Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-517-2025
© Author(s) 2025. 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-17-517-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
07 Feb 2025
Data description paper |
| 07 Feb 2025
| 07 Feb 2025
A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
Gaosong Shi
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Wenye Sun
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Zhongwang Wei
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Hua Yuan
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Lu Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Xiaolin Sun
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Ye Zhang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Hongbin Liang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Danxi Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Feini Huang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Qingliang Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
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Cited
6 citations as recorded by crossref.
- Improving soil pH prediction and mapping using anthropogenic variables and machine learning models D. Li et al. 10.1080/10106049.2025.2482699
- How Do Natural Environmental Factors Influence the Spatial Patterns and Site Selection of Famous Mountain Temple Complexes in China? Quantitative Research on Wudang Mountain in the Ming Dynasty Y. Yan et al. 10.3390/land14071441
- Variation in leaf construction cost and environmental drivers in China Y. Liu et al. 10.1093/jpe/rtaf012
- Machine learning ensemble technique for exploring soil type evolution X. Wu et al. 10.1038/s41598-025-10608-8
- Improved Biome-BGC model for simulating spatiotemporal dynamics of gross primary productivity in evergreen broadleaf forests of the karst region H. Liu et al. 10.1016/j.envsoft.2025.106563
- A China dataset of soil properties for land surface modelling (version 2, CSDLv2) G. Shi et al. 10.5194/essd-17-517-2025
5 citations as recorded by crossref.
- Improving soil pH prediction and mapping using anthropogenic variables and machine learning models D. Li et al. 10.1080/10106049.2025.2482699
- How Do Natural Environmental Factors Influence the Spatial Patterns and Site Selection of Famous Mountain Temple Complexes in China? Quantitative Research on Wudang Mountain in the Ming Dynasty Y. Yan et al. 10.3390/land14071441
- Variation in leaf construction cost and environmental drivers in China Y. Liu et al. 10.1093/jpe/rtaf012
- Machine learning ensemble technique for exploring soil type evolution X. Wu et al. 10.1038/s41598-025-10608-8
- Improved Biome-BGC model for simulating spatiotemporal dynamics of gross primary productivity in evergreen broadleaf forests of the karst region H. Liu et al. 10.1016/j.envsoft.2025.106563
1 citations as recorded by crossref.
Latest update: 07 Aug 2025
Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
In this study, we developed the second version of China's high-resolution soil information grid...
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