CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅰ: 1985-1993)

The China Annual Forest Aboveground Biomass Time-Series Dataset (CFATD) is the first comprehensive, high-resolution annual dataset specifically developed for monitoring forest aboveground biomass (AGB) across China. Covering the period from 1985 to 2023, this dataset was generated using a combination of multi-source remote sensing data and deep learning techniques, including residual neural networks. CFATD provides 30-meter resolution annual AGB density (AGBD) estimates, enabling a detailed assessment of spatiotemporal biomass dynamics in China’s forests. CFATD is publicly available on the Google Earth Engine (GEE) platform, with updates planned annually. The dataset can be accessed via the following link:👉 https://code.earthengine.google.com/4f8ad8d32ddb84e826e941a95f31f9be Users can explore both AGBD estimates and the corresponding uncertainty layers (available only via GEE), supporting in-depth analysis of forest biomass trends and dynamics. Please note that uncertainty estimates are not included in the Zenodo repository. The unit of CFATD is megagrams per hectare (Mg/ha). This is a density value, already normalized per hectare, so no further conversion based on pixel area (900 m²) is required when interpreting per-pixel values. Each pixel represents the AGBD of the forest within that 30 m × 30 m grid cell. However, to compute total biomass or carbon stock, AGBD values should be multiplied by the corresponding forest area (e.g., pixel area × tree cover fraction), following the approach outlined in our related publication (Cai et al., essdd, 2025). The tree cover dataset used in these calculations, the China Annual Tree Cover Dataset (CATCD), is available from our peer-reviewed article:👉 https://doi.org/10.1016/j.isprsjprs.2024.08.001This article provides both GEE access and offline download links for CATCD. Important note: The dataset may contain a small number of unusually high AGB values in certain regions (e.g., >1500 Mg/ha), which may reflect outliers or model artifacts rather than actual forest conditions. While these values occur only in a very limited portion of the dataset, users are advised to apply appropriate filters or quality control procedures when conducting sensitive analyses. These issues will be addressed in future dataset updates. Citation:Cai, Y., Zhu, P., Li, X., Liu, X., Chen, Y., Shen, Q., Xu, X., Zhang, H., Nie, S., Wang, C., Wang, J., Li, B., Wu, C., and Zhuang, H.: Dynamics of China’s Forest Carbon Storage: The First 30 m Annual Aboveground Biomass Mapping from 1985 to 2023, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-96, in review, 2025. For data-related inquiries, please contact:Dr. Yaotong Cai ([e-mail])中国森林年际地上生物量时间序列数据集（China Annual Forest Aboveground Biomass Time-Series Dataset, 简称CFATD）是全球首个专为中国全境森林地上生物量（aboveground biomass, 简称AGB）监测开发的综合高分辨率年际数据集。该数据集覆盖1985年至2023年的时间范围，采用多源遥感数据与深度学习技术（包括残差神经网络）融合生成。CFATD提供30米分辨率的年际地上生物量密度（aboveground biomass density, 简称AGBD）估算结果，可支撑中国森林生物量时空动态的精细化评估。 CFATD可在谷歌地球引擎（Google Earth Engine, 简称GEE）平台公开获取，计划每年进行更新。用户可通过以下链接访问该数据集：👉 https://code.earthengine.google.com/4f8ad8d32ddb84e826e941a95f31f9be 用户可通过平台查看AGBD估算结果及对应的不确定性图层（仅可通过GEE获取），以开展森林生物量趋势与动态的深入分析。需注意，Zenodo仓储中未包含不确定性估算结果。 CFATD的单位为每吨每公顷（megagrams per hectare, 简称Mg/ha）。该值为密度单位，已按公顷完成归一化，因此在解析单像素数值时，无需再基于像素面积（900平方米）进行转换。每个像素代表该30米×30米网格单元内森林的AGBD。但若需计算总生物量或碳储量，则需将AGBD数值乘以对应的森林面积（例如像素面积×林木覆盖占比），具体方法可参考相关研究论文（Cai等，essdd，2025）。 本计算中使用的林木覆盖数据集为中国年际林木覆盖数据集（China Annual Tree Cover Dataset, 简称CATCD），可通过我们的同行评议论文获取：👉 https://doi.org/10.1016/j.isprsjprs.2024.08.001。该论文同时提供了CATCD的GEE访问与离线下载链接。 重要提示：本数据集在部分区域可能存在少量异常偏高的AGB数值（例如>1500 Mg/ha），此类数值大概率为异常值或模型伪影，而非真实的森林状况。尽管此类数值仅占数据集的极小部分，但用户在开展敏感性分析时，建议采用适当的滤波或质量控制流程。相关问题将在未来的数据集更新中予以修正。 引用格式：Cai, Y., Zhu, P., Li, X., Liu, X., Chen, Y., Shen, Q., Xu, X., Zhang, H., Nie, S., Wang, C., Wang, J., Li, B., Wu, C., Zhuang, H.：中国森林碳储量动态：1985-2023年首个30米分辨率年际地上生物量制图，《地球系统科学数据讨论》（预印本），https://doi.org/10.5194/essd-2025-96，已投稿待审，2025。 若有数据相关咨询，请联系：蔡尧通博士（邮箱：[e-mail]）