209 citations as recorded by crossref.

1. Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces Y. Wang et al.
2. Monitoring regional soil organic matter content using a spatiotemporal model with time-series synthetic Landsat images M. Zhang et al.
3. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks X. Ge et al.
4. A Bibliometric Study on Machine Learning-Based Quantification of Agricultural Soil Respiration and Implications for the Management of Agricultural Soil Carbon Sinks T. Chen et al.
5. A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables L. Zhang et al.
6. Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density M. Zhang et al.
7. Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review P. Agyeman et al.
8. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes P. Descombes et al.
9. A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information J. Wang et al.
10. A novel remote sensing approach for estimating soil temperature at depth F. Aliabad et al.
11. Machine Learning and Artificial Intelligence Applications in Soil Science B. Minasny & A. McBratney
12. Enhanced soil organic carbon mapping in Gannan’s alpine meadows: A comparative analysis of machine learning models and satellite data X. Liu et al.
13. Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al.
14. Comparing UNet configurations for anthropogenic geomorphic feature extraction from land surface parameters S. Farhadpour et al.
15. Using a fuzzy logic approach to reveal soil-landscape relationships produced by digital soil maps in the humid tropical region of East Java, Indonesia D. Cahyana et al.
16. 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.
17. Digital mapping of the global soil δ15N at 0.1° × 0.1° resolution using random forest regression with climate classification Q. Zan et al.
18. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al.
19. Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique S. Raghubanshi et al.
20. Soil inorganic carbon distribution, stocks and environmental thresholds along a major climatic gradient M. Pfeiffer et al.
21. The Impact of COVID-19 Lockdown on People’s Mobility and Water Quality of Siak River, Indonesia S. Hasibuan et al.
22. Modeling soil organic carbon in the Brazilian amazon with geostatistical and machine learning techniques G. Tiruneh et al.
23. The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement M. Rybansky & J. Rada
24. Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China L. Zhang et al.
25. Benefits and Challenges of Artificial Intelligence in Soil Science—A Review C. Kikis & V. Antoniadis
26. Assessing human impacts on soil organic carbon change in the Lower Namoi Valley, Australia H. Jang et al.
27. Estimating Gully Erosion Induced by Heavy Rainfall Events Using Stereoscopic Imagery and UAV LiDAR L. Wang et al.
28. Combined diffuse reflectance spectroscopy and digital soil mapping for soil assessment in smallholder farms N. Purushothaman et al.
29. Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data D. Rukhovich et al.
30. Free iron oxide content in tropical soils predicted by integrative digital mapping W. Mendes et al.
31. Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al.
32. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al.
33. A reliable Bayesian neural network for the prediction of reservoir thickness with quantified uncertainty L. Bao et al.
34. Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions S. Mousavi et al.
35. Towards near real-time national-scale soil water content monitoring using data fusion as a downscaling alternative I. Fuentes et al.
36. Self-supervised learning of Vision Transformers for digital soil mapping using visual data P. Tresson et al.
37. A spatial assessment of mercury content in the European Union topsoil C. Ballabio et al.
38. Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization R. Agarwal et al.
39. Digital soil mapping based on the similarity of geographic environment over spatial neighborhoods F. Zhao et al.
40. Improving Soil Organic Matter Mapping Using Transfer Learning and Satellite-Simulated Samples From Bare Soil Hyperspectral Imagery X. Xu et al.
41. Review on Crop Prediction Using Deep Learning Techniques M. Dharani et al.
42. GIS and Remote Sensing Applications for Assessing Soil Contamination in South African Agriculture: A Machine Learning-Enhanced Scoping Review G. Nxumalo et al.
43. Geomorphometry today I. Florinsky
44. Developing high-resolution soil organic carbon maps across Ethiopia through a machine learning approach B. Tikuye et al.
45. Soil carbon sequestration potential in global croplands J. Padarian et al.
46. Simultaneously mapping the 3D distributions of multiple heavy metals in an industrial site using deep learning and multisource auxiliary data Y. Peng et al.
47. Artificial intelligence in soil science A. Wadoux
48. Spatiotemporal modelling of soil organic matter changes in Jiangsu, China between 1980 and 2006 using INLA-SPDE X. Sun et al.
49. Mapping soil profile depth, bulk density and carbon stock in Scotland using remote sensing and spatial covariates M. Aitkenhead & M. Coull
50. Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil K. JOHN et al.
51. High resolution spatial modelling of soil parent material: An initial investigation study using random forest machine learning techniques A. Vijayakumar et al.
52. Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra A. Descals et al.
