FineKarstAGB: a 30 m resolution aboveground biomass dataset for Southwest China derived by upscaling plot-level inventory using sub-meter GaoFen satellite data
6 affiliations
- 1State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
- 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- 3Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
- 4Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang 547100, Guangxi, China
- 5Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- 6State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
Abstract
Southwest China has emerged as a key global carbon stock due to widespread forest expansion and aboveground biomass (AGB) increases driven by major ecological restoration since 2000, making accurate AGB estimates vital for assessing restoration efficacy. However, existing global and national-scale AGB products exhibit substantial limitations in this region, with little correlation with National Forest Inventory (NFI) plot and UAV LiDAR data, which is likely related to the pronounced spatial heterogeneity induced by karst landscapes and large-scale restoration efforts that exacerbate mixed-pixel effects. To address these challenges, this study proposes a Canopy Structure-driven Multi-feature Fusion Network (CSMF-Net) designed for high-precision AGB estimation in complex regions. The method takes NFI plot data as ground truth and integrates GaoFen imagery, horizontal structure derived from tree crown segmentation and vertical structure represented by canopy height data. Based on this approach, we generated a fine-grained 30 m AGB dataset (FineKarstAGB) for 2024, covering four provinces in Southwest China (Yunnan, Guizhou, Guangxi, and Hunan). Accuracy assessment against independent NFI plot data demonstrated the model's robust performance (R²=0.69, RMSE = 28.51 Mg ha⁻¹), showing no evidence of saturation in high-biomass regions. Furthermore, a structural consistency assessment using an independent UAV LiDAR-derived canopy height model confirmed that FineKarstAGB maintains a strong relationship with forest vertical structure (R²=0.54). Other public datasets showed weak relationships with both NFI observations (R²≤0.12) and UAV LiDAR-derived canopy height (R²<0.1). Due to the tree-level segmentation, our dataset also quantifies AGB contributions from sparse trees outside forests, thus enabling more comprehensive and spatially explicit carbon accounting. This dataset provides critical support for regional carbon cycle assessments, fine-scale evaluations of ecological restoration outcomes, and progress toward national carbon neutrality targets. The dataset is available at https://doi.org/10.57760/sciencedb.33452 (Li et al., 2026).
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BibTeX
@article{li2026finekarstagb,
title = {{FineKarstAGB: a 30 m resolution aboveground biomass dataset for Southwest China derived by upscaling plot-level inventory using sub-meter GaoFen satellite data}},
author = {Y. Li and Y. Li and Y. Bai and Z. Chen and C. Liu and X. Yang and X. Tong and X. Tong and M. Brandt and Z. Wu and Z. Wu and Z. Wang and Z. Wang and H. Gao and H. Gao and X. Wang and S. Li and X. Xu and X. Xu and S. Liu},
journal = {Earth System Science Data},
year = {2026},
month = oct,
doi = {10.5194/essd-18-7719-2026},
url = {https://doi.org/10.5194/essd-18-7719-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - FineKarstAGB: a 30 m resolution aboveground biomass dataset for Southwest China derived by upscaling plot-level inventory using sub-meter GaoFen satellite data
AU - Y. Li
AU - Y. Li
AU - Y. Bai
AU - Z. Chen
AU - C. Liu
AU - X. Yang
AU - X. Tong
AU - X. Tong
AU - M. Brandt
AU - Z. Wu
AU - Z. Wu
AU - Z. Wang
AU - Z. Wang
AU - H. Gao
AU - H. Gao
AU - X. Wang
AU - S. Li
AU - X. Xu
AU - X. Xu
AU - S. Liu
PY - 2026
DA - 2026/10/26
JO - Earth System Science Data
DO - 10.5194/essd-18-7719-2026
UR - https://doi.org/10.5194/essd-18-7719-2026
AB - Southwest China has emerged as a key global carbon stock due to widespread forest expansion and aboveground biomass (AGB) increases driven by major ecological restoration since 2000, making accurate AGB estimates vital for assessing restoration efficacy. However, existing global and national-scale AGB products exhibit substantial limitations in this region, with little correlation with National Forest Inventory (NFI) plot and UAV LiDAR data, which is likely related to the pronounced spatial heterogeneity induced by karst landscapes and large-scale restoration efforts that exacerbate mixed-pixel effects. To address these challenges, this study proposes a Canopy Structure-driven Multi-feature Fusion Network (CSMF-Net) designed for high-precision AGB estimation in complex regions. The method takes NFI plot data as ground truth and integrates GaoFen imagery, horizontal structure derived from tree crown segmentation and vertical structure represented by canopy height data. Based on this approach, we generated a fine-grained 30 m AGB dataset (FineKarstAGB) for 2024, covering four provinces in Southwest China (Yunnan, Guizhou, Guangxi, and Hunan). Accuracy assessment against independent NFI plot data demonstrated the model's robust performance (R²=0.69, RMSE = 28.51 Mg ha⁻¹), showing no evidence of saturation in high-biomass regions. Furthermore, a structural consistency assessment using an independent UAV LiDAR-derived canopy height model confirmed that FineKarstAGB maintains a strong relationship with forest vertical structure (R²=0.54). Other public datasets showed weak relationships with both NFI observations (R²≤0.12) and UAV LiDAR-derived canopy height (R²<0.1). Due to the tree-level segmentation, our dataset also quantifies AGB contributions from sparse trees outside forests, thus enabling more comprehensive and spatially explicit carbon accounting. This dataset provides critical support for regional carbon cycle assessments, fine-scale evaluations of ecological restoration outcomes, and progress toward national carbon neutrality targets. The dataset is available at https://doi.org/10.57760/sciencedb.33452 (Li et al., 2026).
SN - 1866-3508
SN - 1866-3516
ER -