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Dynamics of China's forest carbon storage: the first 30 m annual aboveground biomass mapping from 1985 to 2023

Yaotong Cai,Peng Zhu,Xing Li,Xiaoping Liu,Yuhe Chen,Qianhui Shen,Xiaocong Xu,Honghui Zhang,S. Nie,Cheng Wang,Jia Wang,Bingjie Li,Changjiang Wu,Haoming Zhuang

Published 2025 in Earth System Science Data

ABSTRACT

Abstract. Accurate estimation and monitoring of forest aboveground biomass (AGB) are essential for understanding carbon dynamics, managing forest resources, and guiding environmental policies. However, the spatial and temporal patterns, dynamics, and driving factors of forest AGB in China over recent decades remain insufficiently understood, hindering ecosystem analysis and forest management strategies. This study combines multi-source remote sensing data with residual neural networks (ResNets) to develop the first 30 m resolution annual China Forest AGB dataset (1985–2023) with uncertainty quantification. Validation results confirm the robustness of the ResNets model, achieving an R2 of 0.91, RMSE of 16.49 Mg ha−1, and Bias of 0.50 Mg ha−1 against GEDI footprint AGBD, and an R2 of 0.63, RMSE of 68.26 Mg ha−1, and Bias of −19.87 Mg ha−1 against independent multi-year ground survey data. The dataset reveals a notable increase in China's average forest aboveground biomass density (AGBD) from 95.74 ± 11.30 Mg ha−1 in 1985 to 122.69 ± 13.94 Mg ha−1 in 2023. During this period, total forest aboveground carbon (AGC) stock rose from 5.50 ± 0.23 PgC to 13.97 ± 0.87 PgC, establishing China's forests as a significant carbon sink over the past four decades, with a net carbon sink of 0.22 ± 0.01 PgC yr−1, offsetting 11.5 %–14.9 % of China's annual fossil fuel and industrial carbon dioxide emissions. Forest growth contributed 65.1 % (5.75 PgC) of the total AGC increase, while forest expansion accounted for 34.9 % (3.09 PgC). This dataset provides critical information for forest carbon accounting in China and offers valuable insights for climate change mitigation, ecosystem conservation, and sustainable land management. The dataset is available at https://doi.org/10.5281/zenodo.12620984 (Part I: 1985–1993, Cai et al., 2025a), https://doi.org/10.5281/zenodo.12637101 (Part II: 1994–2001, Cai et al., 2025b), https://doi.org/10.5281/zenodo.12655492 (Part III: 2002–2008, Cai et al., 2025c), https://doi.org/10.5281/zenodo.12658255 (Part IV: 2009–2015, Cai et al., 2025d), https://doi.org/10.5281/zenodo.12742210 (Part V: 2016–2021, Cai et al., 2025e), https://doi.org/10.5281/zenodo.12747329 (Part VI: 2022-2023, Cai et al., 2025f).

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