Articles | Volume 17, issue 12
https://doi.org/10.5194/essd-17-6993-2025
© 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-17-6993-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
10 Dec 2025
Data description article |
| 10 Dec 2025
| 10 Dec 2025
Dynamics of China's forest carbon storage: the first 30 m annual aboveground biomass mapping from 1985 to 2023
Yaotong Cai
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Department of Geography, The University of Hong Kong, Hong Kong SAR, 999077, China
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Xiaoping Liu
CORRESPONDING AUTHOR
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Yuhe Chen
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Qianhui Shen
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Xiaocong Xu
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Honghui Zhang
Guangdong Engineering Center for Intelligent Spatial Planning, Guangdong Guodi Planning Science Technology Co. Ltd, Guangzhou, 510650, China
Sheng Nie
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Cheng Wang
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jia Wang
Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing, 100094, China
Ministry of Education of Engineering Research Center for Forest and Grassland Carbon Sequestration, Beijing Forestry University, Beijing, 100094, China
Bingjie Li
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Changjiang Wu
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
Haoming Zhuang
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
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Cited
15 citations as recorded by crossref.
- A hierarchical NFI–GEDI approach for aboveground biomass estimation and uncertainty propagation in subtropical forests of southeastern China C. Zhu et al. https://doi.org/10.1016/j.ecolind.2026.115015
- Responses of plant biomass to rising atmospheric CO2 concentration in the Yellow River Basin J. Luan & N. Ma https://doi.org/10.1016/j.gloplacha.2026.105480
- Changes in ecosystem carbon sequestration and their driving mechanisms in ethnic autonomous and non-autonomous areas of Western China Y. Zhou et al. https://doi.org/10.3389/fenvs.2026.1870986
- Explainable HybridEnsemble approach with golden jackal optimization for AGB estimation using multi-sensor remote sensing A. Aidoo Borsah et al. https://doi.org/10.3389/frsen.2026.1804569
- Spatiotemporal pattern of terrestrial ecosystem carbon storage in China with projections under multiple climate scenarios M. LIU et al. https://doi.org/10.1016/j.regsus.2026.100374
- Aboveground Biomass Retrieval and Time Series Analysis Across Different Forest Types Using Multi-Source Data Fusion Y. Shen et al. https://doi.org/10.3390/f17020273
- Forest Carbon Stock Dynamics in the West Qinling Mountains (2000–2025): A Multi-Source Remote Sensing Assessment with Spatial Robustness and Scenario Uncertainty Q. Ba et al. https://doi.org/10.3390/f17080867
- Carbon fluxes from urban land-use change in Zhejiang Province, China Y. Zhu et al. https://doi.org/10.1016/j.horiz.2026.100181
- Permafrost degradation: A critical driver of aboveground carbon sink loss in China's boreal forests H. Yang et al. https://doi.org/10.1016/j.catena.2026.109912
- A CASA-Based, MODIS-Constrained Framework for Consistent Annual NPP Simulation in Alpine Complex Environments: A Case Study of the Gannan Plateau D. Zhang et al. https://doi.org/10.3390/rs18152456
- Differentiable Physical Modeling for Forest Above-Ground Biomass Retrieval by Unifying a Water Cloud Model and Deep Learning C. Zhao et al. https://doi.org/10.3390/rs18060912
- Conservation Effectiveness and Spatial Drivers of Qianjiangyuan National Park: Causal Evidence from a Quasi-Experimental Framework C. Wang et al. https://doi.org/10.3390/land15050863
- Spatial Patterns and Influencing Factors of Forest Net Ecosystem Productivity in the Middle and Upper Reaches of the Ganjiang River Basin J. Zhao et al. https://doi.org/10.3390/f17060651
- Explainable multi-sensor geospatial modeling of forest biomass dynamics under compound climate extremes in subtropical monsoon regions B. Xu et al. https://doi.org/10.1080/15481603.2026.2658313
- Extraction of eucalyptus age and estimation of its aboveground biomass in China with the integration of empirical model and machine learning algorithm C. Tang et al. https://doi.org/10.1016/j.fecs.2026.100440
15 citations as recorded by crossref.
- A hierarchical NFI–GEDI approach for aboveground biomass estimation and uncertainty propagation in subtropical forests of southeastern China C. Zhu et al. https://doi.org/10.1016/j.ecolind.2026.115015
- Responses of plant biomass to rising atmospheric CO2 concentration in the Yellow River Basin J. Luan & N. Ma https://doi.org/10.1016/j.gloplacha.2026.105480
- Changes in ecosystem carbon sequestration and their driving mechanisms in ethnic autonomous and non-autonomous areas of Western China Y. Zhou et al. https://doi.org/10.3389/fenvs.2026.1870986
- Explainable HybridEnsemble approach with golden jackal optimization for AGB estimation using multi-sensor remote sensing A. Aidoo Borsah et al. https://doi.org/10.3389/frsen.2026.1804569
- Spatiotemporal pattern of terrestrial ecosystem carbon storage in China with projections under multiple climate scenarios M. LIU et al. https://doi.org/10.1016/j.regsus.2026.100374
- Aboveground Biomass Retrieval and Time Series Analysis Across Different Forest Types Using Multi-Source Data Fusion Y. Shen et al. https://doi.org/10.3390/f17020273
- Forest Carbon Stock Dynamics in the West Qinling Mountains (2000–2025): A Multi-Source Remote Sensing Assessment with Spatial Robustness and Scenario Uncertainty Q. Ba et al. https://doi.org/10.3390/f17080867
- Carbon fluxes from urban land-use change in Zhejiang Province, China Y. Zhu et al. https://doi.org/10.1016/j.horiz.2026.100181
- Permafrost degradation: A critical driver of aboveground carbon sink loss in China's boreal forests H. Yang et al. https://doi.org/10.1016/j.catena.2026.109912
- A CASA-Based, MODIS-Constrained Framework for Consistent Annual NPP Simulation in Alpine Complex Environments: A Case Study of the Gannan Plateau D. Zhang et al. https://doi.org/10.3390/rs18152456
- Differentiable Physical Modeling for Forest Above-Ground Biomass Retrieval by Unifying a Water Cloud Model and Deep Learning C. Zhao et al. https://doi.org/10.3390/rs18060912
- Conservation Effectiveness and Spatial Drivers of Qianjiangyuan National Park: Causal Evidence from a Quasi-Experimental Framework C. Wang et al. https://doi.org/10.3390/land15050863
- Spatial Patterns and Influencing Factors of Forest Net Ecosystem Productivity in the Middle and Upper Reaches of the Ganjiang River Basin J. Zhao et al. https://doi.org/10.3390/f17060651
- Explainable multi-sensor geospatial modeling of forest biomass dynamics under compound climate extremes in subtropical monsoon regions B. Xu et al. https://doi.org/10.1080/15481603.2026.2658313
- Extraction of eucalyptus age and estimation of its aboveground biomass in China with the integration of empirical model and machine learning algorithm C. Tang et al. https://doi.org/10.1016/j.fecs.2026.100440
Saved (final revised paper)
Latest update: 17 Aug 2026
Short summary
China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term high-resolution data on their biomass have been limited. We developed a 30 m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.
China’s forests play a crucial role in storing carbon and mitigating climate change, yet...
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