75 citations as recorded by crossref.

1. 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
2. Soil texture prediction with automated deep convolutional neural networks and population-based learning O. Omondiagbe et al. 10.1016/j.geoderma.2023.116521
3. Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. 10.1111/ejss.13463
4. Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation J. Lu et al. 10.3390/rs13173502
5. Evaluation of projected soil organic carbon stocks under future climate and land cover changes in South Africa using a deep learning approach O. Odebiri et al. 10.1016/j.jenvman.2022.117127
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
7. Consequences of spatial structure in soil–geomorphic data on the results of machine learning models D. Kim et al. 10.1080/10106049.2023.2245381
8. A two-step soil modeling approach by integrating pedological classification in digital mapping with non-stationary geostatistics F. Abbaszadeh Afshar et al. 10.1080/15324982.2024.2317399
9. Proximal and remote sensor data fusion for 3D imaging of infertile and acidic soil J. Wang et al. 10.1016/j.geoderma.2022.115972
10. Prediction of soil organic carbon using machine learning techniques and geospatial data for sustainable agriculture S. Mundada et al. 10.3233/JIFS-240493
11. 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
12. Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping S. Chen et al. 10.1016/j.geoderma.2023.116467
13. Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks O. Odebiri et al. 10.1016/j.scitotenv.2022.161150
14. Evaluation of digital soil mapping projection in soil organic carbon change modeling T. Zhang et al. 10.1016/j.ecoinf.2023.102394
15. A generative deep neural network as an alternative to co-kriging H. Rakotonirina et al. 10.1016/j.acags.2024.100198
16. A Novel Self-Supervised Learning Method for Accurate Multigranularity Prediction of Soil Organic Carbon Z. Yu et al. 10.1109/TGRS.2024.3511118
17. A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics X. Zhang et al. 10.1016/j.ecoinf.2024.102923
18. 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
19. A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications Y. He et al. 10.1016/j.apgeochem.2022.105273
20. 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
21. 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
22. Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al. 10.1016/j.catena.2021.105702
23. 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
24. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
25. 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
26. 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
27. Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
28. Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model X. Zhang et al. 10.3390/rs16030565
29. Digital soil mapping based on the similarity of geographic environment over spatial neighborhoods F. Zhao et al. 10.1080/17538947.2025.2471507
30. Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms P. Zeng et al. 10.3390/ijgi11050299
31. Multivariate random forest for digital soil mapping S. van der Westhuizen et al. 10.1016/j.geoderma.2023.116365
32. Digital soil mapping and GlobalSoilMap. Main advances and ways forward D. Arrouays et al. 10.1016/j.geodrs.2020.e00265
33. Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India T. Tarun Kshatriya et al. 10.3390/agronomy14112707
34. 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
35. A novel local-global dependency deep learning model for soil mapping Q. Li et al. 10.1016/j.geoderma.2023.116649
36. 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
37. 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
38. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
39. 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
40. High-Resolution Digital Soil Maps of Forest Soil Nitrogen across South Korea Using Three Machine Learning Algorithms Y. An et al. 10.3390/f14061141
41. Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review O. Odebiri et al. 10.1016/j.jag.2021.102389
42. Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
43. 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
44. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
45. Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
46. 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
47. 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
48. Spatial statistics and soil mapping: A blossoming partnership under pressure G. Heuvelink & R. Webster 10.1016/j.spasta.2022.100639
49. Uncertainty of spatial averages and totals of natural resource maps A. Wadoux & G. Heuvelink 10.1111/2041-210X.14106
50. 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
51. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
52. Measurement error-filtered machine learning in digital soil mapping S. van der Westhuizen et al. 10.1016/j.spasta.2021.100572
53. Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model X. Zhu et al. 10.3389/feart.2024.1455124
54. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
55. Temporal adjustment approach for high-resolution continental scale modeling of soil organic carbon L. Bokati et al. 10.1038/s41598-025-89503-1
56. 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
57. 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
58. Sample design optimization for soil mapping using improved artificial neural networks and simulated annealing S. Shao et al. 10.1016/j.geoderma.2022.115749
59. Mapping Brazilian soil mineralogy using proximal and remote sensing data N. Rosin et al. 10.1016/j.geoderma.2023.116413
60. Conventional and Digital Soil Mapping in the Central Part of the Smolenskoe Poozer’e National Park A. Kornilova et al. 10.1134/S1064229324603494
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. Grand Challenges in Pedometrics-AI Research S. Grunwald 10.3389/fsoil.2021.714323
63. Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. 10.1007/s11004-021-09988-0
64. Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites M. Möller et al. 10.3390/rs14102295
65. 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
66. High resolution middle eastern soil attributes mapping via open data and cloud computing R. Poppiel et al. 10.1016/j.geoderma.2020.114890
67. Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
68. Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al. 10.3390/rs12142234
69. 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
70. 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
71. 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
72. Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning G. Feng et al. 10.1155/2022/3287117
73. 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
74. 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
75. Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359