153 citations as recorded by crossref.

1. 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. 10.3390/app11020714
2. Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces Y. Wang et al. 10.3390/agronomy14050938
3. Monitoring regional soil organic matter content using a spatiotemporal model with time-series synthetic Landsat images M. Zhang et al. 10.1016/j.geodrs.2023.e00702
4. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks X. Ge et al. 10.1016/j.jag.2022.102969
5. Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors Y. Yan et al. 10.3390/rs14133020
6. A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables L. Zhang et al. 10.3390/rs14184441
7. Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density M. Zhang et al. 10.1016/j.geoderma.2023.116446
8. 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. 10.1007/s10653-020-00742-9
9. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes P. Descombes et al. 10.1111/ecog.05117
10. A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information J. Wang et al. 10.1016/j.scitotenv.2023.166112
11. Soil Science-Informed Machine Learning B. Minasny et al. 10.1016/j.geoderma.2024.117094
12. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
13. Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging Z. Zhu et al. 10.1016/j.envpol.2023.122548
14. A modified radial basis function network integrating Mahalanobis distance and LASSO for soil mapping C. Wang et al. 10.1016/j.ecoinf.2023.102279
15. Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms P. Zeng et al. 10.3390/ijgi11050299
16. Automated near-real-time mapping and monitoring of rice growth extent and stages in Selangor Malaysia . Fatchurrachman et al. 10.1016/j.rsase.2023.100993
17. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al. 10.3390/rs12142234
18. 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
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21. Digital mapping of the global soil δ15N at 0.1° × 0.1° resolution using random forest regression with climate classification Q. Zan et al. 10.1016/j.ecolind.2023.110974
22. 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. 10.1016/j.measurement.2022.111706
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24. 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. 10.1016/j.geoderma.2023.116652
25. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
26. Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. 10.1007/s11004-021-09988-0
27. 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. 10.1016/j.postharvbio.2023.112281
28. Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique S. Raghubanshi et al. 10.1007/s12518-023-00503-0
29. Soil inorganic carbon distribution, stocks and environmental thresholds along a major climatic gradient M. Pfeiffer et al. 10.1016/j.geoderma.2023.116449
30. Improving digital soil mapping in Bogor, Indonesia using parent material information D. Cahyana et al. 10.1016/j.geodrs.2023.e00627
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33. Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China L. Zhang et al. 10.3390/plants12071464
34. Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline Z. Alvyar et al. 10.1016/j.scitotenv.2021.152086
35. 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. 10.1016/j.pedsph.2022.07.009
36. Estimation of soil properties from the EU spectral library using long short-term memory networks S. Singh & S. Kasana 10.1016/j.geodrs.2019.e00233
37. Multi-source data integration for soil mapping using deep learning A. Wadoux et al. 10.5194/soil-5-107-2019
38. Game theory interpretation of digital soil mapping convolutional neural networks J. Padarian et al. 10.5194/soil-6-389-2020
39. A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics W. Mendes et al. 10.1016/j.envpol.2021.118397
40. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
41. Assessing human impacts on soil organic carbon change in the Lower Namoi Valley, Australia H. Jang et al. 10.1016/j.ancene.2023.100393
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44. 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
45. Evaluation of Open Geotechnical Knowledge in Urban Environments for 3D Modelling of the City of Seville (Spain) C. Soriano-Cuesta et al. 10.3390/rs16010141
46. 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. 10.3390/rs12071095
47. Density of soil observations in digital soil mapping: A study in the Mayenne region, France T. Loiseau et al. 10.1016/j.geodrs.2021.e00358
48. Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
49. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al. 10.1016/j.scitotenv.2023.169647
50. A reliable Bayesian neural network for the prediction of reservoir thickness with quantified uncertainty L. Bao et al. 10.1016/j.cageo.2023.105409
51. Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions S. Mousavi et al. 10.1016/j.catena.2023.107392
52. Towards near real-time national-scale soil water content monitoring using data fusion as a downscaling alternative I. Fuentes et al. 10.1016/j.jhydrol.2022.127705
53. Self-supervised learning of Vision Transformers for digital soil mapping using visual data P. Tresson et al. 10.1016/j.geoderma.2024.117056
54. A spatial assessment of mercury content in the European Union topsoil C. Ballabio et al. 10.1016/j.scitotenv.2020.144755
55. Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization R. Agarwal et al. 10.1109/ACCESS.2021.3135536
