211 citations as recorded by crossref.

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2. Using Machine Learning to make nanomaterials sustainable J. Scott-Fordsmand & M. Amorim
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4. Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China J. Xiao et al.
5. A China dataset of soil properties for land surface modelling (version 2, CSDLv2) G. Shi et al.
6. Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran Z. Ebrahimi-Khusfi et al.
7. Incorporation of spatial autocorrelation improves soil–landform modeling at A and B horizons D. Kim et al.
8. Using bias correction and ensemble modelling for predictive mapping and related uncertainty: A case study in digital soil mapping J. Sylvain et al.
9. 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. An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study M. Castiello & M. Tonini
11. Unravelling the impact of soil data quality on species distribution models of temperate forest woody plants F. Rota et al.
12. Modeling sediment transport and flow velocity of thawed soil with straw returning D. Yang et al.
13. Integrating ensemble learning and rocky desertification indices improves accuracy and interpretability of soil thickness prediction in karst landscapes F. Fang et al.
14. Soil Organic Carbon Stock Prediction: Fate under 2050 Climate Scenarios, the Case of Eastern Ethiopia M. Negassa et al.
15. No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America M. Guevara et al.
16. Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands N. Levi et al.
17. Effects of environmental factors on the population dynamics of potential pathogenic bacterial community attached to marine plastic debris H. Kim et al.
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19. Modelling and mapping maize yields and making fertilizer recommendations with uncertain soil information B. Takoutsing et al.
20. Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes J. Vollering et al.
21. IRAKA: The first Colombian soil information system with digital soil mapping products G. Araujo-Carrillo et al.
22. Health risk assessment of nitrate using a probabilistic approach in groundwater resources of western part of Iran M. Sakizadeh & C. Zhang
23. Soil salinization monitoring method evolution at various spatial and temporal scales in arid context: a review Z. Besma et al.
24. Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands R. Yang & W. Guo
25. Improvement of soil property mapping in the Great Clay Belt of northern Ontario using multi-source remotely sensed data R. Pittman et al.
26. Integrating Genetic Algorithm and Geographically Weighted Approaches into Machine Learning Improves Soil pH Prediction in China W. Zhang et al.
27. National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach P. Roudier et al.
28. Integration of multispectral and hyperspectral data to map magnetic susceptibility and soil attributes at depth: A novel framework W. Mendes et al.
29. Consequences of spatial structure in soil–geomorphic data on the results of machine learning models D. Kim et al.
30. Soil texture and lithological discontinuity mapping for sustainable land use planning: An application of digital soil mapping in a part of Eastern Himalayan Foothills, India S. Chattaraj et al.
31. 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.
32. A nature‐inclusive future with healthy soils? Mapping soil organic matter in 2050 in the Netherlands A. Helfenstein et al.
33. An l1-regularized total variation minimization model for soil interpolation based on geostatistical priors C. Wang et al.
34. Evaluating Airborne Hyperspectral Scanner (AHS) for the mapping of soil organic matter and clay in a Mediterranean forest ecosystem F. Canero et al.
35. Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data S. Rahmani et al.
36. Artificial intelligence in soil science A. Wadoux
37. 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.
38. Spatial mapping and modelling of soil organic carbon using random forest and remote sensing variables in part of Kaduna, Northern Nigeria M. Yaqub et al.
39. Clay content mapping and uncertainty estimation using weighted model averaging D. Zhao et al.
40. Evaluation of digital soil mapping approach for predicting soil fertility parameters—a case study from Karnataka Plateau, India S. Dharumarajan et al.
41. Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al.
42. 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
43. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties R. Taghizadeh-Mehrjardi et al.
44. Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain N. Rengma et al.
45. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes P. Descombes et al.
46. 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.
47. Spatially heterogeneous controls of soil organic carbon in a karst mountainous area of southern China: Insights from interpretable machine learning H. Xu et al.
48. Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model D. Baldwin et al.
49. Degree of spatial interpretability H. Liu et al.
50. Digital soil mapping of PAU-Regional Research Station, Kapurthala, Punjab, India R. Bhatt & D. Dwivedi
51. Mining soil data of Switzerland: New maps for soil texture, soil organic carbon, nitrogen, and phosphorus S. Gupta et al.
52. A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements A. Molla et al.
53. Estimation of soil thickness in karst landforms using a quantile regression forests approach F. Fang et al.
54. Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data T. Nauman & M. Duniway
55. Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions D. Žížala et al.
56. Mapping the geogenic radon potential for Germany by machine learning E. Petermann et al.
57. Ecosystem-dependent two-stage changes in soil organic carbon stock across the contiguous United States from 1970 to 2014 F. Shen et al.
58. Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach J. Medina-Rubio et al.
59. Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau L. Greiner et al.
60. High-resolution and three-dimensional mapping of soil texture of China F. Liu et al.
61. Uncertainty assessment of soil available water capacity using error propagation: A test in Languedoc-Roussillon Q. Styc & P. Lagacherie
62. Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go? F. Thomas et al.
63. Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods M. Lu et al.
64. 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.
65. Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran S. Manteghi et al.
66. Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals M. Schönauer et al.
