Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
24 Jun 2025
Data description article |
| 24 Jun 2025
| 24 Jun 2025
A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021
Die Hu
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
Yuan Wang
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Han Jing
Energy Administration of Yunnan Province, Yunnan, China
Linwei Yue
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Qiang Zhang
Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, China
Key Laboratory of Polar Environment Monitoring and Public Governance, Ministry of Education, Wuhan University, Wuhan, China
Huanfeng Shen
School of Resource and Environmental Science, Wuhan University, Wuhan, China
Liangpei Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
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Short summary
Existing L-band vegetation optical depth (L-VOD) products suffer from data gaps and coarse resolution of historical data. Therefore, it is necessary to integrate multi-temporal and multisource L-VOD products. Our study begins with the reconstruction of missing data and then develops a spatiotemporal fusion model to generate global daily seamless 9 km L-VOD products from 2010 to 2021, which are crucial for understanding the global carbon cycle.
Existing L-band vegetation optical depth (L-VOD) products suffer from data gaps and coarse...
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