181 citations as recorded by crossref.

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3. Using geostatistics and machine learning models to analyze the influence of soil nutrients and terrain attributes on lead prediction in forest soils S. Ahado et al. 10.1007/s40808-023-01890-4
4. From Crops to Kilowatts: An Empirical Study on Farmland Conversion to Solar Photovoltaic Systems in Kushida River Basin, Japan Z. Xie et al. 10.3390/geographies4020014
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6. Using Machine Learning to make nanomaterials sustainable J. Scott-Fordsmand & M. Amorim 10.1016/j.scitotenv.2022.160303
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9. Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran Z. Ebrahimi-Khusfi et al. 10.1016/j.apr.2020.08.029
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14. 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
15. Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data S. Saidi et al. 10.3390/s22186890
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17. Payments by modelled results: A novel design for agri-environmental schemes B. Bartkowski et al. 10.1016/j.landusepol.2020.105230
18. An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study M. Castiello & M. Tonini 10.5334/jcaa.71
19. Unravelling the impact of soil data quality on species distribution models of temperate forest woody plants F. Rota et al. 10.1016/j.scitotenv.2024.173719
20. Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning C. Patriche et al. 10.1371/journal.pone.0289286
21. Modeling sediment transport and flow velocity of thawed soil with straw returning D. Yang et al. 10.1016/j.jhydrol.2022.127805
22. Storage, patterns, and environmental controls of soil organic carbon stocks in the permafrost regions of the Northern Hemisphere T. Wu et al. 10.1016/j.scitotenv.2022.154464
23. Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation A. Gebauer et al. 10.3389/fsoil.2021.770326
24. Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates M. Zhang et al. 10.3390/rs16213939
25. Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting G. Pereira et al. 10.1007/s11119-022-09880-9
26. Soil Organic Carbon Stock Prediction: Fate under 2050 Climate Scenarios, the Case of Eastern Ethiopia M. Negassa et al. 10.3390/su15086495
27. No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America M. Guevara et al. 10.5194/soil-4-173-2018
28. Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands N. Levi et al. 10.1016/j.isprsjprs.2022.01.018
29. Prediction of Ground Vibration at Surface for Ring Blasting in Sublevel Stoping Through Empirical Approach, k-Nearest Neighbor, and Random Forest Model A. Vishwakarma et al. 10.1007/s42461-024-00976-6
30. Mapping soil thickness by accounting for right‐censored data with survival probabilities and machine learning S. van der Westhuizen et al. 10.1111/ejss.13589
31. Modelling and mapping maize yields and making fertilizer recommendations with uncertain soil information B. Takoutsing et al. 10.1007/s11119-024-10200-6
32. CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile D. Dinamarca et al. 10.1038/s41597-023-02536-x
33. EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia A. Kmoch et al. 10.5194/essd-13-83-2021
34. IRAKA: The first Colombian soil information system with digital soil mapping products G. Araujo-Carrillo et al. 10.1016/j.catena.2020.104940
35. Health risk assessment of nitrate using a probabilistic approach in groundwater resources of western part of Iran M. Sakizadeh & C. Zhang 10.1007/s12665-019-8786-y
36. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al. 10.5194/gmd-14-151-2021
37. Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al. 10.1016/j.catena.2021.105702
38. Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas N. Fan et al. 10.1016/j.jag.2022.102986
39. Finer soil properties mapping framework for broad-scale area: A case study of Hubei Province, China R. Wang et al. 10.1016/j.geoderma.2024.117023
40. Soil salinization monitoring method evolution at various spatial and temporal scales in arid context: a review Z. Besma et al. 10.1007/s12517-021-06557-x
41. Modelling of soil depth and hydraulic properties at regional level using environmental covariates- A case study in India S. Dharumarajan et al. 10.1016/j.geodrs.2021.e00439
42. An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data A. Auzzas et al. 10.1007/s40808-024-02127-8
43. Mapping of soils and land-related environmental attributes in modern agriculture systems using geomatics M. AbdelRahman et al. 10.1007/s40899-022-00704-2
44. National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France A. Suleymanov et al. 10.1016/j.geodrs.2024.e00801
45. Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands R. Yang & W. Guo 10.1007/s10661-019-7580-3
46. Improvement of soil property mapping in the Great Clay Belt of northern Ontario using multi-source remotely sensed data R. Pittman et al. 10.1016/j.geoderma.2020.114761
47. A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping C. Wang et al. 10.1016/j.ecoinf.2023.102279
48. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques S. Araya et al. 10.5194/hess-25-2739-2021
49. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
50. National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach P. Roudier et al. 10.3390/rs12182872
51. Integration of multispectral and hyperspectral data to map magnetic susceptibility and soil attributes at depth: A novel framework W. Mendes et al. 10.1016/j.geoderma.2020.114885
52. Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran K. Azizi et al. 10.1016/j.still.2023.105681
53. 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. 10.1016/j.catena.2023.107677
54. Consequences of spatial structure in soil–geomorphic data on the results of machine learning models D. Kim et al. 10.1080/10106049.2023.2245381
55. Extrapolation of Digital Soil Mapping Approaches for Soil Organic Carbon Stock Predictions in an Afromontane Environment J. Kotzé & J. van Tol 10.3390/land12030520
56. Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images T. Zhou et al. 10.1016/j.scitotenv.2020.142661
57. Urban soil management in the strategies for adaptation to climate change of cities in the Tropical Andes S. Bonilla-Bedoya et al. 10.1016/j.geoderma.2022.115840
58. Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization S. Dega et al. 10.3389/fenvs.2023.1009191
59. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
60. A nature‐inclusive future with healthy soils? Mapping soil organic matter in 2050 in the Netherlands A. Helfenstein et al. 10.1111/ejss.13529
61. An l1-regularized total variation minimization model for soil interpolation based on geostatistical priors C. Wang et al. 10.1016/j.geoderma.2023.116412
62. A framework for optimizing environmental covariates to support model interpretability in digital soil mapping B. Kasraei et al. 10.1016/j.geoderma.2024.116873
63. Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data S. Rahmani et al. 10.3390/agronomy12061338
64. Enhancing Soil Mapping with Hyperspectral Subsurface Images generated from soil lab Vis-SWIR spectra tested in southern Brazil Y. Gelsleichter et al. 10.1016/j.geodrs.2023.e00641
65. A Comprehensive Evaluation of Machine Learning Algorithms for Digital Soil Organic Carbon Mapping on a National Scale D. Radočaj et al. 10.3390/app14219990
66. Leveraging the application of Earth observation data for mapping cropland soils in Brazil J. Safanelli et al. 10.1016/j.geoderma.2021.115042
67. Mapping Soil Properties in the Haihun River Sub-Watershed, Yangtze River Basin, China, by Integrating Machine Learning and Variable Selection J. Huang et al. 10.3390/s24123784
68. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. 10.1016/j.geoderma.2019.05.031
69. Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm Y. Ellili-Bargaoui et al. 10.5194/soil-6-371-2020
70. Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning M. Meliho et al. 10.3390/rs15102494
71. Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing X. Wang et al. 10.1016/j.still.2024.106311
72. Clay content mapping and uncertainty estimation using weighted model averaging D. Zhao et al. 10.1016/j.catena.2021.105791
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76. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties R. Taghizadeh-Mehrjardi et al. 10.3390/rs14030472
77. Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study X. Sun et al. 10.1016/j.catena.2022.106217
78. Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain N. Rengma et al. 10.1007/s10661-023-11608-9
79. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes P. Descombes et al. 10.1111/ecog.05117
80. Can soil fertility properties in rice fields in sub-Saharan Africa be predicted by digital soil information? A case study of AfSoilGrids250m J. Djagba et al. 10.1016/j.geodrs.2022.e00563
81. Optimal resolution of soil properties maps varies according to their geographical extent and location C. Piedallu et al. 10.1016/j.geoderma.2022.115723
82. Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model D. Baldwin et al. 10.3390/rs11172013
83. Extracting the pickpocketing information implied in the built environment by treating it as the anomalies Y. Yao et al. 10.1016/j.cities.2023.104575
84. Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data P. Sorenson et al. 10.1016/j.geoderma.2021.115316
85. Digital soil mapping of PAU-Regional Research Station, Kapurthala, Punjab, India R. Bhatt & D. Dwivedi 10.1007/s43538-022-00077-2
86. National-scale spatial prediction of soil organic carbon and total nitrogen using long-term optical and microwave satellite observations in Google Earth Engine T. Zhou et al. 10.1016/j.compag.2023.107928
87. Sampling and classifier modification to DSMART for disaggregating soil polygon maps T. Easher et al. 10.1016/j.geoderma.2023.116360
88. Mining soil data of Switzerland: New maps for soil texture, soil organic carbon, nitrogen, and phosphorus S. Gupta et al. 10.1016/j.geodrs.2023.e00747
89. Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data C. Silva et al. 10.3390/agriengineering6010006
90. Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows G. Samodra et al. 10.1016/j.aiig.2024.100093
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92. A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements A. Molla et al. 10.1007/s00477-022-02284-1
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94. Improving 3D Digital Soil Mapping Based on Spatialized Lab Soil Spectral Information Z. Sun et al. 10.3390/rs15215228
95. Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North U. Acharya et al. 10.3390/soilsystems5040057
96. Evaluating the impact of using digital soil mapping products as input for spatializing a crop model: The case of drainage and maize yield simulated by STICS in the Berambadi catchment (India) P. Lagacherie et al. 10.1016/j.geoderma.2021.115503
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99. Mapping the geogenic radon potential for Germany by machine learning E. Petermann et al. 10.1016/j.scitotenv.2020.142291
100. Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources L. Greiner et al. 10.5194/soil-4-123-2018
101. Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests K. Vantas et al. 10.3390/w12030687
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103. Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map F. Parsaie et al. 10.1007/s10661-021-08947-w
104. Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region P. Alaboz et al. 10.1080/03650340.2023.2248002
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118. Producing the digital soil fertility map of Karelia S. Novikov 10.19047/0136-1694-2020-103-34-50
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129. Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil R. Poppiel et al. 10.3390/rs11242905
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131. What is the Best Inference Trajectory for Mapping Soil Functions: An Example of Mapping Soil Available Water Capacity over Languedoc Roussillon (France) Q. Styc & P. Lagacherie 10.3390/soilsystems3020034
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138. What are the most crucial soil variables for predicting the distribution of mountain plant species? A comprehensive study in the Swiss Alps A. Buri et al. 10.1111/jbi.13803
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154. Three-dimensional modelling using spatial regression machine learning and hydrogeological basement VES G. Mendoza Veirana et al. 10.1016/j.cageo.2021.104907
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