45 citations as recorded by crossref.

1. Mapping soil organic carbon on a national scale: Towards an improved and updated map of Madagascar N. Ramifehiarivo et al. https://doi.org/10.1016/j.geodrs.2016.12.002
2. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. https://doi.org/10.1016/j.geoderma.2019.05.031
3. Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region A. García-Tomillo et al. https://doi.org/10.1007/s11119-016-9481-6
4. Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks L. Lombardo et al. https://doi.org/10.1016/j.geoderma.2017.12.011
5. GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth V. Mulder et al. https://doi.org/10.1016/j.scitotenv.2016.07.066
6. How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery P. Lagacherie et al. https://doi.org/10.1016/j.geoderma.2018.08.024
7. No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America M. Guevara et al. https://doi.org/10.5194/soil-4-173-2018
8. Robust soil mapping at the farm scale with vis–NIR spectroscopy L. Ramirez‐Lopez et al. https://doi.org/10.1111/ejss.12752
9. Hydrodynamic, Seasonal, and Climatic Factors Affecting the Terrestrial-to-Aquatic Export of Plant-Derived Biomarkers M. Schwab et al. https://doi.org/10.1016/j.gca.2024.12.009
10. Deconvolving the Fate of Carbon in Coastal Sediments T. Van der Voort et al. https://doi.org/10.1029/2018GL077009
11. Evidence for Soil Phosphorus Resource Partitioning in a Diverse Tropical Tree Community R. Müller et al. https://doi.org/10.3390/f15020361
12. Comparison of geostatistics, machine learning algorithms, and their hybrid approaches for modeling soil organic carbon density in tropical forests V. Ho et al. https://doi.org/10.1007/s11368-025-04027-5
13. Improved Kriging-based Spatial Interpolation Technique for Mapping Soil Organic Carbon using Categorical Auxiliary Information in Forest Ecosystems V. Ho et al. https://doi.org/10.1007/s42729-026-03163-2
14. Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation O. Odebiri et al. https://doi.org/10.1016/j.geodrs.2024.e00817
15. Validation of uncertainty predictions in digital soil mapping J. Schmidinger & G. Heuvelink https://doi.org/10.1016/j.geoderma.2023.116585
16. 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. https://doi.org/10.1016/j.scitotenv.2020.142661
17. Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al. https://doi.org/10.1016/j.geodrs.2021.e00437
18. Environmental and hydrologic controls on sediment and organic carbon export from a subalpine catchment: insights from a time series M. Schwab et al. https://doi.org/10.5194/bg-19-5591-2022
19. Spatial aggregation of soil property predictions in support of local land management K. Vaysse et al. https://doi.org/10.1111/sum.12350
20. Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes O. Odebiri et al. https://doi.org/10.1016/j.catena.2024.108216
21. Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach L. YANG et al. https://doi.org/10.1016/S1002-0160(17)60322-9
22. Evaluation of digital soil mapping approaches with large sets of environmental covariates M. Nussbaum et al. https://doi.org/10.5194/soil-4-1-2018
23. Spatial 3D mapping of forest soil carbon stocks in Hesse, Germany F. Heitkamp et al. https://doi.org/10.1002/jpln.202100138
24. Radiocarbon signatures of carbon phases exported by Swiss rivers in the Anthropocene T. Rhyner et al. https://doi.org/10.1098/rsta.2022.0326
25. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. https://doi.org/10.1016/j.catena.2021.105299
26. Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape L. Borůvka et al. https://doi.org/10.17221/4/2022-SWR
27. Dynamics of deep soil carbon – insights from 14C time series across a climatic gradient T. van der Voort et al. https://doi.org/10.5194/bg-16-3233-2019
28. Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging I. Aulló-Maestro et al. https://doi.org/10.1007/s13595-021-01056-1
29. Snow instability patterns at the scale of a small basin B. Reuter et al. https://doi.org/10.1002/2015JF003700
30. A concept to optimize the accuracy of soil surface area and SOC stock quantification in mountainous landscapes J. Prietzel & M. Wiesmeier https://doi.org/10.1016/j.geoderma.2019.113922
31. A digital soil mapping approach to soil carbon monitoring, reporting and verification (MRV) A. Wadoux et al. https://doi.org/10.1038/s44264-026-00125-0
32. Digital soil mapping of soil organic carbon stocks in Western Ghats, South India S. Dharumarajan et al. https://doi.org/10.1016/j.geodrs.2021.e00387
33. Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. https://doi.org/10.1016/j.geoderma.2023.116579
34. Mapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction Measurements A. García-Tomillo et al. https://doi.org/10.1097/SS.0000000000000213
35. Digital mapping of soil carbon fractions with machine learning H. Keskin et al. https://doi.org/10.1016/j.geoderma.2018.12.037
36. Enabling soil carbon farming: presentation of a robust, affordable, and scalable method for soil carbon stock assessment T. van der Voort et al. https://doi.org/10.1007/s13593-022-00856-7
37. Soil organic carbon stocks did not change after 130 years of afforestation on a former Swiss Alpine pasture T. Speckert et al. https://doi.org/10.5194/soil-9-609-2023
38. Terrestrial laser scanning improves digital elevation models and topsoil pH modelling in regions with complex topography and dense vegetation A. Baltensweiler et al. https://doi.org/10.1016/j.envsoft.2017.05.009
39. Three-Dimensional Mapping of Forest Soil Carbon Stocks Using SCORPAN Modelling and Relative Depth Gradients in the North-Eastern Lowlands of Germany A. Russ et al. https://doi.org/10.3390/app11020714
40. Pedotransfer function to predict density of forest soils in Switzerland M. Nussbaum et al. https://doi.org/10.1002/jpln.201500546
41. Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. https://doi.org/10.1002/jpln.201800128
42. Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation O. Odebiri et al. https://doi.org/10.1016/j.geodrs.2025.e01022
43. Potential to map depth-specific soil organic matter content across an olive grove using quasi-2d and quasi-3d inversion of DUALEM-21 data J. Huang et al. https://doi.org/10.1016/j.catena.2017.01.017
44. Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models M. Nussbaum et al. https://doi.org/10.5194/soil-3-191-2017
45. Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall‐induced landslides L. Fan et al. https://doi.org/10.1002/2015WR017758