56 citations as recorded by crossref.

1. An ensemble machine learning bioavailable strontium isoscape for Eastern Canada M. Le Corre et al.
2. 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.
3. Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France D. Urbina-Salazar et al.
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.
5. Downscaling digital soil maps using electromagnetic induction and aerial imagery A. Bjørn Møller et al.
6. BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al.
7. Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China X. Zhang et al.
8. Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network H. Chen & J. Wang
9. A comparison of country-scale subsoil predictions between a numeric and a taxonomic soil classification system T. Flynn et al.
10. Inequality in heat: The role of spatial patterns of urban green infrastructure P. Chen
11. National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images J. Xue et al.
12. Refined insights into Africa's plant-soil diversity interplay N. Kebonye et al.
13. Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties C. Xu et al.
14. Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland T. Broeg et al.
15. Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree N. Kebonye et al.
16. Adapting machine learning for environmental spatial data - A review M. Jemeļjanova et al.
17. Mapping the phosphorus sorption capacity of Danish soils in four depths with quantile regression forests and uncertainty propagation A. Møller et al.
18. Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al.
19. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al.
20. Soilscapes of New Zealand: Pedologic diversity as organised along environmental gradients P. Roudier et al.
21. 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.
22. 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.
23. On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization I. Dunkl & M. Ließ
24. Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al.
25. Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics G. Szatmári et al.
26. High-performance soil class delineation via UMAP coupled with machine learning in Kurdistan Province, Iran R. Taghizadeh-Mehrjardi et al.
27. High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management M. van Doorn et al.
28. High-Resolution Soil Property Maps from Digital Soil Mapping Methods, Czech Republic D. Žížala et al.
29. Incorporating Spatial Information for Regionalization of Environmental Parameters in Machine Learning Models M. Ohmer et al.
30. Reducing bias in cropland soil organic carbon and clay predictions using Sentinel-2 composites and data balancing T. Broeg et al.
31. Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al.
32. Can We Use Machine Learning for Agricultural Land Suitability Assessment? A. Møller et al.
33. Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland J. Skála et al.
34. Soil textural class modeling using digital soil mapping approaches: Effect of resampling strategies on imbalanced dataset predictions F. Mirzaei et al.
35. Major overlap in plant and soil organic carbon hotspots across Africa N. Kebonye et al.
36. Improving the Landsat-8 determinations of soil organic carbon and total nitrogen stocks through reducing the effects of buildings, water, and vegetation L. Lin et al.
37. Mapping benthic sediment types and composition in a turbid Jamaican bay using hydroacoustic data and different spatially explicit interpolation techniques K. McLaren
38. Spatial Downscaling of Global Categorical Soil Information Into Data Suitable for Land-Use Management T. FLYNN
39. Strontium isoscapes for provenance, mobility and migration: the way forward M. Spies et al.
40. Colombian soil texture: building a spatial ensemble model V. Varón-Ramírez et al.
41. Spatial downscaling of global soil texture classes into 30 m images at the province scale T. Flynn & R. Kostecki
42. Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al.
43. 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.
44. Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning C. Patriche et al.
45. 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.
46. Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework P. Bogaert et al.
47. Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping X. Zhang et al.
48. Impact of Land Cover Changes on Soil Type Mapping in Plain Areas: Evidence from Tongzhou District of Beijing, China X. Wu et al.
49. An assessment of spatial random forests for environmental mapping: the case of groundwater nitrate concentration J. Frank et al.
50. Using remote sensing to identify soil types based on multiscale image texture features M. Duan & X. Zhang
51. Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction G. Singh et al.
52. Improving model performance in mapping cropland soil organic matter using time-series remote sensing data X. Zhang et al.
53. Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation A. Suleymanov et al.
54. Can environmental clustering reveal soil profile patterns? A depth-based approach at field scale Z. Rasaei et al.
55. A bioavailable 87Sr/86Sr isoscape of Mongolia: Implications for the reconstruction of past human and animal mobility M. Le Corre et al.
56. A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands M. Viljanen et al.