215 citations as recorded by crossref.

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20. Spatial prediction of soil contamination based on machine learning: a review Y. Zhang et al. 10.1007/s11783-023-1693-1
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22. Free iron oxide content in tropical soils predicted by integrative digital mapping W. Mendes et al. 10.1016/j.still.2022.105346
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24. Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method F. Meng et al. 10.1016/j.jenvman.2023.118817
25. Machine Learning With GA Optimization to Model the Agricultural Soil-Landscape of Germany: An Approach Involving Soil Functional Types With Their Multivariate Parameter Distributions Along the Depth Profile M. Ließ et al. 10.3389/fenvs.2021.692959
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27. Carbon and Phosphorus in Volcanic Soils of Mt. Kerinci (Sumatra) After Long Term Tea and Paddy Cultivation D. Fiantis et al. 10.1080/00103624.2023.2253843
28. Quantification of non-structural carbohydrates in different tissues of trees with diffuse reflectance spectroscopy: Is there a benefit from a fusion of near- and mid-infrared data? M. Vohland et al. 10.1016/j.talanta.2023.125406
29. A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives Y. Fan et al. 10.1021/acs.est.2c03562
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32. Using Machine Learning to make nanomaterials sustainable J. Scott-Fordsmand & M. Amorim 10.1016/j.scitotenv.2022.160303
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49. Österreichweite Regionalisierung bodenhydraulischer Eigenschaften H. Zeitfogel et al. 10.1007/s00506-022-00842-z
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54. The possibility of spatial mapping of soil organic carbon content at three depths using easy-to-obtain ancillary data in a Mediterranean area F. Blanco Velázquez et al. 10.12688/openreseurope.14716.1
55. Spatial variability of soil Cd content based on IDW and RBF in Fujiang River, Mianyang, China J. Sheng et al. 10.1007/s11368-020-02758-1
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63. Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining D. Li et al. 10.1007/s10653-024-01928-1
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65. A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity A. Ghaderi et al. 10.1016/j.catena.2022.106289
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