184 citations as recorded by crossref.

1. Prediction of Soil Organic Carbon Content Using Sentinel-1/2 and Machine Learning Algorithms in Swamp Wetlands in Northeast China H. Zhang et al. 10.1109/JSTARS.2023.3281732
2. Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil R. Poppiel et al. 10.3390/rs12071197
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. 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
5. Advances and applications of machine learning and deep learning in environmental ecology and health S. Cui et al. 10.1016/j.envpol.2023.122358
6. Investigating the spatio-temporal variability of soil organic carbon stocks in different ecosystems of China S. Wang et al. 10.1016/j.scitotenv.2020.143644
7. CPT Data Interpretation Employing Different Machine Learning Techniques S. Rauter & F. Tschuchnigg 10.3390/geosciences11070265
8. Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features L. Parent & G. Deslauriers 10.3390/nitrogen4040024
9. Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications D. Chakraborty et al. 10.1016/j.eswa.2022.119056
10. 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
11. Soil carbon prediction under different land uses by integration of remote sensing and machine learning algorithms in a semi-arid watershed, Iran M. Tahmoures et al. 10.1007/s12517-023-11188-5
12. Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security E. McLennon et al. 10.1002/agj2.20814
13. Assessing the geographic specificity of pH prediction by classification and regression trees J. Egelberg et al. 10.1371/journal.pone.0255119
14. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
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. 10.1016/j.geodrs.2021.e00468
16. Machine learning facilitates connections between soil thermal conductivity, soil water content, and soil matric potential X. Wang et al. 10.1016/j.jhydrol.2024.130950
17. Spatial prediction of soil contamination based on machine learning: a review Y. Zhang et al. 10.1007/s11783-023-1693-1
18. Free iron oxide content in tropical soils predicted by integrative digital mapping W. Mendes et al. 10.1016/j.still.2022.105346
19. 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
20. Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method F. Meng et al. 10.1016/j.jenvman.2023.118817
21. 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
22. Carbon and Phosphorus in Volcanic Soils of Mt. Kerinci (Sumatra) After Long Term Tea and Paddy Cultivation D. Fiantis et al. 10.1080/00103624.2023.2253843
23. Quantification of non-structural carbohydrates in different tissues of trees with diffuse reflectance spectroscopy: Is there a benefit from a fusion of near- and mid-infrared data? M. Vohland et al. 10.1016/j.talanta.2023.125406
24. A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives Y. Fan et al. 10.1021/acs.est.2c03562
25. Accuracy and uncertainty of geostatistical models versus machine learning for digital mapping of soil calcium and potassium A. Sharififar 10.1007/s10661-022-10434-9
26. Using Machine Learning to make nanomaterials sustainable J. Scott-Fordsmand & M. Amorim 10.1016/j.scitotenv.2022.160303
27. Global predictions of topsoil organic carbon stocks under changing climate in the 21st century B. Chen et al. 10.1016/j.scitotenv.2023.168448
28. Necromass-derived soil organic carbon and its drivers at the global scale Y. Cao et al. 10.1016/j.soilbio.2023.109025
29. Online characterization of solid biofuels via pocket sized and low-cost near-infrared devices during small-scale boiler operation: Model development, evaluation and application tests S. Semella et al. 10.1016/j.biombioe.2023.106978
30. Mapping Brazilian soil mineralogy using proximal and remote sensing data N. Rosin et al. 10.1016/j.geoderma.2023.116413
31. Tolerance of cultivars and clonal selections of peach rootstocks to excess aluminum M. Aguilar et al. 10.5433/1679-0359.2023v44n3p1127
32. Instance-based transfer learning for soil organic carbon estimation P. Bursać et al. 10.3389/fenvs.2022.1003918
33. Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method J. Trontelj ml. & O. Chambers 10.3390/s21124208
34. Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau W. Deng et al. 10.3390/agronomy14040703
35. Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion H. Yang et al. 10.1109/JSTARS.2023.3267102
36. Detecting accounting fraud in family firms: Evidence from machine learning approaches M. Rahman & H. Zhu 10.1016/j.adiac.2023.100722
37. Trends in Soil Science over the Past Three Decades (1992–2022) Based on the Scientometric Analysis of 39 Soil Science Journals L. Jia et al. 10.3390/agriculture14030445
38. 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
