Articles | Volume 18, issue 10
https://doi.org/10.5194/essd-18-7719-2026
© 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-18-7719-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
26 Oct 2026
Data description article |
| 26 Oct 2026
| 26 Oct 2026
FineKarstAGB: a 30 m resolution aboveground biomass dataset for Southwest China derived by upscaling plot-level inventory using sub-meter GaoFen satellite data
Yixiang Li
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Zhengchao Chen
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Caixia Liu
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xuan Yang
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xiaowei Tong
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang 547100, Guangxi, China
Martin Brandt
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Zhaoming Wu
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Zeqing Wang
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Huaming Gao
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Xiaoyi Wang
State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
Sizhuo Li
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Xin Xu
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Siyu Liu
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Data sets
FineKarstAGB: A Fine-Grained, High-Resolution Dataset of Aboveground Biomass in Southwest China Yixiang Li et al. https://doi.org/10.57760/sciencedb.33452
Short summary
Southwest China has become an important carbon stock due to extensive forest restoration, yet existing biomass datasets are limited in highly heterogeneous landscapes. We developed a fine-grained 30 m aboveground biomass dataset in Southwest China using sub-meter GaoFen imagery and forest structural information. The dataset captures fine-scale spatial heterogeneity, includes trees outside forests, and provides support for regional carbon accounting and restoration efficacy assessment.
Southwest China has become an important carbon stock due to extensive forest restoration, yet...
Altmetrics
Final-revised paper
Preprint