52 citations as recorded by crossref.

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4. Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area M. Sakizadeh et al. 10.1007/s10653-023-01845-9
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7. Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China X. Zhang et al. 10.3390/rs15205033
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10. Inequality in heat: The role of spatial patterns of urban green infrastructure P. Chen 10.1016/j.uclim.2024.101820
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16. Mapping the phosphorus sorption capacity of Danish soils in four depths with quantile regression forests and uncertainty propagation A. Møller et al. 10.1016/j.geoderma.2022.116316
17. Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al. 10.1016/j.geodrs.2021.e00437
18. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al. 10.3390/agronomy12112613
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20. Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region V. Dura et al. 10.1002/joc.8662
21. Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region) A. Zeyliger et al. 10.3390/s22166153
22. On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization I. Dunkl & M. Ließ 10.5194/soil-8-541-2022
23. Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al. 10.3390/rs15040876
24. Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics G. Szatmári et al. 10.1016/j.geoderma.2021.115356
25. High-performance soil class delineation via UMAP coupled with machine learning in Kurdistan Province, Iran R. Taghizadeh-Mehrjardi et al. 10.1016/j.geodrs.2024.e00754
26. High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management M. van Doorn et al. 10.1016/j.geoderma.2024.116838
27. High-Resolution Soil Property Maps from Digital Soil Mapping Methods, Czech Republic D. Žížala et al. 10.2139/ssrn.3928321
28. Incorporating Spatial Information for Regionalization of Environmental Parameters in Machine Learning Models M. Ohmer et al. 10.1007/s11004-024-10163-4
29. Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. 10.1016/j.geoderma.2023.116579
30. Can We Use Machine Learning for Agricultural Land Suitability Assessment? A. Møller et al. 10.3390/agronomy11040703
31. 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
32. Soil textural class modeling using digital soil mapping approaches: Effect of resampling strategies on imbalanced dataset predictions F. Mirzaei et al. 10.1016/j.geodrs.2024.e00821
33. Major overlap in plant and soil organic carbon hotspots across Africa N. Kebonye et al. 10.1016/j.scitotenv.2024.175476
34. Mapping benthic sediment types and composition in a turbid Jamaican bay using hydroacoustic data and different spatially explicit interpolation techniques K. McLaren 10.1016/j.rsma.2024.103655
35. Spatial Downscaling of Global Categorical Soil Information Into Data Suitable for Land-Use Management T. FLYNN 10.2139/ssrn.4496677
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37. Colombian soil texture: building a spatial ensemble model V. Varón-Ramírez et al. 10.5194/essd-14-4719-2022
38. Spatial downscaling of global soil texture classes into 30 m images at the province scale T. Flynn & R. Kostecki 10.1016/j.geomat.2024.100028
39. Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al. 10.1016/j.ejrh.2021.100946
40. The patchiness of soil 13C versus the uniformity of 15N distribution with geomorphic position provides evidence of erosion and accelerated organic matter turnover M. Ghotbi et al. 10.1016/j.agee.2023.108616
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42. Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales Y. Xia et al. 10.3389/fsoil.2022.890437
43. Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework P. Bogaert et al. 10.1016/j.geoderma.2023.116604
44. Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping X. Zhang et al. 10.1016/j.geoderma.2023.116383
45. Impact of Land Cover Changes on Soil Type Mapping in Plain Areas: Evidence from Tongzhou District of Beijing, China X. Wu et al. 10.3390/land12091696
46. Using remote sensing to identify soil types based on multiscale image texture features M. Duan & X. Zhang 10.1016/j.compag.2021.106272
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48. Improving model performance in mapping cropland soil organic matter using time-series remote sensing data X. Zhang et al. 10.1016/j.jia.2024.01.015
49. Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation A. Suleymanov et al. 10.3390/land14050931
50. A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands M. Viljanen et al. 10.1186/s12942-022-00304-5
51. Oblique geographic coordinates as covariates for digital soil mapping A. Møller et al. 10.5194/soil-6-269-2020
52. Gap-free global annual soil moisture: 15 km grids for 1991–2018 M. Guevara et al. 10.5194/essd-13-1711-2021