. Ray et al.
31. Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model B. Hu et al.
32. Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone S. Azamat et al.
33. Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning Y. Dong et al.
34. Understanding the risks of peri-urbanization to food systems to help establish sustainable agriculture near cities X. Wang et al.
35. Explainable reinforcement learning for glucose monitoring based on shapley value analysis A. Adjevi et al.
36. A shapley additive exPlanations-informed, threshold-based environmental variable optimization strategy for enhancing soil organic carbon content Z. Wu et al.
37. Mapping of cropland humus content of the Bryansk region using machine learning methods L. Konoplina et al.
38. Mining global soil carbon datasets: can modern machine learning uncover the missing pieces of process-based models? S. Hashimoto et al.
39. 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
40. Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al.
41. Warming could cause significant soil organic carbon loss around the southern Baltic Sea Y. Li et al.
42. Unraveling drivers of maize (Zea mays L.) yield variability in Ghana: A machine learning approach A. Kouame et al.
43. 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.
44. Shapley values reveal geomorphic controls on exposed bedrock-gravel differentiation X. Zhang et al.
45. Influences of outliers on performance of geographically weighted random forest for modelling cadmium concentrations in topsoil of the northern part of Ireland Y. Li et al.
46. Interpretation of a Complex Model of Soil-Landscape Relationships: Thematic Modeling of Organic Carbon Content in Arable Soils of the Bugul’minsko-Belebeevskaya Upland A. Chinilin et al.
47. Hydrothermal stress influences the spatial distribution pattern of soil organic carbon and its stock in Xinjiang Y. Li et al.
48. The importance of zeros in digital soil mapping II: A case study of depth-to-bedrock mapping in New Brunswick, Canada T. Pennell et al.
49. Global insights into nitrogen losses and efficiency in rice, wheat, and maize cultivation D. Chakraborty et al.
50. 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.
51. Targeting degraded soils for restoration: mapping surface hardpan soils in Niger with satellite imagery A. Hunakunti et al.
52. Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) H. Li et al.
53. Multi-scenario modeling of soil organic carbon in semi-arid croplands with uncertainty quantification and model interpretation A. Suleymanov et al.
54. Impact of Updating Vegetation Information on Land Surface Model Performance M. Ruiz‐Vásquez et al.
55. Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach A. Suleymanov et al.
56. Enhanced soil organic carbon mapping in Gannan’s alpine meadows: A comparative analysis of machine learning models and satellite data X. Liu et al.
57. Divergent responses of soil organic carbon stocks in different layers to global changes on the Tibetan Plateau X. Zhang et al.