Articles | Volume 7, issue 3
https://doi.org/10.5194/gmd-7-1197-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-1197-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
25 Jun 2014
Methods for assessment of models |
| 25 Jun 2014
Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging
M. Nussbaum
Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
A. Papritz
Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
A. Baltensweiler
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
L. Walthert
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
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Cited
42 citations as recorded by crossref.
- Mapping soil organic carbon on a national scale: Towards an improved and updated map of Madagascar N. Ramifehiarivo et al. 10.1016/j.geodrs.2016.12.002
- Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. 10.1016/j.geoderma.2019.05.031
- 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. 10.1007/s11119-016-9481-6
- Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks L. Lombardo et al. 10.1016/j.geoderma.2017.12.011
- GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth V. Mulder et al. 10.1016/j.scitotenv.2016.07.066
- 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. 10.1016/j.geoderma.2018.08.024
- No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America M. Guevara et al. 10.5194/soil-4-173-2018
- Robust soil mapping at the farm scale with vis–NIR spectroscopy L. Ramirez‐Lopez et al. 10.1111/ejss.12752
- Hydrodynamic, Seasonal, and Climatic Factors Affecting the Terrestrial-to-Aquatic Export of Plant-Derived Biomarkers M. Schwab et al. 10.1016/j.gca.2024.12.009
- Deconvolving the Fate of Carbon in Coastal Sediments T. Van der Voort et al. 10.1029/2018GL077009
- Evidence for Soil Phosphorus Resource Partitioning in a Diverse Tropical Tree Community R. Müller et al. 10.3390/f15020361
- Comparison of geostatistics, machine learning algorithms, and their hybrid approaches for modeling soil organic carbon density in tropical forests V. Ho et al. 10.1007/s11368-025-04027-5
- 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. 10.1016/j.geodrs.2024.e00817
- Validation of uncertainty predictions in digital soil mapping J. Schmidinger & G. Heuvelink 10.1016/j.geoderma.2023.116585
- 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. 10.1016/j.scitotenv.2020.142661
- Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al. 10.1016/j.geodrs.2021.e00437
- Environmental and hydrologic controls on sediment and organic carbon export from a subalpine catchment: insights from a time series M. Schwab et al. 10.5194/bg-19-5591-2022
- Spatial aggregation of soil property predictions in support of local land management K. Vaysse et al. 10.1111/sum.12350
- Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes O. Odebiri et al. 10.1016/j.catena.2024.108216
- Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach L. YANG et al. 10.1016/S1002-0160(17)60322-9
- Evaluation of digital soil mapping approaches with large sets of environmental covariates M. Nussbaum et al. 10.5194/soil-4-1-2018
- Spatial 3D mapping of forest soil carbon stocks in Hesse, Germany F. Heitkamp et al. 10.1002/jpln.202100138
- Radiocarbon signatures of carbon phases exported by Swiss rivers in the Anthropocene T. Rhyner et al. 10.1098/rsta.2022.0326
- Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
- Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape L. Borůvka et al. 10.17221/4/2022-SWR
- Dynamics of deep soil carbon – insights from 14C time series across a climatic gradient T. van der Voort et al. 10.5194/bg-16-3233-2019
- Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging I. Aulló-Maestro et al. 10.1007/s13595-021-01056-1
- Snow instability patterns at the scale of a small basin B. Reuter et al. 10.1002/2015JF003700
- A concept to optimize the accuracy of soil surface area and SOC stock quantification in mountainous landscapes J. Prietzel & M. Wiesmeier 10.1016/j.geoderma.2019.113922
- Digital soil mapping of soil organic carbon stocks in Western Ghats, South India S. Dharumarajan et al. 10.1016/j.geodrs.2021.e00387
- Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. 10.1016/j.geoderma.2023.116579
- Mapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction Measurements A. García-Tomillo et al. 10.1097/SS.0000000000000213
- Digital mapping of soil carbon fractions with machine learning H. Keskin et al. 10.1016/j.geoderma.2018.12.037
- Enabling soil carbon farming: presentation of a robust, affordable, and scalable method for soil carbon stock assessment T. van der Voort et al. 10.1007/s13593-022-00856-7
- Soil organic carbon stocks did not change after 130 years of afforestation on a former Swiss Alpine pasture T. Speckert et al. 10.5194/soil-9-609-2023
- Terrestrial laser scanning improves digital elevation models and topsoil pH modelling in regions with complex topography and dense vegetation A. Baltensweiler et al. 10.1016/j.envsoft.2017.05.009
- 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. 10.3390/app11020714
- Pedotransfer function to predict density of forest soils in Switzerland M. Nussbaum et al. 10.1002/jpln.201500546
- Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
- 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. 10.1016/j.catena.2017.01.017
- Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models M. Nussbaum et al. 10.5194/soil-3-191-2017
- Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall‐induced landslides L. Fan et al. 10.1002/2015WR017758
42 citations as recorded by crossref.
