Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 May 2024
Data description paper |
| 16 May 2024
| 16 May 2024
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Songchao Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Zhongxing Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Xianglin Zhang
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
UMR ECOSYS, AgroParisTech, INRAE, Universiteé Paris-Saclay, Palaiseau 91120, France
Zhongkui Luo
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Calogero Schillaci
European Commission, Joint Research Centre, Ispra, 21026, Italy
Dominique Arrouays
INRAE, Info&Sols, Orléans 45075, France
Anne Christine Richer-de-Forges
INRAE, Info&Sols, Orléans 45075, France
Zhou Shi
CORRESPONDING AUTHOR
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
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Cited
11 citations as recorded by crossref.
- Hand-feel soil texture classes and particle-size distribution as predictors of soil water content at field capacity. Further insights into the sources of uncertainty A. Richer-de-Forges et al. 10.1016/j.catena.2024.108268
- Spatial Estimation of Soil Organic Matter and Total Nitrogen by Fusing Field Vis–NIR Spectroscopy and Multispectral Remote Sensing Data D. Xu et al. 10.3390/rs17040729
- Assessing Land Cover Changes Using the LUCAS Database and Sentinel Imagery: A Comparative Analysis of Accuracy Metrics B. Hejmanowska & P. Kramarczyk 10.3390/app15010240
- Predicting bulk density in Brazilian soils for carbon stocks calculation: a comparative study of multiple linear regression and Random Forest models using continuous and categorical variables W. dos Santos et al. 10.1007/s44378-025-00035-6
- Deep soil organic carbon: A review J. Dubeux, et al. 10.1079/cabireviews.2024.0024
- A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data Z. Cui et al. 10.3390/land14040677
- Prediction of soil organic carbon fractions in tropical cropland using a regional visible and near-infrared spectral library and machine learning L. Dai et al. 10.1016/j.still.2024.106297
- Assessing the effect of pedotransfer functions on modeling of soil water dynamics R. Hessine et al. 10.1080/23249676.2025.2463901
- Ensemble modelling-based pedotransfer functions for predicting soil bulk density in China Z. Chen et al. 10.1016/j.geoderma.2024.116969
- Including soil spatial neighbor information for digital soil mapping Z. Chen et al. 10.1016/j.geoderma.2024.117072
- European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions S. Chen et al. 10.5194/essd-16-2367-2024
10 citations as recorded by crossref.
- Hand-feel soil texture classes and particle-size distribution as predictors of soil water content at field capacity. Further insights into the sources of uncertainty A. Richer-de-Forges et al. 10.1016/j.catena.2024.108268
- Spatial Estimation of Soil Organic Matter and Total Nitrogen by Fusing Field Vis–NIR Spectroscopy and Multispectral Remote Sensing Data D. Xu et al. 10.3390/rs17040729
- Assessing Land Cover Changes Using the LUCAS Database and Sentinel Imagery: A Comparative Analysis of Accuracy Metrics B. Hejmanowska & P. Kramarczyk 10.3390/app15010240
- Predicting bulk density in Brazilian soils for carbon stocks calculation: a comparative study of multiple linear regression and Random Forest models using continuous and categorical variables W. dos Santos et al. 10.1007/s44378-025-00035-6
- Deep soil organic carbon: A review J. Dubeux, et al. 10.1079/cabireviews.2024.0024
- A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data Z. Cui et al. 10.3390/land14040677
- Prediction of soil organic carbon fractions in tropical cropland using a regional visible and near-infrared spectral library and machine learning L. Dai et al. 10.1016/j.still.2024.106297
- Assessing the effect of pedotransfer functions on modeling of soil water dynamics R. Hessine et al. 10.1080/23249676.2025.2463901
- Ensemble modelling-based pedotransfer functions for predicting soil bulk density in China Z. Chen et al. 10.1016/j.geoderma.2024.116969
- Including soil spatial neighbor information for digital soil mapping Z. Chen et al. 10.1016/j.geoderma.2024.117072
Latest update: 28 Mar 2025
Short summary
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across...
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