56. 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
57. Review on Crop Prediction Using Deep Learning Techniques M. Dharani et al. 10.1088/1742-6596/1767/1/012026
58. Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
59. Soil carbon sequestration potential in global croplands J. Padarian et al. 10.7717/peerj.13740
60. Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society J. Rosas et al. 10.1016/j.scitotenv.2024.173537
61. Simultaneously mapping the 3D distributions of multiple heavy metals in an industrial site using deep learning and multisource auxiliary data Y. Peng et al. 10.1016/j.jhazmat.2024.136000
62. Spatiotemporal modelling of soil organic matter changes in Jiangsu, China between 1980 and 2006 using INLA-SPDE X. Sun et al. 10.1016/j.geoderma.2020.114808
63. Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al. 10.1016/j.catena.2023.107440
64. Mapping soil profile depth, bulk density and carbon stock in Scotland using remote sensing and spatial covariates M. Aitkenhead & M. Coull 10.1111/ejss.12916
65. Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic E. Duarte et al. 10.1080/22797254.2022.2045226
66. 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. 10.3390/land9120487
67. 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
68. Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra A. Descals et al. 10.3390/rs11212590
69. ML-based technologies in sustainable agro-food production and beyond: Tapping the (semi) arid landscape for bioactives-based product development T. Joshi et al. 10.1016/j.jafr.2024.101350
70. Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al. 10.1007/s10661-021-09561-6
71. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
72. 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. 10.1016/j.still.2024.106317
73. Rate of soil organic carbon sequestration in a millennium coastal soil chronosequence in northern Jiangsu, China C. Jiao et al. 10.1016/j.catena.2020.104627
74. Potential of soil minerals to sequester soil organic carbon H. Rodríguez-Albarracín et al. 10.1016/j.geoderma.2023.116549
75. Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model X. Zhu et al. 10.3389/feart.2024.1455124
76. Geochemical and mineralogical composition of the 2018 volcanic deposits of Mt. Anak Krakatau D. Fiantis et al. 10.1016/j.geodrs.2021.e00393
77. Assessing the improvement potentials of climate model partitioning and time-variant feature extraction for soil organic carbon prediction Y. Bao et al. 10.1016/j.catena.2024.108014
78. 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. 10.1080/07038992.2022.2056435
79. Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning P. Fu et al. 10.3390/rs16122217
80. An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran M. Garajeh et al. 10.1016/j.scitotenv.2021.146253
81. Mapping of Soil Organic Carbon Stocks Based on Aerial Photography in a Fragmented Desertification Landscape J. Lian et al. 10.3390/rs14122829
82. Assessment of different digital soil mapping methods for prediction of soil classes in the Shahrekord plain, Central Iran I. Esfandiarpour-Boroujeni et al. 10.1016/j.catena.2020.104648
83. Digital mapping of soil chemical properties with limited data in the Thung Kula Ronghai region, Thailand S. Srisomkiew et al. 10.1016/j.geoderma.2021.114942
84. Semantic Segmentation of Portuguese Agri-Forestry Using High-Resolution Orthophotos T. Morais et al. 10.3390/agronomy13112741
85. 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. 10.3390/rs13010155
86. 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. 10.1093/forestry/cpac002
87. 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
88. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
89. 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
90. 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. 10.1371/journal.pone.0268658
91. Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India T. Tarun Kshatriya et al. 10.3390/agronomy14112707
92. Mapping Soil Organic Matter Content during the Bare Soil Period by Using Satellite Data and an Improved Deep Learning Network X. Xu & X. Zhai 10.3390/su15010323
93. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
94. Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al. 10.1021/acs.est.4c01172
95. Unveiling the explanatory power of environmental variables in soil organic carbon mapping: A global–local analysis framework Y. Wei et al. 10.1016/j.geoderma.2024.117011
96. Use of Topographic Models for Mapping Soil Properties and Processes X. Li et al. 10.3390/soilsystems4020032
97. 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. 10.1007/s10021-024-00928-7
98. Balancing phosphorus fertilization for sustainable maize yield and soil test phosphorus management: A long-term study using machine learning . Jagdeep-Singh et al. 10.1016/j.fcr.2023.109169
99. 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. 10.1016/j.geoderma.2019.114151
100. Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al. 10.1016/j.still.2022.105519
101. Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data C. Smith et al. 10.3390/rs12060928
102. Multivariate random forest for digital soil mapping S. van der Westhuizen et al. 10.1016/j.geoderma.2023.116365
103. 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. 10.1016/j.geoderma.2023.116769
104. Predicting wetland soil properties using machine learning, geophysics, and soil measurement data D. Driba et al. 10.1007/s11368-024-03801-1
