163 citations as recorded by crossref.

1. Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics B. Szabó et al. 10.5194/hess-23-2615-2019
2. Mapping topsoil pH using different predictive models and covariate sets in Henan Province, Central China L. Zhang et al. 10.1016/j.ecoinf.2023.102290
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
5. Multi-source data integration for soil mapping using deep learning A. Wadoux et al. 10.5194/soil-5-107-2019
6. Using Machine Learning to make nanomaterials sustainable J. Scott-Fordsmand & M. Amorim 10.1016/j.scitotenv.2022.160303
7. A reliable Bayesian neural network for the prediction of reservoir thickness with quantified uncertainty L. Bao et al. 10.1016/j.cageo.2023.105409
8. Multiscale evaluations of global, national and regional digital soil mapping products in France B. Lemercier et al. 10.1016/j.geoderma.2022.116052
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
10. Incorporation of spatial autocorrelation improves soil–landform modeling at A and B horizons D. Kim et al. 10.1016/j.catena.2019.104226
11. Using bias correction and ensemble modelling for predictive mapping and related uncertainty: A case study in digital soil mapping J. Sylvain et al. 10.1016/j.geoderma.2021.115153
12. Mapping soil properties in a poorly-accessible area E. Costa et al. 10.36783/18069657rbcs20190107
13. A comparison of the use of local legacy soil data and global datasets for hydrological modelling a small-scale watersheds: Implications for nitrate loading estimation P. Krpec et al. 10.1016/j.geoderma.2020.114575
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
16. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach J. Yang et al. 10.5194/tc-14-1763-2020
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. Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning C. Patriche et al. 10.1371/journal.pone.0289286
20. Modeling sediment transport and flow velocity of thawed soil with straw returning D. Yang et al. 10.1016/j.jhydrol.2022.127805
21. 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
22. Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation A. Gebauer et al. 10.3389/fsoil.2021.770326
23. Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting G. Pereira et al. 10.1007/s11119-022-09880-9
24. Soil Organic Carbon Stock Prediction: Fate under 2050 Climate Scenarios, the Case of Eastern Ethiopia M. Negassa et al. 10.3390/su15086495
25. 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
26. Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands N. Levi et al. 10.1016/j.isprsjprs.2022.01.018
27. 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
28. CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile D. Dinamarca et al. 10.1038/s41597-023-02536-x
29. EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia A. Kmoch et al. 10.5194/essd-13-83-2021
30. IRAKA: The first Colombian soil information system with digital soil mapping products G. Araujo-Carrillo et al. 10.1016/j.catena.2020.104940
31. 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
32. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al. 10.5194/gmd-14-151-2021
33. Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al. 10.1016/j.catena.2021.105702
34. 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
35. 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
36. 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
37. Mapping of soils and land-related environmental attributes in modern agriculture systems using geomatics M. AbdelRahman et al. 10.1007/s40899-022-00704-2
38. Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands R. Yang & W. Guo 10.1007/s10661-019-7580-3
39. 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
40. A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping C. Wang et al. 10.1016/j.ecoinf.2023.102279
41. 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
42. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
43. National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach P. Roudier et al. 10.3390/rs12182872
44. 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
45. 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
46. 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
47. Consequences of spatial structure in soil–geomorphic data on the results of machine learning models D. Kim et al. 10.1080/10106049.2023.2245381
48. Extrapolation of Digital Soil Mapping Approaches for Soil Organic Carbon Stock Predictions in an Afromontane Environment J. Kotzé & J. van Tol 10.3390/land12030520
49. 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
50. 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
51. 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
52. 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
53. An l1-regularized total variation minimization model for soil interpolation based on geostatistical priors C. Wang et al. 10.1016/j.geoderma.2023.116412
54. A framework for optimizing environmental covariates to support model interpretability in digital soil mapping B. Kasraei et al. 10.1016/j.geoderma.2024.116873
55. 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
56. 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
57. Leveraging the application of Earth observation data for mapping cropland soils in Brazil J. Safanelli et al. 10.1016/j.geoderma.2021.115042
58. 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
59. 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
60. Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning M. Meliho et al. 10.3390/rs15102494