53. ML-based technologies in sustainable agro-food production and beyond: Tapping the (semi) arid landscape for bioactives-based product development T. Joshi et al.
54. Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning M. Widyastuti et al.
55. Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al.
56. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al.
57. Modeling and digital mapping of shallow water table depth using satellite-based spectral and thermal data: Introducing a framework for digital shallow water table mapping M. Jeihouni et al.
58. Large-Scale Soil Organic Carbon Estimation via a Multisource Data Fusion Approach E. Kalopesa et al.
59. Rate of soil organic carbon sequestration in a millennium coastal soil chronosequence in northern Jiangsu, China C. Jiao et al.
60. Geochemical and mineralogical composition of the 2018 volcanic deposits of Mt. Anak Krakatau D. Fiantis et al.
61. Assessing the improvement potentials of climate model partitioning and time-variant feature extraction for soil organic carbon prediction Y. Bao et al.
62. A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network M. Kazemi Garajeh et al.
63. An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran M. Garajeh et al.
64. Mapping of Soil Organic Carbon Stocks Based on Aerial Photography in a Fragmented Desertification Landscape J. Lian et al.
65. Prediction of the spatial distribution of soil organic carbon content in Central European agriculturally used peatlands: a case study of the Grójec Valley, Central Poland S. Pindral et al.
66. Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications A. Hamedianfar et al.
67. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al.
68. Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India T. Tarun Kshatriya et al.
69. Mapping Soil Organic Matter Content during the Bare Soil Period by Using Satellite Data and an Improved Deep Learning Network X. Xu & X. Zhai
70. A global soil spectral grid based on space sensing J. Demattê et al.
71. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ
72. Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al.
73. Use of Topographic Models for Mapping Soil Properties and Processes X. Li et al.
74. From Rangelands to Cropland, Land-Use Change and Its Impact on Soil Organic Carbon Variables in a Peruvian Andean Highlands: A Machine Learning Modeling Approach M. Carbajal et al.
75. Exploring the untapped potential of hand-feel soil texture data for enhancing digital soil mapping: Revealing hidden spatial patterns from field observations A. Eymard et al.
76. Predicting wetland soil properties using machine learning, geophysics, and soil measurement data D. Driba et al.
77. History and interpretation of early soil and organic matter investigations in Deli, Sumatra, Indonesia B. Minasny et al.
78. Towards a two-stage low-cost soil carbon estimation over large area using proximal sensing and digital soil mapping A. Das et al.
79. Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al.
80. High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine . Fatchurrachman et al.
81. Developing and testing of pedogenons in the lower Namoi valley, NSW, Australia H. Jang et al.
82. Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images D. Kim et al.
83. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al.
84. National-scale mapping of soil organic carbon stock in France: New insights and lessons learned by direct and indirect approaches Z. Chen et al.
85. A novel local-global dependency deep learning model for soil mapping Q. Li et al.
86. SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopy J. Martins et al.
87. A new methodology for establishing an SOC content prediction model that is spatiotemporally transferable at multidecadal and intercontinental scales X. Meng et al.
88. Spatiotemporal mapping of chemical soil degradation in Iranian arid lands using legacy soil data and hybrid RF–IMDPA H. Fathizad & M. Ardakani
89. A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China Y. Bao et al.
90. Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia A. Suleymanov et al.
91. Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India R. Kumaraperumal et al.
92. Prediction of Soil Organic Carbon Content in Complex Vegetation Areas Based on CNN-LSTM Model Z. Dong et al.
93. Digital Mapping of Soil Physicochemical Properties for Sustainable Irrigation Management in a Semi-Arid Region of Central Mexico O. Galván-Cano et al.
94. BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al.
95. An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms X. Meng et al.
96. Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data N. Tziolas et al.
97. Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran V. Habibi et al.
98. Machine learning and soil sciences: a review aided by machine learning tools J. Padarian et al.
99. An explainable machine learning model for accurate estimation of residual phthalate esters in Chinese agricultural soils W. Wang et al.
100. Harnessing machine learning and geospatial technologies for precise soil erodibility mapping and prediction W. Abiye et al.
101. Grand Challenges in Pedometrics-AI Research S. Grunwald
102. Assessing the carbon sequestration potential and identifying influential factors of cultivated soils in Northeast China S. Wang et al.
103. Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine Y. Geng et al.
104. The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review R. Wang & W. Su
105. Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon M. Goydaragh et al.
