Articles | Volume 7, issue 2
https://doi.org/10.5194/soil-7-377-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-7-377-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
06 Jul 2021
Original research article |
| 06 Jul 2021
| 06 Jul 2021
Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data
Kpade O. L. Hounkpatin
CORRESPONDING AUTHOR
Department of Soil and Environment, Swedish University of
Agricultural Sciences, P.O. Box 7014,
75007 Uppsala, Sweden
Johan Stendahl
Department of Soil and Environment, Swedish University of
Agricultural Sciences, P.O. Box 7014,
75007 Uppsala, Sweden
Mattias Lundblad
Department of Soil and Environment, Swedish University of
Agricultural Sciences, P.O. Box 7014,
75007 Uppsala, Sweden
Erik Karltun
Department of Soil and Environment, Swedish University of
Agricultural Sciences, P.O. Box 7014,
75007 Uppsala, Sweden
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Cited
27 citations as recorded by crossref.
- Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape L. Borůvka et al.
- A zoning-based machine learning framework for accurate soil organic matter prediction across Mollisol and non-Mollisol regions X. Li et al.
- Recovery of ecosystem carbon pools 35 years after whole-tree and stem-only clearcutting a red spruce – balsam fir forest in north-central Maine, USA I. Stupak et al.
- Improving spatial prediction of soil organic matter in central Vietnam using Bayesian-enhanced machine learning and environmental covariates N. Ngu et al.
- Geospatial and field-based digital mapping of soil organic carbon in Taiwan's agricultural and forest landscapes C. Syu et al.
- 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
- Detecting Drivers and Predicting Spatial Distribution of Soil Organic Carbon in an Arid Region Using Machine Learning G. Chen et al.
- Decadal Changes of Organic Carbon, Nitrogen, and Acidity of Austrian Forest Soils R. Jandl et al.
- Quantitative assessment of seasonal plant litter of Voronezh upland oak forest I. Golyadkina et al.
- Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning P. Fu et al.
- Spatial predictive modeling of soil organic carbon stocks in Norwegian forests A. Hagenbo et al.
- Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods M. Lu et al.
- 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.
- Carbon pool dynamics after variable retention harvesting in Nothofagus pumilio forests of Tierra del Fuego J. Chaves et al.
- Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions A. Ågren et al.
- Do South African restoration projects overpromise on carbon sequestration potential? J. Prevôst et al.
- Digital Soil Mapping of Soil Organic Carbon at Eastern Slopes of Mount Kenya Y. Gelsleichter et al.
- Soil organic carbon-to-clay ratio as a proxy for land degradation: machine learning-based spatial prediction in semi-arid agricultural lands M. Kılıç et al.
- Do we have globally representative data to understand soil processes? A. Malhotra et al.
- Analysis of the Effect of Moisture Content on the Spatial Variability of Carbon Stock in Forest Soils of European Russia I. Ryzhova et al.
- Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape J. Larson et al.
- Predicting soil carbon stock in remote areas of the Central Amazon region using machine learning techniques A. Ferreira et al.
- Bayesian spatial prediction of soil organic carbon stocks in eastern DRC using INLA-SPDE and environmental covariates A. Kangela et al.
- A Comparative Assessment of Regular and Spatial Cross-Validation in Subfield Machine Learning Prediction of Maize Yield from Sentinel-2 Phenology D. Radočaj et al.
- Reassessing boreal wildfire drivers enables high-resolution mapping of emissions for climate adaptation J. Eckdahl et al.
- The impact of scale and variable selection for predicting topsoil organic carbon using machine learning and geostatistics based on legacy soil data N. Ngubo et al.
- Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru C. Carbajal et al.
27 citations as recorded by crossref.
- Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape L. Borůvka et al.
- A zoning-based machine learning framework for accurate soil organic matter prediction across Mollisol and non-Mollisol regions X. Li et al.
- Recovery of ecosystem carbon pools 35 years after whole-tree and stem-only clearcutting a red spruce – balsam fir forest in north-central Maine, USA I. Stupak et al.
- Improving spatial prediction of soil organic matter in central Vietnam using Bayesian-enhanced machine learning and environmental covariates N. Ngu et al.
- Geospatial and field-based digital mapping of soil organic carbon in Taiwan's agricultural and forest landscapes C. Syu et al.
- 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
- Detecting Drivers and Predicting Spatial Distribution of Soil Organic Carbon in an Arid Region Using Machine Learning G. Chen et al.
- Decadal Changes of Organic Carbon, Nitrogen, and Acidity of Austrian Forest Soils R. Jandl et al.
- Quantitative assessment of seasonal plant litter of Voronezh upland oak forest I. Golyadkina et al.
- Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning P. Fu et al.
- Spatial predictive modeling of soil organic carbon stocks in Norwegian forests A. Hagenbo et al.
- Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods M. Lu et al.
- 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.
- Carbon pool dynamics after variable retention harvesting in Nothofagus pumilio forests of Tierra del Fuego J. Chaves et al.
- Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions A. Ågren et al.
- Do South African restoration projects overpromise on carbon sequestration potential? J. Prevôst et al.
- Digital Soil Mapping of Soil Organic Carbon at Eastern Slopes of Mount Kenya Y. Gelsleichter et al.
- Soil organic carbon-to-clay ratio as a proxy for land degradation: machine learning-based spatial prediction in semi-arid agricultural lands M. Kılıç et al.
- Do we have globally representative data to understand soil processes? A. Malhotra et al.
- Analysis of the Effect of Moisture Content on the Spatial Variability of Carbon Stock in Forest Soils of European Russia I. Ryzhova et al.
- Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape J. Larson et al.
- Predicting soil carbon stock in remote areas of the Central Amazon region using machine learning techniques A. Ferreira et al.
- Bayesian spatial prediction of soil organic carbon stocks in eastern DRC using INLA-SPDE and environmental covariates A. Kangela et al.
- A Comparative Assessment of Regular and Spatial Cross-Validation in Subfield Machine Learning Prediction of Maize Yield from Sentinel-2 Phenology D. Radočaj et al.
- Reassessing boreal wildfire drivers enables high-resolution mapping of emissions for climate adaptation J. Eckdahl et al.
- The impact of scale and variable selection for predicting topsoil organic carbon using machine learning and geostatistics based on legacy soil data N. Ngubo et al.
- Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru C. Carbajal et al.
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
Forests store large amounts of carbon in soils. Implementing suitable measures to improve the sink potential of forest soils would require accurate data on the carbon stored in forest soils and a better understanding of the factors affecting this storage. This study showed that the prediction of soil carbon stock in Swedish forest soils can increase in accuracy when one divides a big region into smaller areas in combination with information collected locally and derived from satellites.
Forests store large amounts of carbon in soils. Implementing suitable measures to improve the...
