Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3219-2025
© 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-17-3219-2025
© 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
| 
04 Jul 2025
Data description paper |
| 04 Jul 2025
| 04 Jul 2025
China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022
Rong Shang
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Academy of Carbon Neutrality, Fujian Normal University, Fuzhou, 350117, China
Xudong Lin
CORRESPONDING AUTHOR
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Jing M. Chen
CORRESPONDING AUTHOR
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Department of Geography and Planning, University of Toronto, Toronto, Ontario, ON M5S 3G3, Canada
Yunjian Liang
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Keyan Fang
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Mingzhu Xu
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Yulin Yan
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
Weimin Ju
International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Guirui Yu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Nianpeng He
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Li Xu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Liangyun Liu
International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China
Jing Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Wang Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Jun Zhai
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100094, China
Zhongmin Hu
College of Ecology and Environment, Hainan University, Haikou, 570228, China
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Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Maierdang Keyimu, Zongshan Li, Bojie Fu, Guohua Liu, Fanjiang Zeng, Weiliang Chen, Zexin Fan, Keyan Fang, Xiuchen Wu, and Xiaochun Wang
Clim. Past, 17, 2381–2392, https://doi.org/10.5194/cp-17-2381-2021, https://doi.org/10.5194/cp-17-2381-2021, 2021
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We created a residual tree-ring width chronology and reconstructed non-growth-season precipitation (NGSP) over the period spanning 1600–2005 in the southeastern Tibetan Plateau (SETP), China. Reconstruction model verification as well as similar variations of NGSP reconstruction and Palmer Drought Severity Index reconstructions from the surrounding region indicate the reliability of the present reconstruction. Our reconstruction is representative of NGSP variability of a large region in the SETP.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
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Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
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We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
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Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
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Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Forest age is critical for carbon cycle modeling and effective forest management. Existing...
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