Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-131-2026
© Author(s) 2026. 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-18-131-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global dataset of soil organic carbon mineralization in response to incubation temperature changes
Shuai Zhang
State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou 310058, China
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Mingming Wang
State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou 310058, China
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Jinyang Zheng
State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou 310058, China
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou 310058, China
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
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Earth Syst. Sci. Data, 16, 2367–2383, https://doi.org/10.5194/essd-16-2367-2024, https://doi.org/10.5194/essd-16-2367-2024, 2024
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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.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
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Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Guocheng Wang, Zhongkui Luo, Yao Huang, Wenjuan Sun, Yurong Wei, Liujun Xiao, Xi Deng, Jinhuan Zhu, Tingting Li, and Wen Zhang
Atmos. Chem. Phys., 21, 3059–3071, https://doi.org/10.5194/acp-21-3059-2021, https://doi.org/10.5194/acp-21-3059-2021, 2021
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We simulate the spatiotemporal dynamics of aboveground biomass (AGB) in Inner Mongolian grasslands using a machine-learning-based approach. Under climate change, on average, compared with the historical AGB (average of 1981–2019), the AGB at the end of this century (average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5. The decrease in AGB might be mitigated or even reversed by positive carbon dioxide enrichment effects on plant growth.
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Short summary
We compiled a global dataset of soil carbon release rates from lab incubations across 721 soils and various temperatures. Many experiments did not reflect real-world conditions, especially for deeper soils and realistic temperature ranges. Simulation experiments showed that both carbon properties and environmental constraints equally drive temperature sensitivity. Current climate models miss these patterns, but our findings can help improve predictions of soil carbon responses to warming.
We compiled a global dataset of soil carbon release rates from lab incubations across 721 soils...
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