GEOXYGEN: a global long-term dissolved oxygen dataset based on biogeochemistry-aware machine learning framework and multi-source observations

Wang, Zhenguo; Fu, Weiwei; Xue, Cunjin; Wang, Guihua

doi:10.5194/essd-18-3125-2026

Articles | Volume 18, issue 5

https://doi.org/10.5194/essd-18-3125-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-18-3125-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 5

Data description article

|

12 May 2026

Data description article |

| 12 May 2026

GEOXYGEN: a global long-term dissolved oxygen dataset based on biogeochemistry-aware machine learning framework and multi-source observations

Zhenguo Wang, Weiwei Fu, Cunjin Xue, and Guihua Wang

Data sets

GEOXYGEN: A global monthly gridded dissolved oxygen product (0.5° × 0.5°) Zhenguo Wang et al. https://doi.org/10.5281/zenodo.19703198

Model code and software

GEOXYGEN_Code Zhenguo Wang https://doi.org/10.5281/zenodo.19852901

Short summary

Ocean oxygen is vital for marine life and climate, but long records are uneven. We combine nearly one million ship and autonomous float measurements with careful quality control and machine learning to create GEOXYGEN, a monthly global map of dissolved oxygen from 1960–2024 with high spatial detail and full-depth coverage. It reveals broad long-term oxygen changes and offers a consistent basis for studies of ocean deoxygenation and climate impacts.