61. Clay content mapping and uncertainty estimation using weighted model averaging D. Zhao et al. 10.1016/j.catena.2021.105791
62. Evaluation of digital soil mapping approach for predicting soil fertility parameters—a case study from Karnataka Plateau, India S. Dharumarajan et al. 10.1007/s12517-022-09629-8
63. Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. 10.1111/ejss.13463
64. Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors B. Takoutsing & G. Heuvelink 10.1016/j.geoderma.2022.116192
65. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties R. Taghizadeh-Mehrjardi et al. 10.3390/rs14030472
66. 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
67. 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
68. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes P. Descombes et al. 10.1111/ecog.05117
69. 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
70. Optimal resolution of soil properties maps varies according to their geographical extent and location C. Piedallu et al. 10.1016/j.geoderma.2022.115723
71. 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
72. 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
73. Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data P. Sorenson et al. 10.1016/j.geoderma.2021.115316
74. Digital soil mapping of PAU-Regional Research Station, Kapurthala, Punjab, India R. Bhatt & D. Dwivedi 10.1007/s43538-022-00077-2
75. 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
76. Sampling and classifier modification to DSMART for disaggregating soil polygon maps T. Easher et al. 10.1016/j.geoderma.2023.116360
77. 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
78. Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data C. Silva et al. 10.3390/agriengineering6010006
79. A framework for the predictive mapping of forest soil properties in mountain areas A. Simon et al. 10.1016/j.geoderma.2020.114383
80. 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
81. Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data T. Nauman & M. Duniway 10.1016/j.geoderma.2019.03.037
82. Improving 3D Digital Soil Mapping Based on Spatialized Lab Soil Spectral Information Z. Sun et al. 10.3390/rs15215228
83. 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
84. 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
85. Large-area soil mapping based on environmental similarity with adaptive consideration of spatial distance to samples X. Fan et al. 10.1016/j.geoderma.2023.116683
86. Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions D. Žížala et al. 10.3390/rs11242947
87. Mapping the geogenic radon potential for Germany by machine learning E. Petermann et al. 10.1016/j.scitotenv.2020.142291
88. 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
89. Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests K. Vantas et al. 10.3390/w12030687
90. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
91. 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
92. 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
93. Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery S. Pizarro et al. 10.3390/rs15123203
94. Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau L. Greiner et al. 10.1016/j.geodrs.2018.e00181
95. High-resolution and three-dimensional mapping of soil texture of China F. Liu et al. 10.1016/j.geoderma.2019.114061
96. Uncertainty assessment of soil available water capacity using error propagation: A test in Languedoc-Roussillon Q. Styc & P. Lagacherie 10.1016/j.geoderma.2021.114968
97. Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go? F. Thomas et al. 10.3390/rs14061368
98. Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods M. Lu et al. 10.1016/j.compag.2022.107246
99. Oblique geographic coordinates as covariates for digital soil mapping A. Møller et al. 10.5194/soil-6-269-2020
100. A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China Y. Bao et al. 10.1016/j.still.2024.106010
101. Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran Y. Garosi et al. 10.1016/j.geodrs.2022.e00513
102. Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals M. Schönauer et al. 10.1016/j.jag.2022.102730
103. High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms T. Zhou et al. 10.1016/j.scitotenv.2020.138244
104. How and to What Extent Does Topography Control the Results of Soil Function Assessment: A Case Study From the Alps in South Tyrol (Italy) F. Gruber et al. 10.3390/soilsystems3010018
105. Producing the digital soil fertility map of Karelia S. Novikov 10.19047/0136-1694-2020-103-34-50
106. A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management D. Radočaj et al. 10.3390/su141912170
107. Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment Y. Garosi et al. 10.1080/01431161.2022.2147037
108. Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions R. Singh et al. 10.1016/j.ecoinf.2022.101933
109. Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China S. Lv et al. 10.1016/j.scitotenv.2024.172223
110. An integrated approach for the evaluation of quantitative soil maps through Taylor and solar diagrams A. Wadoux et al. 10.1016/j.geoderma.2021.115332
111. Multi-Horizon Predictive Soil Mapping of Historical Soil Properties Using Remote Sensing Imagery P. Sorenson et al. 10.3390/rs14225803
112. Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis P. Zhao et al. 10.1016/j.still.2023.105718
113. Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest W. He et al. 10.3390/rs16050785
114. Estimating soil texture from vis–NIR spectra E. Hobley & I. Prater 10.1111/ejss.12733
