Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation
Abstract 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 r...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05672-8 |
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| author | Qiwang Yuan Xufeng Wang Tao Che Jun Li |
| author_facet | Qiwang Yuan Xufeng Wang Tao Che Jun Li |
| author_sort | Qiwang Yuan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f8e0500c3c1944aea4e251f4b758f97c |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-f8e0500c3c1944aea4e251f4b758f97c2025-08-20T03:04:18ZengNature PortfolioScientific Data2052-44632025-08-0112111510.1038/s41597-025-05672-8Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observationQiwang Yuan0Xufeng Wang1Tao Che2Jun Li3School of Computer Science, China University of GeosciencesKey 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 SciencesKey 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 SciencesSchool of Computer Science, China University of GeosciencesAbstract 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.https://doi.org/10.1038/s41597-025-05672-8 |
| spellingShingle | Qiwang Yuan Xufeng Wang Tao Che Jun Li Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation Scientific Data |
| title | Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation |
| title_full | Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation |
| title_fullStr | Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation |
| title_full_unstemmed | Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation |
| title_short | Global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation |
| title_sort | global carbon flux dataset generated by fusing remote sensing and multiple flux networks observation |
| url | https://doi.org/10.1038/s41597-025-05672-8 |
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