105. Machine learning techniques for estimating seismic site amplification in the Santiago basin, Chile J. Díaz et al. 10.1016/j.enggeo.2022.106764
106. History and interpretation of early soil and organic matter investigations in Deli, Sumatra, Indonesia B. Minasny et al. 10.1016/j.catena.2020.104909
107. Soil organic carbon prediction with terrain derivatives using geostatistics and sequential Gaussian simulation K. John et al. 10.1016/j.jssas.2021.04.005
108. Payments by modelled results: A novel design for agri-environmental schemes B. Bartkowski et al. 10.1016/j.landusepol.2020.105230
109. Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data A. Maxwell et al. 10.1029/2023EA002845
110. Teleconnections in spatial modelling T. Behrens et al. 10.1016/j.geoderma.2019.07.012
111. Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al. 10.3390/agriculture14081298
112. 3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase H. Gerke et al. 10.1002/jpln.202200239
113. 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. 10.1016/j.geoderma.2021.115691
114. 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. 10.3390/rs14081875
115. Developing and testing of pedogenons in the lower Namoi valley, NSW, Australia H. Jang et al. 10.1016/j.geoderma.2022.116182
116. Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images D. Kim et al. 10.3390/app13052936
117. Increment-averaged kriging for 3-D modelling and mapping soil properties: Combining machine learning and geostatistical methods T. Orton et al. 10.1016/j.geoderma.2019.114094
118. Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale F. Stumpf et al. 10.3390/rs16152712
119. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
120. National-scale mapping of soil organic carbon stock in France: New insights and lessons learned by direct and indirect approaches Z. Chen et al. 10.1016/j.seh.2023.100049
121. 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. 10.1016/j.agwat.2020.106121
122. 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
123. A novel local-global dependency deep learning model for soil mapping Q. Li et al. 10.1016/j.geoderma.2023.116649
124. SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopy J. Martins et al. 10.1016/j.compag.2022.106945
125. Modeling soil landscapes and soil textures using hyperscale terrain attributes S. Riza et al. 10.1016/j.geoderma.2021.115177
126. A new methodology for establishing an SOC content prediction model that is spatiotemporally transferable at multidecadal and intercontinental scales X. Meng et al. 10.1016/j.isprsjprs.2024.09.038
127. Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques M. Jeihouni et al. 10.1016/j.jhydrol.2020.124786
128. 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
129. Digital soil mapping and GlobalSoilMap. Main advances and ways forward D. Arrouays et al. 10.1016/j.geodrs.2020.e00265
130. 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. 10.1016/j.geodrs.2021.e00368
131. 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. 10.1007/s40808-023-01723-4
132. 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. 10.3390/land11122279
133. Prediction of Soil Organic Carbon Content in Complex Vegetation Areas Based on CNN-LSTM Model Z. Dong et al. 10.3390/land13070915
134. Estimating soil bacterial abundance and diversity in the Southeast Qinghai-Tibet Plateau Y. Yang et al. 10.1016/j.geoderma.2022.115807
135. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
136. On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping J. Wang et al. 10.1016/j.geoderma.2024.117026
137. BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
138. 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
139. 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. 10.1016/j.rse.2022.113166
140. 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. 10.1016/j.geoderma.2024.116840
141. 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. 10.3390/rs12091389
142. Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran V. Habibi et al. 10.1007/s10661-020-08718-z
143. Artificial neural network and spatial distribution of soil attributes for farming management in Western Desert, Egypt K. Darwish et al. 10.1007/s12517-022-10565-w
144. Machine learning and soil sciences: a review aided by machine learning tools J. Padarian et al. 10.5194/soil-6-35-2020
145. Grand Challenges in Pedometrics-AI Research S. Grunwald 10.3389/fsoil.2021.714323
146. Enhancement and analysis of hyperspectral satellite images for Soil Study and Behavior V. Malik et al. 10.1007/s11042-023-16729-4
147. Assessing the carbon sequestration potential and identifying influential factors of cultivated soils in Northeast China S. Wang et al. 10.1016/j.geodrs.2023.e00655
148. 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. 10.1016/j.ecolind.2024.112246
149. The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review R. Wang & W. Su 10.3390/agriculture14081225
150. Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon M. Goydaragh et al. 10.1016/j.catena.2021.105280
151. A review of spectral feature extraction and multi-feature fusion methods in predicting soil organic carbon X. Li et al. 10.1080/05704928.2024.2369570
152. Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales Y. Xia et al. 10.3389/fsoil.2022.890437
153. 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