115. Optimal soil organic matter mapping using an ensemble model incorporating moderate resolution imaging spectroradiometer, portable X-ray fluorescence, and visible near-infrared data Y. Yan et al. 10.1016/j.compag.2023.107885
116. Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil R. Poppiel et al. 10.3390/rs11242905
117. Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques A. Naskar et al. 10.1007/s11356-023-29769-y
118. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative G. Szatmári et al. 10.3390/rs12244073
119. 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
120. 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
121. Urban soils as a spatial indicator of quality for urban socio-ecological systems S. Bonilla-Bedoya et al. 10.1016/j.jenvman.2021.113556
122. The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture D. Radočaj et al. 10.3390/rs14030778
123. Modelling the extent of northern peat soil and its uncertainty with Sentinel: Scotland as example of highly cloudy region L. Poggio et al. 10.1016/j.geoderma.2019.03.017
124. Modelling of the shallow water table at high spatial resolution using random forests J. Koch et al. 10.5194/hess-23-4603-2019
125. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula R. Siqueira et al. 10.1016/j.geoderma.2023.116405
126. 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
127. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty L. Poggio et al. 10.5194/soil-7-217-2021
128. Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning Q. Lu et al. 10.1016/j.scitotenv.2022.159171
129. 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
130. A Machine Learning-Based Approach for Spatial Estimation Using the Spatial Features of Coordinate Information S. Ahn et al. 10.3390/ijgi9100587
131. Digital soil mapping outputs on soil classification and sugarcane production in Brazil W. Mendes & J. Demattê 10.1016/j.jsames.2022.103881
132. Mapping of soil organic matter in a typical black soil area using Landsat-8 synthetic images at different time periods C. Luo et al. 10.1016/j.catena.2023.107336
133. Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data Y. Zhang et al. 10.3390/s21206715
134. Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging Y. Zhou et al. 10.3390/rs12010085
135. Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands? S. Wang et al. 10.3389/feart.2023.1340020
136. Ten challenges for the future of pedometrics A. Wadoux et al. 10.1016/j.geoderma.2021.115155
137. Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers M. Osat et al. 10.1007/s10661-023-12088-7
138. Harvesting spatially dense legacy soil datasets for digital soil mapping of available water capacity in Southern France Q. Styc et al. 10.1016/j.geodrs.2020.e00353
139. Improving soil property maps for precision agriculture in the presence of outliers using covariates M. Pusch et al. 10.1007/s11119-022-09898-z
140. New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems C. Schillaci et al. 10.1016/j.scitotenv.2021.146609
141. Three-dimensional modelling using spatial regression machine learning and hydrogeological basement VES G. Mendoza Veirana et al. 10.1016/j.cageo.2021.104907
142. Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping X. Zhang et al. 10.1016/j.geoderma.2023.116383
143. Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
144. Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114552
145. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
146. Added value of geophysics-based soil mapping in agro-ecosystem simulations C. Brogi et al. 10.5194/soil-7-125-2021
147. Taking digital soil mapping to the field: Lessons learned from the Water Smart Agriculture soil mapping project in Central America P. Owens et al. 10.1016/j.geodrs.2020.e00285
148. Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models F. Abedi et al. 10.1002/ldr.3811
149. Covariates in sample planning optimization for digital soil fertility mapping in agricultural areas M. Pusch et al. 10.1016/j.geoderma.2022.116252
150. Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation F. Veronesi & C. Schillaci 10.1016/j.ecolind.2019.02.026
151. Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin M. Kalumba et al. 10.3390/land11040591
152. The need for digital soil mapping in India S. Dharumarajan et al. 10.1016/j.geodrs.2019.e00204
153. A hybrid approach for predictive soil property mapping using conventional soil survey data T. Nauman & M. Duniway 10.1002/saj2.20080
154. 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
155. A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure E. Benà et al. 10.1016/j.scitotenv.2023.169569
156. Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2021.115108
157. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
158. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables T. Hengl et al. 10.7717/peerj.5518
159. Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models M. Nussbaum et al. 10.5194/soil-3-191-2017
160. Barest Pixel Composite for Agricultural Areas Using Landsat Time Series S. Diek et al. 10.3390/rs9121245
161. Modeling and mapping spatial distribution of baseline soil organic carbon stock, a case of West Hararghe, Oromia Regional State, Eastern Ethiopia M. Negassa et al. 10.1080/24749508.2023.2167632
162. Digital soil mapping as a basis for climatically oriented agriculture a thematic on the territory of the national crop testing fields of the Republic of Tatarstan, Russia I. Sahabiev et al. 10.1088/1755-1315/107/1/012122
163. Identifying multidisciplinary research gaps across Arctic terrestrial gradients A. Virkkala et al. 10.1088/1748-9326/ab4291