106. Mapping soil organic matter contents under the impact of soil erosion intensity Z. Liu et al.
107. Three-Dimensional Mapping of Forest Soil Carbon Stocks Using SCORPAN Modelling and Relative Depth Gradients in the North-Eastern Lowlands of Germany A. Russ et al.
108. Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors Y. Yan et al.
109. Soil Science-Informed Machine Learning B. Minasny et al.
110. Multi-level wavelet deep learning for soil property prediction based on multi-platform, multi-modal remote sensing data Y. Bao et al.
111. Path to robust digital mapping of soil C:N ratio: geographic bias and uncertainty gap Y. Hong et al.
112. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux
113. Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging Z. Zhu et al.
114. Unveiling the “Sparse Carbon Pool”: High-Resolution Mapping and Storage Estimation of Topsoil Organic Carbon in Arid Xinjiang, China Y. Li et al.
115. A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping C. Wang et al.
116. autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil H. Rodrigues et al.
117. Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms P. Zeng et al.
118. Automated near-real-time mapping and monitoring of rice growth extent and stages in Selangor Malaysia . Fatchurrachman et al.
119. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al.
120. 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.
121. Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran S. Mousavi et al.
122. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison X. Zhu et al.
123. High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra J. Wagner et al.
124. Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al.
125. Estimation of soluble solids content and fruit temperature in 'Rocha' pear using Vis-NIR spectroscopy and the SpectraNet–32 deep learning architecture J. Martins et al.
126. Exploring soil organic carbon dynamics based on climatic change in the Central Black Sea Region through machine learning algorithms and future scenarios A. Çağlar et al.
127. Random forest algorithm and remote sensing techniques for wetland soil organic carbon prediction towards environmental sustainability A. Tobore et al.
128. High-Resolution Mapping of Soil Organic Carbon Stocks Using Machine and Deep Learning Approaches Across Mediterranean Land Uses M. Oukhattar et al.
129. Improving digital soil mapping in Bogor, Indonesia using parent material information D. Cahyana et al.
130. An innovative mapping framework for soil erodibility integrating spatial association dimensions and machine learning L. Wei et al.
131. Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline Z. Alvyar et al.
132. A high-resolution soil organic carbon mapping framework integrated machine learning and elevation-corrected climate data in alpine canyon region J. Dou et al.
133. Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France A. RICHER-de-FORGES et al.
134. Estimation of soil properties from the EU spectral library using long short-term memory networks S. Singh & S. Kasana
135. Multi-source data integration for soil mapping using deep learning A. Wadoux et al.
136. Game theory interpretation of digital soil mapping convolutional neural networks J. Padarian et al.
137. A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics W. Mendes et al.
138. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al.
139. Environmental controls and spatiotemporal patterns of bacterial diversity across China's desert ecosystems Y. Wen et al.
140. Quantitative estimation of soil properties using hybrid features and RNN variants S. Singh & S. Kasana
141. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al.
142. Evaluation of Open Geotechnical Knowledge in Urban Environments for 3D Modelling of the City of Seville (Spain) C. Soriano-Cuesta et al.
143. Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet A. Maxwell et al.
144. Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space R. Taghizadeh-Mehrjardi et al.
145. Density of soil observations in digital soil mapping: A study in the Mayenne region, France T. Loiseau et al.
146. Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study X. Sun et al.
147. AI-generated content in landscape architecture: A survey Y. Xing et al.
148. Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society J. Rosas et al.
149. Uncertainty quantification for digital soil mapping: An overview A. ZHU et al.
150. Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al.
151. Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic E. Duarte et al.
152. Digital Soil Mapping of Soil Depth and Textural Classes Using Random Forest and CNN Algorithms for Theri Soils of Tamil Nadu, India M. Nivas Raj et al.
153. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al.
154. Potential of soil minerals to sequester soil organic carbon H. Rodríguez-Albarracín et al.
155. Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model X. Zhu et al.
156. Improving in-situ spectral estimation of wetland soil organic carbon by integrating multiple optimization strategies H. Li et al.
157. Optimizing soil texture spatial prediction in the Brazilian Cerrado: Insights from random forest and spectral data M. Pelegrino et al.
158. Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning P. Fu et al.
159. Assessment of different digital soil mapping methods for prediction of soil classes in the Shahrekord plain, Central Iran I. Esfandiarpour-Boroujeni et al.
160. Digital mapping of soil chemical properties with limited data in the Thung Kula Ronghai region, Thailand S. Srisomkiew et al.
161. Semantic Segmentation of Portuguese Agri-Forestry Using High-Resolution Orthophotos T. Morais et al.
162. The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution D. Rukhovich et al.
163. Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain N. Rengma et al.