- Mapping soil organic carbon on a national scale: Towards an improved and updated map of Madagascar N. Ramifehiarivo et al. 10.1016/j.geodrs.2016.12.002
- Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. 10.1016/j.geoderma.2019.05.031
- 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. 10.1007/s11119-016-9481-6
- Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks L. Lombardo et al. 10.1016/j.geoderma.2017.12.011
- GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth V. Mulder et al. 10.1016/j.scitotenv.2016.07.066
- 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. 10.1016/j.geoderma.2018.08.024
- No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America M. Guevara et al. 10.5194/soil-4-173-2018
- Robust soil mapping at the farm scale with vis–NIR spectroscopy L. Ramirez‐Lopez et al. 10.1111/ejss.12752
- Hydrodynamic, Seasonal, and Climatic Factors Affecting the Terrestrial-to-Aquatic Export of Plant-Derived Biomarkers M. Schwab et al. 10.1016/j.gca.2024.12.009
- Deconvolving the Fate of Carbon in Coastal Sediments T. Van der Voort et al. 10.1029/2018GL077009
- Evidence for Soil Phosphorus Resource Partitioning in a Diverse Tropical Tree Community R. Müller et al. 10.3390/f15020361
- Comparison of geostatistics, machine learning algorithms, and their hybrid approaches for modeling soil organic carbon density in tropical forests V. Ho et al. 10.1007/s11368-025-04027-5
- 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. 10.1016/j.geodrs.2024.e00817
- Validation of uncertainty predictions in digital soil mapping J. Schmidinger & G. Heuvelink 10.1016/j.geoderma.2023.116585
- 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. 10.1016/j.scitotenv.2020.142661
- Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al. 10.1016/j.geodrs.2021.e00437
- Environmental and hydrologic controls on sediment and organic carbon export from a subalpine catchment: insights from a time series M. Schwab et al. 10.5194/bg-19-5591-2022
- Spatial aggregation of soil property predictions in support of local land management K. Vaysse et al. 10.1111/sum.12350
- Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes O. Odebiri et al. 10.1016/j.catena.2024.108216
- Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach L. YANG et al. 10.1016/S1002-0160(17)60322-9
- Evaluation of digital soil mapping approaches with large sets of environmental covariates M. Nussbaum et al. 10.5194/soil-4-1-2018
- Spatial 3D mapping of forest soil carbon stocks in Hesse, Germany F. Heitkamp et al. 10.1002/jpln.202100138
- Radiocarbon signatures of carbon phases exported by Swiss rivers in the Anthropocene T. Rhyner et al. 10.1098/rsta.2022.0326
- Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
- Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape L. Borůvka et al. 10.17221/4/2022-SWR
- Dynamics of deep soil carbon – insights from 14C time series across a climatic gradient T. van der Voort et al. 10.5194/bg-16-3233-2019
- Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging I. Aulló-Maestro et al. 10.1007/s13595-021-01056-1
- Snow instability patterns at the scale of a small basin B. Reuter et al. 10.1002/2015JF003700
- A concept to optimize the accuracy of soil surface area and SOC stock quantification in mountainous landscapes J. Prietzel & M. Wiesmeier 10.1016/j.geoderma.2019.113922
- Digital soil mapping of soil organic carbon stocks in Western Ghats, South India S. Dharumarajan et al. 10.1016/j.geodrs.2021.e00387
- Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. 10.1016/j.geoderma.2023.116579
- Mapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction Measurements A. García-Tomillo et al. 10.1097/SS.0000000000000213
- Digital mapping of soil carbon fractions with machine learning H. Keskin et al. 10.1016/j.geoderma.2018.12.037
- Enabling soil carbon farming: presentation of a robust, affordable, and scalable method for soil carbon stock assessment T. van der Voort et al. 10.1007/s13593-022-00856-7
- Soil organic carbon stocks did not change after 130 years of afforestation on a former Swiss Alpine pasture T. Speckert et al. 10.5194/soil-9-609-2023
- Terrestrial laser scanning improves digital elevation models and topsoil pH modelling in regions with complex topography and dense vegetation A. Baltensweiler et al. 10.1016/j.envsoft.2017.05.009
- 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. 10.3390/app11020714
- Pedotransfer function to predict density of forest soils in Switzerland M. Nussbaum et al. 10.1002/jpln.201500546
- Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
- 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. 10.1016/j.catena.2017.01.017
- Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models M. Nussbaum et al. 10.5194/soil-3-191-2017
- Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall‐induced landslides L. Fan et al. 10.1002/2015WR017758
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