67. A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management D. Radočaj et al.
68. 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.
69. Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions R. Singh et al.
70. 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.
71. Multi-Horizon Predictive Soil Mapping of Historical Soil Properties Using Remote Sensing Imagery P. Sorenson et al.
72. Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis P. Zhao et al.
73. Estimating soil texture from vis–NIR spectra E. Hobley & I. Prater
74. Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil R. Poppiel et al.
75. Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques A. Naskar et al.
76. The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture D. Radočaj et al.
77. Modelling the extent of northern peat soil and its uncertainty with Sentinel: Scotland as example of highly cloudy region L. Poggio et al.
78. Modelling of the shallow water table at high spatial resolution using random forests J. Koch et al.
79. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula R. Siqueira et al.
80. 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.
81. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty L. Poggio et al.
82. Utilizing Machine Learning to Map Aquatic Weed Yields for Biochar Production: A Case Study in the Bangweulu Wetlands, Zambia F. Banda et al.
83. High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management M. van Doorn et al.
84. Digital soil mapping outputs on soil classification and sugarcane production in Brazil W. Mendes & J. Demattê
85. Covariate selection approaches in spatial prediction of soil quality indices using machine learning models at the watershed scale, west of Iran M. Zandi Baghche-Maryam et al.
86. Optimizing soil texture spatial prediction in the Brazilian Cerrado: Insights from random forest and spectral data M. Pelegrino et al.
87. Digital Mapping of Key Static Soil Attributes of Tamil Nadu, India using Legacy Soil Information B. Kalaiselvi et al.
88. Improving soil property maps for precision agriculture in the presence of outliers using covariates M. Pusch et al.
89. Estimating soil salinity in cotton fields using UAV multispectral remote sensing and SSA–SVM optimised machine learning model J. Li et al.
90. Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al.
91. Added value of geophysics-based soil mapping in agro-ecosystem simulations C. Brogi et al.
92. Taking digital soil mapping to the field: Lessons learned from the Water Smart Agriculture soil mapping project in Central America P. Owens et al.
93. Covariates in sample planning optimization for digital soil fertility mapping in agricultural areas M. Pusch et al.
94. Priority areas identification for arable soil pollution prevention based on the accumulative risk of heavy metals X. Li et al.
95. The need for digital soil mapping in India S. Dharumarajan et al.
96. Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics B. Szabó et al.
97. Mapping topsoil pH using different predictive models and covariate sets in Henan Province, Central China L. Zhang et al.
98. 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.
99. Multi-source data integration for soil mapping using deep learning A. Wadoux et al.
100. Multiscale evaluations of global, national and regional digital soil mapping products in France B. Lemercier et al.
101. Mapping soil properties in a poorly-accessible area E. Costa et al.
102. 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.
103. Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data S. Saidi et al.
104. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach J. Yang et al.
105. Payments by modelled results: A novel design for agri-environmental schemes B. Bartkowski et al.
106. Evaluation of Global Data for National-Scale Soil Depth Mapping in Data-Scarce Regions: A Case Study from Sri Lanka E. Jahanshiri et al.
107. Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning C. Patriche et al.
108. Storage, patterns, and environmental controls of soil organic carbon stocks in the permafrost regions of the Northern Hemisphere T. Wu et al.
109. Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation A. Gebauer et al.
110. Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates M. Zhang et al.
111. Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting G. Pereira et al.
112. Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario R. Pittman & B. Hu
113. Improving soil organic carbon mapping in intensively cultivated Mollisols: A geographically adjusted Gaussian mixture model incorporating tillage practices and spectral indicators Q. Tan et al.
114. 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.
115. Spatial and temporal dynamics of plant water source distribution in China H. Chen et al.
116. CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile D. Dinamarca et al.
117. Spatial modeling of soil chemical properties in an arid region of Central Iran using machine learning and remote sensing data A. Molaeinasab et al.
118. EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia A. Kmoch et al.
119. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al.
120. Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al.
121. Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas N. Fan et al.
122. Finer soil properties mapping framework for broad-scale area: A case study of Hubei Province, China R. Wang et al.
123. Modelling of soil depth and hydraulic properties at regional level using environmental covariates- A case study in India S. Dharumarajan et al.
124. An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data A. Auzzas et al.
125. Mapping of soils and land-related environmental attributes in modern agriculture systems using geomatics M. AbdelRahman et al.
126. National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France A. Suleymanov et al.
127. Soil thickness prediction models: Types, accuracy and influencing factors Q. Zhu et al.
128. A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping C. Wang et al.
129. Mapping soil total carbon using multisource remote sensing at the continental scale T. Zhou et al.
130. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques S. Araya et al.
131. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux
132. 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.
133. 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.
134. Extrapolation of Digital Soil Mapping Approaches for Soil Organic Carbon Stock Predictions in an Afromontane Environment J. Kotzé & J. van Tol
135. Urban soil management in the strategies for adaptation to climate change of cities in the Tropical Andes S. Bonilla-Bedoya et al.
136. 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.
137. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al.
138. A framework for optimizing environmental covariates to support model interpretability in digital soil mapping B. Kasraei et al.