39. Estimating soil–water characteristic curve (SWCC) using machine learning and soil micro-porosity analysis A. Bakhshi et al. 10.1007/s12145-023-01131-3
40. Soil organic carbon content increase in the east and south of China is accompanied by soil acidification X. Sun et al. 10.1016/j.scitotenv.2022.159253
41. Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations J. Biney et al. 10.1016/j.still.2022.105379
42. Estimating forest floor carbon stocks in woodland formations in Spain E. López-Senespleda et al. 10.1016/j.scitotenv.2021.147734
43. Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images T. Zhou et al. 10.1016/j.scitotenv.2020.142661
44. Österreichweite Regionalisierung bodenhydraulischer Eigenschaften H. Zeitfogel et al. 10.1007/s00506-022-00842-z
45. Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review O. Folorunso et al. 10.3390/bdcc7020113
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. Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China B. He et al. 10.3390/su151813996
48. Digital soil mapping: a predictive performance assessment of spatial linear regression, Bayesian and ML-based models A. Matazi et al. 10.1007/s40808-023-01788-1
49. The possibility of spatial mapping of soil organic carbon content at three depths using easy-to-obtain ancillary data in a Mediterranean area F. Blanco Velázquez et al. 10.12688/openreseurope.14716.1
50. Spatial variability of soil Cd content based on IDW and RBF in Fujiang River, Mianyang, China J. Sheng et al. 10.1007/s11368-020-02758-1
51. Pedotransfer Functions for Field Capacity, Permanent Wilting Point, and Available Water Capacity Based on Random Forest Models for Routine Soil Health Analysis J. Amsili et al. 10.1080/00103624.2024.2336573
52. Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research M. Sharma et al. 10.3390/su13158600
53. Predicting phosphorus sorption isotherm parameters in soil using data of routine laboratory tests K. DUNNE et al. 10.1016/S1002-0160(21)60012-7
54. Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales J. Song et al. 10.1016/j.jia.2023.09.017
55. Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114793
56. Method for dataset preparation for soil data analysis in decision support applications S. Motia & S. Reddy 10.1088/1757-899X/1022/1/012104
57. Contamination vertical distribution and key factors identification of metal(loid)s in site soil from an abandoned Pb/Zn smelter using machine learning Z. Guo et al. 10.1016/j.scitotenv.2022.159264
58. Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining D. Li et al. 10.1007/s10653-024-01928-1
59. Soil organic carbon mapping utilizing convolutional neural networks and Earth observation data, a case study in Bavaria state Germany N. Tziolas et al. 10.1016/j.geoderma.2024.116867
60. A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity A. Ghaderi et al. 10.1016/j.catena.2022.106289
61. Mapping peat thickness and carbon stock of a degraded peatland in West Sumatra, Indonesia D. Fiantis et al. 10.1111/sum.12954
62. Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin 10.1016/j.ejrs.2023.07.007
63. Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis L. Wang et al. 10.1021/acs.analchem.3c05311
64. Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms A. Sakhaee et al. 10.5194/soil-8-587-2022
65. Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática M. Vargas-Zapata et al. 10.19053/20278306.v12.n1.2022.14212
66. Exploration of machine learning methods for prediction and assessment of soil properties for agricultural soil management: a quantitative evaluation S. Motia & S. Reddy 10.1088/1742-6596/1950/1/012037
67. Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms V. de Oliveira et al. 10.1111/ejss.13123
68. Ensemble classifier to support decisions on soil classification S. Motia & S. Reddy 10.1088/1757-899X/1022/1/012044
69. Assessing a VisNIR penetrometer system for in-situ estimation of soil organic carbon under variable soil moisture conditions M. Murad et al. 10.1016/j.biosystemseng.2022.10.011
70. On-the-Go Vis-NIR Spectroscopy for Field-Scale Spatial-Temporal Monitoring of Soil Organic Carbon J. Reyes & M. Ließ 10.3390/agriculture13081611
71. Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods L. Hahn et al. 10.1038/s41598-024-55647-9
72. Crop Prediction Model Using Machine Learning Algorithms E. Elbasi et al. 10.3390/app13169288
73. Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data M. Pezij et al. 10.1016/j.envsoft.2020.104756
74. Perspectives on validation in digital soil mapping of continuous attributes—A review K. Piikki et al. 10.1111/sum.12694
75. Digital Mapping of Topsoil Texture Classes Using a Hybridized Classical Statistics–Artificial Neural Networks Approach and Relief Data S. Mallah et al. 10.3390/agriengineering5010004
76. Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout J. Padarian et al. 10.1016/j.geoderma.2022.116063
77. Identifying electrical conductivity in topsoil by interpretable machine learning B. Tutmez 10.1007/s40808-023-01878-0
78. Modelling and mapping soil organic carbon stocks under future climate change in south-eastern Australia B. Wang et al. 10.1016/j.geoderma.2021.115442
79. Using autoencoders to compress soil VNIR–SWIR spectra for more robust prediction of soil properties E. Tsimpouris et al. 10.1016/j.geoderma.2021.114967
80. Optimized data-driven pipeline for digital mapping of quantitative and categorical properties of soils in Colombia A. Coca-Castro et al. 10.36783/18069657rbcs20210084
81. Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets H. Zayani et al. 10.3390/rs15174264
82. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication J. Demattê et al. 10.3390/rs14030740
83. A new model for intra- and inter-institutional soil data sharing J. Padarian & A. McBratney 10.5194/soil-6-89-2020
84. Modeling soil landscapes and soil textures using hyperscale terrain attributes S. Riza et al. 10.1016/j.geoderma.2021.115177
85. 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
86. Pedotransfer functions and machine learning: Advancements and challenges in tropical soils L. de Castro Moreira da Silva et al. 10.1016/j.geodrs.2023.e00720
87. A Comprehensive Insight of Current and Future Challenges in Large-Scale Soil Microbiome Analyses J. Legeay & M. Hijri 10.1007/s00248-022-02060-2
88. Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements R. Suleymanov et al. 10.3390/app13095249
89. Effect of measurement error in wet chemistry soil data on the calibration and model performance of pedotransfer functions C. van Leeuwen et al. 10.1016/j.geoderma.2023.116762
90. Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region O. Abakay et al. 10.1007/s10661-024-12431-6
91. Prognostic of Soil Nutrients and Soil Fertility Index Using Machine Learning Classifier Techniques . Swapna B. et al. 10.4018/IJeC.304034
92. Vis–NIR spectroscopy and machine learning methods to diagnose chemical properties in Colombian sugarcane soils D. Delgadillo-Duran et al. 10.1016/j.geodrs.2022.e00588
93. Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades S. Condran et al. 10.1109/ACCESS.2022.3188649
94. 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
95. Performance of in situ vs laboratory mid-infrared soil spectroscopy using local and regional calibration strategies I. Greenberg et al. 10.1016/j.geoderma.2021.115614
96. Kargo Firmalarının Hizmet Kalitesinin Metin Madenciliği İle İncelenmesi B. ÇULLU & A. OKURSOY 10.18037/ausbd.1205507
97. Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil M. Vicentini et al. 10.1007/s10661-023-11679-8
98. Comparative analysis of seven machine learning algorithms and five empirical models to estimate soil thermal conductivity T. Zhao et al. 10.1016/j.agrformet.2022.109080
99. Prediction of spontaneous imbibition in porous media using deep and ensemble learning techniques M. Mahdaviara et al. 10.1016/j.fuel.2022.125349
100. Exploring soil property spatial patterns in a small grazed catchment using machine learning J. Barrena-González et al. 10.1007/s12145-023-01125-1
101. Mapping the phosphorus sorption capacity of Danish soils in four depths with quantile regression forests and uncertainty propagation A. Møller et al. 10.1016/j.geoderma.2022.116316
102. Predicting long-term dynamics of soil salinity and sodicity on a global scale A. Hassani et al. 10.1073/pnas.2013771117
103. Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
104. Efficient seismic risk analysis of gravity dams via screening of intensity measures and simulated non-parametric fragility curves Z. Li et al. 10.1016/j.soildyn.2021.107040
105. Effect of long-term treated wastewater irrigation on soil sodium levels and table grapevines' health R. Simhayov et al. 10.1016/j.agwat.2022.108002
106. On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization I. Dunkl & M. Ließ 10.5194/soil-8-541-2022
107. Empirical evidence shows that air quality changes during COVID-19 pandemic lockdown in Jakarta, Indonesia are due to seasonal variation, not restricted movements A. Jakob et al. 10.1016/j.envres.2021.112391
108. Challenges in the Geo-Processing of Big Soil Spatial Data L. Liakos & P. Panagos 10.3390/land11122287
109. Earth Observation Data-Driven Cropland Soil Monitoring: A Review N. Tziolas et al. 10.3390/rs13214439
110. Global Soil Salinity Prediction by Open Soil Vis-NIR Spectral Library Y. Zhou et al. 10.3390/rs14215627
111. First soil organic carbon report of Paraguay A. Encina-Rojas et al. 10.1016/j.geodrs.2023.e00611
112. 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