164. An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data A. Auzzas et al.
165. Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon M. Kılıç et al.
166. Predicting surface soil pH spatial distribution based on three machine learning methods: a case study of Heilongjiang Province P. Huang et al.
167. A two-stage algorithm for regional-scale SOC prediction: Eliminating the spatial scale effect between multi-source remote sensing data Y. Bao et al.
168. A deep learning architecture and workflow for geomorphic feature extraction using digital terrain model‐derived land surface parameters A. Maxwell et al.
169. Unveiling the explanatory power of environmental variables in soil organic carbon mapping: A global–local analysis framework Y. Wei et al.
170. Balancing phosphorus fertilization for sustainable maize yield and soil test phosphorus management: A long-term study using machine learning . Jagdeep-Singh et al.
171. Integrating transformer-based learning and Sentinel-2 bare soil composites for soil organic carbon mapping in the black soil region of Northeast China N. Chen et al.
172. Increment-averaged kriging: a comparison with depth-harmonized mapping of soil exchangeable sodium percentage in a cropping region of eastern Australia Y. Lai et al.
173. Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al.
174. Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data C. Smith et al.
175. Multivariate random forest for digital soil mapping S. van der Westhuizen et al.
176. Mid-infrared spectroscopy for soil organic carbon estimation. Part II: Evaluating preprocessing at global and national scales T. Li et al.
177. Machine learning techniques for estimating seismic site amplification in the Santiago basin, Chile J. Díaz et al.
178. Soil organic carbon prediction with terrain derivatives using geostatistics and sequential Gaussian simulation K. John et al.
179. Payments by modelled results: A novel design for agri-environmental schemes B. Bartkowski et al.
180. Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data A. Maxwell et al.
181. Teleconnections in spatial modelling T. Behrens et al.
182. 3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase H. Gerke et al.
183. Spatio-temporal mapping of soil water storage in a semi-arid landscape of northern Ghana – A multi-tasked ensemble machine-learning approach K. Nketia et al.
184. Increment-averaged kriging for 3-D modelling and mapping soil properties: Combining machine learning and geostatistical methods T. Orton et al.
185. Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale F. Stumpf et al.
186. Machine learning ensemble technique for exploring soil type evolution X. Wu et al.
187. Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area S. Yamaç et al.
188. Mapping topsoil pH using different predictive models and covariate sets in Henan Province, Central China L. Zhang et al.
189. Modeling soil landscapes and soil textures using hyperscale terrain attributes S. Riza et al.
190. Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques M. Jeihouni et al.
191. Digital soil mapping and GlobalSoilMap. Main advances and ways forward D. Arrouays et al.
192. Identifying soil provenance based on portable X-ray fluorescence measurements using similarity and inverse-mapping approaches – A case in the Lower Hunter Valley, Australia Y. Ma et al.
193. Targeting degraded soils for restoration: mapping surface hardpan soils in Niger with satellite imagery A. Hunakunti et al.
194. Estimating soil bacterial abundance and diversity in the Southeast Qinghai-Tibet Plateau Y. Yang et al.
195. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al.
196. On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping J. Wang et al.
197. Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al.
198. The roles of sampling depth, lateral profile density and vertical sampling density in 3D digital soil mapping of soil organic carbon M. Zhang et al.
199. Reconstructing soil acidity neutralization curves using Machine learning and chemical or spectral soil signatures H. Jouichat & L. Khiari
200. Artificial neural network and spatial distribution of soil attributes for farming management in Western Desert, Egypt K. Darwish et al.
201. Predictive performance of mid-infrared FTIR spectroscopy and machine learning models for soil property estimation in Japan J. He et al.
202. Multi-Modal Vision Transformer for high-resolution soil texture prediction of German agricultural soils using remote sensing imagery L. Wittstruck et al.
203. Enhancement and analysis of hyperspectral satellite images for Soil Study and Behavior V. Malik et al.
204. The Evolution of (Digital) Pedology – drawing from the past, adapting to the future A. McBratney et al.
205. Deep Carbon: A Multiscale Feature‐Time Fusion Approach for Field Level Digital Soil Organic Carbon Mapping A. Das et al.
206. Land-use controls on soil organic carbon dynamics across Amazonian ecosystems, Brazil G. Tiruneh et al.
207. Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model B. Hu et al.
208. A review of spectral feature extraction and multi-feature fusion methods in predicting soil organic carbon X. Li et al.
209. Digital soil mapping in the era of big data and artificial Intelligence L. Yang & X. Li