139. Enhancing Soil Mapping with Hyperspectral Subsurface Images generated from soil lab Vis-SWIR spectra tested in southern Brazil Y. Gelsleichter et al.
140. A Comprehensive Evaluation of Machine Learning Algorithms for Digital Soil Organic Carbon Mapping on a National Scale D. Radočaj et al.
141. Leveraging the application of Earth observation data for mapping cropland soils in Brazil J. Safanelli et al.
142. Developing high-resolution soil organic carbon maps across Ethiopia through a machine learning approach B. Tikuye et al.
143. Mapping Soil Properties in the Haihun River Sub-Watershed, Yangtze River Basin, China, by Integrating Machine Learning and Variable Selection J. Huang et al.
144. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al.
145. 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.
146. Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning M. Meliho et al.
147. Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study X. Sun et al.
148. Digital Soil Mapping of Soil Macronutrients (N, P, K) in Emilia-Romagna (NE Italy): A Regional Baseline for the EU Soil Monitoring Law F. Ungaro et al.
149. Optimal resolution of soil properties maps varies according to their geographical extent and location C. Piedallu et al.
150. Mapping soil fertility properties in central Ethiopia at 100 m spatial resolution M. Redi et al.
151. Extracting the pickpocketing information implied in the built environment by treating it as the anomalies Y. Yao et al.
152. Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data P. Sorenson et al.
153. 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.
154. Sampling and classifier modification to DSMART for disaggregating soil polygon maps T. Easher et al.
155. Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data C. Silva et al.
156. Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows G. Samodra et al.
157. A framework for the predictive mapping of forest soil properties in mountain areas A. Simon et al.
158. Improving 3D Digital Soil Mapping Based on Spatialized Lab Soil Spectral Information Z. Sun et al.
159. 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.
160. 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.
161. Large-area soil mapping based on environmental similarity with adaptive consideration of spatial distance to samples X. Fan et al.
162. Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland J. Skála et al.
163. Spatio-temporal mapping reveals changes in soil organic carbon stocks across the contiguous United States since 1955 C. Yang et al.
164. Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources L. Greiner et al.
165. Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests K. Vantas et al.
166. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al.
167. Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map F. Parsaie et al.
168. 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.
169. Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery S. Pizarro et al.
170. Oblique geographic coordinates as covariates for digital soil mapping A. Møller et al.
171. A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China Y. Bao et al.
172. Simulation of Soil Organic Carbon and Inorganic Carbon in the Upper Yellow River Basin Based on Random Forest-Kriging Model and Their Driving Mechanisms J. She et al.
173. 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.
174. 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.
175. Producing the digital soil fertility map of Karelia S. Novikov
176. An integrated approach for the evaluation of quantitative soil maps through Taylor and solar diagrams A. Wadoux et al.
177. Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest W. He et al.
178. 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.
179. 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
180. Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al.
181. Urban soils as a spatial indicator of quality for urban socio-ecological systems S. Bonilla-Bedoya et al.
182. Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning Q. Lu et al.
183. A Machine Learning-Based Approach for Spatial Estimation Using the Spatial Features of Coordinate Information S. Ahn et al.
184. Mapping of soil organic matter in a typical black soil area using Landsat-8 synthetic images at different time periods C. Luo et al.
185. Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data Y. Zhang et al.
186. Comparative assessment of spectral covariates from Sentinel 1 A, Sentinel 2 A, Landsat 8, and PRISMA for digital soil mapping of infiltration rate and textural classes N. Moorthi et al.
187. Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging Y. Zhou et al.
188. 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.
189. Ten challenges for the future of pedometrics A. Wadoux et al.
190. Modeling Soil pH at regional scale using environmental covariates and machine learning algorithm R. Vasundhara et al.
191. Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers M. Osat et al.
192. Improved estimation of soil organic carbon stock in subtropical cropland of Southern China based on digital soil mapping and multi-sources data B. Hu et al.
193. Harvesting spatially dense legacy soil datasets for digital soil mapping of available water capacity in Southern France Q. Styc et al.
194. New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems C. Schillaci et al.
195. Three-dimensional modelling using spatial regression machine learning and hydrogeological basement VES G. Mendoza Veirana et al.
196. Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping X. Zhang et al.
197. Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al.
198. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al.
199. A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware river basin, USA P. Goodling et al.
200. Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models F. Abedi et al.
201. A Novel Self-Supervised Learning Method for Accurate Multigranularity Prediction of Soil Organic Carbon Z. Yu et al.
202. Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation F. Veronesi & C. Schillaci
203. Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin M. Kalumba et al.
204. A hybrid approach for predictive soil property mapping using conventional soil survey data T. Nauman & M. Duniway
205. On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization I. Dunkl & M. Ließ
206. 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.
207. Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping R. Taghizadeh-Mehrjardi et al.
208. High-resolution mapping of soil mechanical properties by integrating geophysical sensors and terrain attributes A. Khatkar et al.
209. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al.
210. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables T. Hengl et al.
211. Mapping stem volume in fast-growing eucalypt plantations: integrating spectral, textural, and temporal remote sensing information with forest inventories and spatial models L. Aló et al.