13 citations as recorded by crossref.

1. Spatial predictive modeling of soil organic carbon stocks in Norwegian forests A. Hagenbo et al. 10.1016/j.scitotenv.2025.179451
2. Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods M. Lu et al. 10.1016/j.compag.2022.107246
3. Comparison of Soil Water Content from SCATSAR-SWI and Cosmic Ray Neutron Sensing at Four Agricultural Sites in Northern Italy: Insights from Spatial Variability and Representativeness S. Emamalizadeh et al. 10.3390/rs16183384
4. Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape L. Borůvka et al. 10.17221/4/2022-SWR
5. Carbon pool dynamics after variable retention harvesting in Nothofagus pumilio forests of Tierra del Fuego J. Chaves et al. 10.1186/s13717-023-00418-z
6. Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions A. Ågren et al. 10.5194/soil-8-733-2022
7. Improving spatial prediction of soil organic matter in central Vietnam using Bayesian-enhanced machine learning and environmental covariates N. Ngu et al. 10.1080/03650340.2024.2448623
8. Carbon, nitrogen, and phosphorus stoichiometry of organic matter in Swedish forest soils and its relationship with climate, tree species, and soil texture M. Spohn & J. Stendahl 10.5194/bg-19-2171-2022
9. Decadal Changes of Organic Carbon, Nitrogen, and Acidity of Austrian Forest Soils R. Jandl et al. 10.3390/soilsystems6010028
10. Analysis of the Effect of Moisture Content on the Spatial Variability of Carbon Stock in Forest Soils of European Russia I. Ryzhova et al. 10.3103/S0147687422020065
11. Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape J. Larson et al. 10.1038/s41598-023-42091-4
12. Predicting soil carbon stock in remote areas of the Central Amazon region using machine learning techniques A. Ferreira et al. 10.1016/j.geodrs.2023.e00614
13. Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning P. Fu et al. 10.3390/rs16122217