46 citations as recorded by crossref.

1. Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al.
2. Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat S. Zepp et al.
3. Soil organic carbon (SOC) prediction using super learner algorithm based on the remote sensing variables Y. Jo et al.
4. Navigating predictions at nanoscale: a comprehensive study of regression models in magnetic nanoparticle synthesis L. Glänzer et al.
5. Enhanced Spatially Explicit Modeling of Soil Particle Size and Texture Classification Using a Novel Two-Point Machine Learning Hybrid Framework L. Qin et al.
6. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ
7. Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data D. Triantakonstantis & A. Karakostas
8. Digital mapping of soil inorganic carbon content and density in soil profiles after ‘Grain for Green’ program L. Ye et al.
9. Soil Property, Carbon Stock and Peat Extent Mapping at 10 m Resolution in Scotland Using Digital Soil Mapping Techniques C. Robb et al.
10. Estimation of soil organic matter in mollisols based on artificial intelligence S. Cui et al.
11. TLM-stack: A deep learning-based novel framework for soil nutrients estimation using hyperspectral data S. Jain et al.
12. SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction N. Kakhani et al.
13. Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al.
14. Multi-frequency SAR and optical data integration for continental-scale digital mapping of soil chemical properties across Europe T. Zhou et al.
15. Towards Explainable AI: Interpreting Soil Organic Carbon Prediction Models Using a Learning‐Based Explanation Method N. Kakhani et al.
16. Surface soil organic carbon losses in Dongting Lake floodplain as evidenced by field observations from 2013 to 2022 L. Wang et al.
17. Soil penetration resistance prediction based on a comparative evaluation of individual and ensemble machine learning under varying tillage, fertilization and liming treatments D. Jug et al.
18. Employing Google Earth Engine and Machine Learning Algorithms for Soil Organic Carbon Mapping: A Comparison Study of Sentinel-2A and MODIS Images in Croplands A. Adeel & A. Jadhav
19. Land use and soil property effects on aggregate stability assessed by three different slaking methods C. Poeplau et al.
20. Satellite Soil Observation (SatSoil): extraction of bare soil reflectance for soil organic carbon mapping on Google Earth Engine M. Khazaei et al.
21. Influence of Field Survey Principles on the Quality of Approximation of the Relationship between the Soil Organic Matter Content and the Multitemporal Spectral Characteristics D. Rukhovich et al.
22. 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
23. Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation A. Abdelbaki et al.
24. Supporting the spatial allocation of management practices to improve ecosystem services – An opportunity map approach for agricultural landscapes I. Heiß et al.
25. Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin
26. Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland T. Broeg et al.
27. A detailed mapping of soil organic matter content in arable land based on the multitemporal soil line coefficients and neural network filtering of big remote sensing data D. Rukhovich et al.
28. The older, the better: a comprehensive survey of soil organic carbon under commercial oil palm plantations K. Golicz et al.
29. Impacts of spatial scales and data distribution patterns on geographically weighted machine learning models for the spatial prediction of soil organic carbon Y. LEE et al.
30. Machine learning ensemble technique for exploring soil type evolution X. Wu et al.
31. Multitemporal Spectral Characteristics of the Open Soil Surface and Mapping of the Organic Matter Content in Plow Horizons P. Koroleva et al.
32. Prediction of Water Infiltration of Three Types of Soil with Machine Learning in the Sahuayo River Basin M. Lupián-Machuca et al.
33. Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil A. Sarkar et al.
34. A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany A. Säurich et al.
35. Maximizing the carbon sink function of paddy systems in China with machine learning J. Wang et al.
36. Modeling the Impact of Climate Change on Soil Health Using Predictive Analytics Z. Alsalami et al.
37. Enhancing digital mapping of soil organic carbon through spatial modeling and validation A. Jafari et al.
38. A high-resolution map of soil organic carbon in cropland of Southern China B. Hu et al.
39. Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions X. Ji et al.
40. Spatiotemporal dynamics and driving factors of soil organic carbon storage in the Yangtze River Basin under climate change and land use scenarios Y. Liu et al.
41. The use of machine learning models in solving problems in the field of organic agriculture A. Linkina et al.
42. Randomness in Data Partitioning and Its Impact on Digital Soil Mapping Accuracy: A Comparison of Cross-Validation and Split-Sample Approaches D. Radočaj et al.
43. Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction N. Kakhani et al.
44. A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia C. Ojeda Riaños et al.
45. Decrypting spatiotemporal evolution and influencing factors of cultivated land use carbon compensation in the middle reaches of Yangtze River: An interpretable machine learning approach T. Lv et al.
46. Applying machine learning to model radon using topsoil geochemistry M. Banríon et al.