10 citations as recorded by crossref.

1. Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone S. Azamat et al. 10.1016/j.geodrs.2024.e00855
2. 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
3. Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach A. Suleymanov et al. 10.1134/S1064229324602014
4. Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach J. Rohmer et al. 10.5194/soil-10-679-2024
5. Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) H. Li et al. 10.1016/j.scitotenv.2024.170330
6. Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
7. Impact of Updating Vegetation Information on Land Surface Model Performance M. Ruiz‐Vásquez et al. 10.1029/2023JD039076
8. Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits J. Rukaitė et al. 10.1016/j.jafr.2024.101434
9. Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica R. Siqueira et al. 10.1016/j.catena.2023.107677
10. Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands A. Helfenstein et al. 10.1038/s43247-024-01293-y