7 citations as recorded by crossref.

1. Uncertainty quantification for digital soil mapping: An overview A. ZHU et al. https://doi.org/10.1016/j.pedsph.2025.12.011
2. A seventeen-year soil analysis dataset for ecosystem management M. Collinet et al. https://doi.org/10.1038/s41597-026-07254-8
3. Evaluating the Impact of Multi-Source Digital Elevation Model Quality on Archeological Predictive Modeling: An Integrated Framework Based on Machine Learning and SHAP-Based Interpretability Analysis J. Yang et al. https://doi.org/10.3390/rs18060961
4. Mapping and modelling land degradation vulnerability in a semi-arid region: a case study from Battalgazi District, Turkiye M. Kılıç https://doi.org/10.7717/peerj.20606
5. Importance ranking of data and model uncertainties in quantile regression forest-based spatial predictions when data are sparse, imprecise and clustered J. Rohmer https://doi.org/10.1016/j.ecoinf.2025.103459
6. Explainable machine learning models for predicting topsoil metals and oxides A. Suleymanov et al. https://doi.org/10.1007/s11368-025-04143-2
7. Evaluation of spatial effects on hydrogeochemical interactions in nitrate pollution using machine learning and explainable artificial intelligence J. Mogaraju https://doi.org/10.1007/s44288-026-00404-w