the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Mar 2025
Dynamics of China’s Forest Carbon Storage: The First 30 m Annual Aboveground Biomass Mapping from 1985 to 2023
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 0.92, RMSE of 16.06 Mg/ha, and Bias of 0.06 Mg/ha against GEDI footprint AGBD, and an R2 of 0.63, RMSE of 68.26 Mg/ha, and Bias of -19.87 Mg/ha 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 in 1985 to 122.69±13.94 Mg/ha 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 fossil fuel and industrial 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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth System Science Data.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: final response (author comments only)
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RC1: 'Comment on essd-2025-96', Anonymous Referee #1, 11 Jun 2025
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. It’s a long-term series of AGB data, which is of great significance for estimating forest carbon sink in China.
Comments:
1. Introduction
- The first three paragraghs need to be condensed.
- Line 90-100, It is best to compare the advantages and disadvantages of the existing data. eg: Have you compared international time series data, such as the AGB data of the European Space Agency, etc.
- Why did you choose the ResNet-based deep learning algorithm? You should make it clear, because this is your core technical approach.
2. Methodology & Data
- The GEDI AGBD samples are between 2019 to 2021, and field survey data across China is from the 1990s to the 2010s. How did you deal with the gap in time.
- Given that GEDI data has only been available since 2019, how to ensure the model's predictive ability for the early years (1985-2018) requires a more detailed explanation.
3. Results
- Spatial pattern of forest AGB: Figure 6 shows the spatial pattern of AGB in 2023, but it lacks corresponding diagrams for earlier years (such as 1985), making it difficult to visually compare the changes. It is suggested to supplement the AGB distribution map of key years.
- Trends in AGBD/AGC: Critique the reported increase in AGBD (95.74 to 122.69 Mg/ha) and AGC stock (5.50 to 13.97 PgC). Are these trends plausible given China’s afforestation efforts?
- Drivers of Change: Evaluate the partitioning of AGC growth (65.1% forest growth vs. 34.9% expansion). Are these proportions supported by ancillary data (e.g., land-use maps)?
4. Discussion
- You compared trends of different datasets, your data has a long time series, is there more accurate for every year? I think you’d better use field data to verify.
- Although the comparison with GEDI data showed a smaller deviation (0.06 Mg/ha), the comparison with ground survey data showed a larger negative deviation (-19.87 Mg/ha), especially in the tropical rainforest area (-68.57 Mg/ha). This systematic deviation requires more in-depth analysis and discussion.
- Although the paper discusses the influence of different canopy coverage thresholds on AGB estimation in lines 547-555, it does not quantitatively explain the degree of influence of using different thresholds (such as 10% vs. 20%) on the main conclusions of this study. It is suggested to add sensitivity analysis.
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RC2: 'Comment on essd-2025-96', Anonymous Referee #2, 30 Jul 2025
This manuscript generated a long-term 30 m Annual Aboveground Biomass data from 1985 to 2023 in China. They mainly used a residual neural networks (ResNets) together with the GEDI footprint AGBD and Landsat images to generate the AGBD map. The manuscript is generally well-written. I have several suggestions listed below, which I think may be helpful for revising the manuscript.
Materials and methods:
- Does the observations from Landsat suffers from the sensor degradation effect? Since they are merged by different sensors, it may have some impacts on the long-term trend of Landsat data, which can finally propagate to the AGBD map.
- How do you consider the risk of overfitting of your model? And the multicollinearity issue among the predictors you choose?
- How do you validate the long-term trend of AGBD you generated? Since it is the key novelty aspect of this study.
Results and discussion:
- Figure 3: from this figure, it suggests that your AGBD map is systematically lower than the observed value, right? Since the slope is lower than one, and with a negative bias value. Please explain this point.
- Figure 8: how do you validate that the temporal trend in AGBD you generated is correct or not? Could you find some ground observations to validate this?
- Figure 9: how do you separate the AGBD trend into forest expansion and forest growth? Please added some detailed process of how you did this.
- Figure 15: we can find a large difference (even two-three times larger) between your map with Hengeveld, CCI and Chen’s maps. Could you explain the underlying reason?
Citation: https://doi.org/10.5194/essd-2025-96-RC2 -
EC1: 'Comment on essd-2025-96', Zhen Yu, 02 Aug 2025
There are several products that provide high-resolution biomass and/or carbon stock information. One unique aspect of this dataset is its long-term coverage of forest carbon storage. However, based on a preliminary review, the temporal changes in carbon stock—particularly at fine spatial scales—appear to be relatively unreliable. This suggests that while the product may effectively capture spatial distribution, it is less capable of representing temporal dynamics. Please pay close attention to this issue.
Citation: https://doi.org/10.5194/essd-2025-96-EC1
Data sets
CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅵ: 2022-2023) Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang https://doi.org/10.5281/zenodo.12747329
CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅴ: 2016-2021) Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang https://doi.org/10.5281/zenodo.12742210
CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅳ: 2009-2015) Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang https://doi.org/10.5281/zenodo.12658255
CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅲ: 2002-2008) Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang https://doi.org/10.5281/zenodo.12655492
CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅱ: 1994-2001) Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang https://doi.org/10.5281/zenodo.12637101
CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅰ: 1985-1993) Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang https://doi.org/10.5281/zenodo.12620984
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