113. Land quality evaluation for sustainable development goals: a structured review using bibliometric and social network analysis T. Pham et al. 10.1007/s10661-022-10226-1
114. Digital soil mapping: Predicting soil classes distribution in large areas based on existing soil maps from similar small areas T. Gonçalves et al. 10.1590/1413-7054202145007921
115. Improving digital soil mapping in Bogor, Indonesia using parent material information D. Cahyana et al. 10.1016/j.geodrs.2023.e00627
116. Including soil depth as a predictor variable increases prediction accuracy of SOC stocks J. Li et al. 10.1016/j.still.2024.106007
117. Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation A. Gebauer et al. 10.3389/fsoil.2021.770326
118. Creating a soil parent material map digitally using a combination of interpretation and statistical techniques H. Jang et al. 10.1071/SR20212
119. 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
120. Spatiotemporal prediction of rainfall erosivity by machine learning in southeastern Brazil C. de Souza et al. 10.1080/10106049.2022.2060318
121. Using pXRF and Vis-NIR for characterizing diagnostic horizons of fine-textured podzolic soils in subtropical forests Y. Huang et al. 10.1016/j.geoderma.2023.116582
122. Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery Y. Sun et al. 10.1016/j.jhazmat.2023.130722
123. 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
124. Changes in soil organic carbon stocks from 1980‐1990 and 2010‐2020 in the northwest arid zone of China Z. Zhang et al. 10.1002/ldr.4293
125. Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence A. Addas et al. 10.3390/su16010355
126. Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
127. 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
128. Game theory interpretation of digital soil mapping convolutional neural networks J. Padarian et al. 10.5194/soil-6-389-2020
129. 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
130. Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment Y. Garosi et al. 10.1080/01431161.2022.2147037
131. 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
132. Prediction of Soil Salinity/Sodicity and Salt-Affected Soil Classes from Soluble Salt Ions Using Machine Learning Algorithms D. Andrade Foronda & G. Colinet 10.3390/soilsystems7020047
133. Shear strength evaluation of solid waste incinerator ash-derived composites S. Kumar & D. Singh 10.1016/j.mtcomm.2023.107446
134. Enabling soil carbon farming: presentation of a robust, affordable, and scalable method for soil carbon stock assessment T. van der Voort et al. 10.1007/s13593-022-00856-7
135. 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
136. Estimating pasture aboveground biomass under an integrated crop-livestock system based on spectral and texture measures derived from UAV images R. Freitas et al. 10.1016/j.compag.2022.107122
137. Quantification of soil organic carbon at regional scale: Benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches M. Vohland et al. 10.1016/j.geoderma.2021.115426
138. The Scope for Using Proximal Soil Sensing by the Farmers of India S. Behera et al. 10.3390/su14148561
139. System Cognition and Analytic Technology of Cultivated Land Quality from a Data Perspective H. Tang et al. 10.3390/land12010237
140. Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland T. Broeg et al. 10.1016/j.geoderma.2024.116850
141. Potential of soil minerals to sequester soil organic carbon H. Rodríguez-Albarracín et al. 10.1016/j.geoderma.2023.116549
142. Application of machine learning models in groundwater quality assessment and prediction: progress and challenges Y. Huang et al. 10.1007/s11783-024-1789-2
143. GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables A. Seifi et al. 10.1007/s00500-021-06009-4
144. Choosing between prediction and explanation in geological engineering: lessons from psychology A. Mitelman et al. 10.1080/03080188.2023.2234216
145. Assessing toxic metal chromium in the soil in coal mining areas via proximal sensing: Prerequisites for land rehabilitation and sustainable development J. Wang et al. 10.1016/j.geoderma.2021.115399
146. Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data J. Jin et al. 10.1016/j.jag.2021.102458
147. QuadMap: Variable resolution maps to better represent spatial uncertainty J. Padarian & A. McBratney 10.1016/j.cageo.2023.105480
148. Multi-property digital soil mapping at 30-m spatial resolution down to 1 m using extreme gradient boosting tree model and environmental covariates F. Rahbar Alam Shirazi et al. 10.1016/j.rsase.2023.101123
149. Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning Q. Lu et al. 10.1016/j.scitotenv.2022.159171
