Yuan Qiwang, Wang Xufeng, Che Tao, Li Jun
School of Computer Science, China University of Geosciences, Wuhan, 430078, China.
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China.
Sci Data. 2025 Aug 5;12(1):1359. doi: 10.1038/s41597-025-05672-8.
We developed a global carbon flux dataset, GloFlux, using a machine learning model that integrates in situ observations from FLUXNET, AmeriFlux, ICOS, JapanFlux2024, and HBRFlux with satellite remote sensing and meteorological data. The dataset covers 2000-2023, has a 0.1 × 0. 1 spatial resolution, and monthly temporal resolution. It includes three key variables: Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), and Ecosystem Respiration (RECO). Validation at independent flux sites not used in model training shows strong performance at the site level, with correlation coefficients of 0.84 for GPP, 0.66 for NEE, and 0.80 for RECO. The spatiotemporal patterns of GloFlux align well with existing datasets such as FLUXCOM and MODIS, supporting the reliability and robustness of the product. GloFlux offers a valuable resource for assessing global vegetation dynamics and understanding ecosystem responses to climate change.
我们利用机器学习模型开发了一个全球碳通量数据集GloFlux，该模型将来自FLUXNET、AmeriFlux、ICOS、JapanFlux2024和HBRFlux的原位观测数据与卫星遥感和气象数据整合在一起。该数据集涵盖2000 - 2023年，空间分辨率为0.1×0.1，时间分辨率为月度。它包括三个关键变量：总初级生产力（GPP）、生态系统净交换量（NEE）和生态系统呼吸量（RECO）。在模型训练中未使用的独立通量站点进行的验证显示，在站点层面表现良好，GPP的相关系数为0.84，NEE为0.66，RECO为0.80。GloFlux的时空模式与FLUXCOM和MODIS等现有数据集高度吻合，支持了该产品的可靠性和稳健性。GloFlux为评估全球植被动态和理解生态系统对气候变化的响应提供了宝贵资源。
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