86 citations as recorded by crossref.

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4. Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation J. Lu et al. 10.3390/rs13173502
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6. 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
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20. 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
21. A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications Y. He et al. 10.1016/j.apgeochem.2022.105273
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23. 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
24. 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
25. Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al. 10.1016/j.catena.2021.105702
26. Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy T. Li et al. 10.3390/rs15235571
27. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
28. Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil F. Mello et al. 10.1016/j.geodrs.2021.e00412
29. 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
30. Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
31. Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model X. Zhang et al. 10.3390/rs16030565
32. Digital soil mapping based on the similarity of geographic environment over spatial neighborhoods F. Zhao et al. 10.1080/17538947.2025.2471507
33. Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms P. Zeng et al. 10.3390/ijgi11050299
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38. Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India T. Tarun Kshatriya et al. 10.3390/agronomy14112707
39. 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
40. A novel local-global dependency deep learning model for soil mapping Q. Li et al. 10.1016/j.geoderma.2023.116649
41. 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
42. Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database M. Turek et al. 10.1016/j.iswcr.2022.08.001
43. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
44. A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest X. Xu et al. 10.1016/j.envres.2023.117570
45. High-Resolution Digital Soil Maps of Forest Soil Nitrogen across South Korea Using Three Machine Learning Algorithms Y. An et al. 10.3390/f14061141
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47. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
48. Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape M. Ließ & A. Sakhaee 10.3390/agriculture14081230
49. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
50. Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
51. 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
52. 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
53. Spatial statistics and soil mapping: A blossoming partnership under pressure G. Heuvelink & R. Webster 10.1016/j.spasta.2022.100639
54. Uncertainty of spatial averages and totals of natural resource maps A. Wadoux & G. Heuvelink 10.1111/2041-210X.14106
55. 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
56. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
57. Measurement error-filtered machine learning in digital soil mapping S. van der Westhuizen et al. 10.1016/j.spasta.2021.100572
58. Mapping soil fertility properties in central Ethiopia at 100 m spatial resolution M. Redi et al. 10.1016/j.geodrs.2025.e00952
59. Predictive performance of mid-infrared FTIR spectroscopy and machine learning models for soil property estimation in Japan J. He et al. 10.1080/00380768.2025.2558584
60. Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model X. Zhu et al. 10.3389/feart.2024.1455124
61. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
62. Temporal adjustment approach for high-resolution continental scale modeling of soil organic carbon L. Bokati et al. 10.1038/s41598-025-89503-1
63. Quantifying intra-field soil variability using categorical data: A case study of predicting soil organic matter using soil survey maps H. Etezadi et al. 10.1016/j.atech.2025.101503
64. Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron S. Chen et al. 10.1002/ldr.3497
65. 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
66. A two-stage algorithm for regional-scale SOC prediction: Eliminating the spatial scale effect between multi-source remote sensing data Y. Bao et al. 10.1016/j.still.2025.106552
67. Sample design optimization for soil mapping using improved artificial neural networks and simulated annealing S. Shao et al. 10.1016/j.geoderma.2022.115749
68. Mapping Brazilian soil mineralogy using proximal and remote sensing data N. Rosin et al. 10.1016/j.geoderma.2023.116413
69. Conventional and Digital Soil Mapping in the Central Part of the Smolenskoe Poozer’e National Park A. Kornilova et al. 10.1134/S1064229324603494
70. 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
71. Estimating Soil Attributes for Yield Gap Reduction in Africa Using Hyperspectral Remote Sensing Data with Artificial Intelligence Methods: An Extensive Review and Synthesis N. El Bouanani et al. 10.3390/rs17091597
72. Grand Challenges in Pedometrics-AI Research S. Grunwald 10.3389/fsoil.2021.714323
73. Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. 10.1007/s11004-021-09988-0
74. Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites M. Möller et al. 10.3390/rs14102295
75. 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. 10.1016/j.envsoft.2024.106124
76. High resolution middle eastern soil attributes mapping via open data and cloud computing R. Poppiel et al. 10.1016/j.geoderma.2020.114890
77. Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
78. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al. 10.3390/rs12142234
79. 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
80. Importance ranking of data and model uncertainties in quantile regression forest-based spatial predictions when data are sparse, imprecise and clustered J. Rohmer 10.1016/j.ecoinf.2025.103459
81. 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
82. 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
83. Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning G. Feng et al. 10.1155/2022/3287117
84. 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
85. 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
86. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359