150. Break barriers in soil data stewardship by rewarding data generators T. Gobezie & A. Biswas 10.1038/s43017-023-00439-4
151. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing S. Wang et al. 10.1016/j.rse.2022.112914
152. Exploring the inter-decadal variability of soil organic carbon in China Z. Zhang et al. 10.1016/j.catena.2023.107242
153. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
154. A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields G. Lei et al. 10.1016/j.agwat.2022.108115
155. A Review on the Possibilities and Challenges of Today’s Soil and Soil Surface Assessment Techniques in the Context of Process-Based Soil Erosion Models L. Epple et al. 10.3390/rs14102468
156. Global predictions of primary soil salinization under changing climate in the 21st century A. Hassani et al. 10.1038/s41467-021-26907-3
157. Salt Stress in Plants and Mitigation Approaches G. Ondrasek et al. 10.3390/plants11060717
158. Use of remote sensing data to predict soil organic carbon in some agricultural soils of Iran P. Abdoli et al. 10.1016/j.rsase.2023.100969
159. Balancing machine learning and artificial intelligence in soil science with human perspective and experience D. WEINDORF & S. CHAKRABORTY 10.1016/j.pedsph.2023.09.010
160. Trustworthy Predictive Algorithms for Complex Forest System Decision-Making P. Rana & L. Varshney 10.3389/ffgc.2020.587178
161. Pedotransfer functions for estimating soil hydraulic properties from saturation to dryness . Rudiyanto et al. 10.1016/j.geoderma.2021.115194
162. Crop plot segmentation based on soil surface temperature dynamics D. Basurto-Lozada et al. 10.1016/j.rsase.2022.100871
163. Stubble Burning Effect On Soil’s Dielectric Behavior: An Exploration Of Machine Learning-Based Modelling Approaches P. Palta & P. Kaur 10.1080/15320383.2023.2249993
164. 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
165. The possibility of spatial mapping of SOC content in olive groves under integrated production using easy-to-obtain ancillary data in a Mediterranean area F. Blanco Velázquez et al. 10.12688/openreseurope.14716.2
166. Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area H. Bellinaso et al. 10.1016/j.geoderma.2021.115116
167. Evaluating the accuracy and usefulness of commercially-available proximal soil mapping services for grassland nutrient management planning and soil health monitoring J. Rhymes et al. 10.1007/s11119-022-09979-z
168. Modelling the Whole Profile Soil Organic Carbon Dynamics Considering Soil Redistribution under Future Climate Change and Landscape Projections over the Lower Hunter Valley, Australia Y. Ma et al. 10.3390/land12010255
169. Component identification for the SERS spectra of microplastics mixture with convolutional neural network Y. Luo et al. 10.1016/j.scitotenv.2023.165138
170. Simultaneous estimation of multiple soil properties under moist conditions using fractional-order derivative of vis-NIR spectra and deep learning Y. Liu et al. 10.1016/j.geoderma.2023.116653
171. Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
172. A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction M. Bordoni et al. 10.3390/w13091208
173. Origin of carbon in agricultural soil profiles deduced from depth gradients of C:N ratios, carbon fractions, δ13C and δ15N values F. Schneider et al. 10.1007/s11104-020-04769-w
174. Responses of soil organic carbon to climate change in the Qilian Mountains and its future projection H. Li et al. 10.1016/j.jhydrol.2021.126110
175. Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa Z. Venter et al. 10.1016/j.scitotenv.2021.145384
176. Potential of natural language processing for metadata extraction from environmental scientific publications G. Blanchy et al. 10.5194/soil-9-155-2023
177. Mapping rill soil erosion in agricultural fields with UAV‐borne remote sensing data R. Malinowski et al. 10.1002/esp.5505
178. Assessing the Complex Links Between Soils and Human Health: An Area of Pressing Need E. Brevik et al. 10.3389/fsoil.2021.731085
179. Patterns, Dynamics, and Drivers of Soil Available Nitrogen and Phosphorus in Alpine Grasslands across the QingZang Plateau Y. He et al. 10.3390/rs14194929
180. Methods and approaches to advance soil macroecology H. White et al. 10.1111/geb.13156
181. The power of integrating proximal and high-resolution remote sensing for mapping SOC stocks in agricultural peatlands W. de Sousa Mendes et al. 10.1007/s11104-023-06198-x
182. Vis-NIR Spectroscopy and Machine Learning Methods to Diagnose Chemical Properties in Colombian Sugarcane Soils D. Delgadillo-Duran et al. 10.2139/ssrn.4135425
183. Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil R. Poppiel et al. 10.3390/rs11242905
184. Depth-to-bedrock map of China at a spatial resolution of 100 meters F. Yan et al. 10.1038/s41597-019-0345-6