13 citations as recorded by crossref.

1. Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al. 10.3390/rs15040876
2. Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat S. Zepp et al. 10.1016/j.isprsjprs.2023.06.003
3. Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil A. Sarkar et al. 10.1016/j.ecoinf.2022.101959
4. 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. 10.1007/s10661-024-12640-z
5. Maximizing the carbon sink function of paddy systems in China with machine learning J. Wang et al. 10.1016/j.scitotenv.2023.168542
6. 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
7. 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
8. Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
9. A high-resolution map of soil organic carbon in cropland of Southern China B. Hu et al. 10.1016/j.catena.2024.107813
10. 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. 10.1016/j.geoderma.2024.116941
11. Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction N. Kakhani et al. 10.3390/rs16030438
12. Applying machine learning to model radon using topsoil geochemistry M. Banríon et al. 10.1016/j.apgeochem.2023.105790
13. Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus F. Kaya et al. 10.3390/agriculture12071062