136 citations as recorded by crossref.

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18. 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. 10.1016/j.geodrs.2021.e00468
19. 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
20. 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
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22. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison X. Zhu et al. 10.3390/su12052099
23. 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
24. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
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28. Soil inorganic carbon distribution, stocks and environmental thresholds along a major climatic gradient M. Pfeiffer et al. 10.1016/j.geoderma.2023.116449
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30. The Impact of COVID-19 Lockdown on People’s Mobility and Water Quality of Siak River, Indonesia S. Hasibuan et al. 10.2965/jwet.21-178
31. The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement M. Rybansky & J. Rada 10.3390/app12126178
32. Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China L. Zhang et al. 10.3390/plants12071464
33. 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
34. 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
35. 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
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38. 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
39. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
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41. 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. 10.3390/rs14092224
42. Free iron oxide content in tropical soils predicted by integrative digital mapping W. Mendes et al. 10.1016/j.still.2022.105346
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44. 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
45. 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
46. 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
47. Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
48. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al. 10.1016/j.scitotenv.2023.169647
49. A reliable Bayesian neural network for the prediction of reservoir thickness with quantified uncertainty L. Bao et al. 10.1016/j.cageo.2023.105409
50. 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
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52. A spatial assessment of mercury content in the European Union topsoil C. Ballabio et al. 10.1016/j.scitotenv.2020.144755
53. Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization R. Agarwal et al. 10.1109/ACCESS.2021.3135536
54. 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
55. Review on Crop Prediction Using Deep Learning Techniques M. Dharani et al. 10.1088/1742-6596/1767/1/012026
56. Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
57. Soil carbon sequestration potential in global croplands J. Padarian et al. 10.7717/peerj.13740
58. Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society J. Rosas et al. 10.1016/j.scitotenv.2024.173537
59. 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
60. 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
61. 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
62. Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic E. Duarte et al. 10.1080/22797254.2022.2045226
63. 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
64. 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
65. Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra A. Descals et al. 10.3390/rs11212590
66. 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
67. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
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70. Geochemical and mineralogical composition of the 2018 volcanic deposits of Mt. Anak Krakatau D. Fiantis et al. 10.1016/j.geodrs.2021.e00393
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72. 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
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75. Mapping of Soil Organic Carbon Stocks Based on Aerial Photography in a Fragmented Desertification Landscape J. Lian et al. 10.3390/rs14122829
76. 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
77. 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
78. Semantic Segmentation of Portuguese Agri-Forestry Using High-Resolution Orthophotos T. Morais et al. 10.3390/agronomy13112741
79. 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
80. 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
81. 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
82. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
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84. 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
85. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
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87. Use of Topographic Models for Mapping Soil Properties and Processes X. Li et al. 10.3390/soilsystems4020032
88. 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
89. 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
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103. 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
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