Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2907-2026
© Author(s) 2026. 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-18-2907-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
27 Apr 2026
Data description article |
| 27 Apr 2026
| 27 Apr 2026
CCAV-10m: an annual spatiotemporal dataset for eastern coastal China’s wetland vegetation by integrating Sentinel-1/2 observations via deep learning
Yuying Li
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
Zijiang Song
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
Shuang Yang
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
Zilong Zhu
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
Min Liu
CORRESPONDING AUTHOR
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai 200241, China
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Short summary
We introduce CCAV-10m, an annual, species-level 10m resolution coastal wetland vegetation dataset for eastern China. It was produced using a novel phenology-guided vegetation classification network that integrates Sentinel-1/2 satellite imagery with field observations. Validation demonstrates strong classification accuracy (overall accuracy: 0.916, Kappa: 0.898). Regular updates will support long-term coastal wetland research, invasive species monitoring, and precision wetland management.
We introduce CCAV-10m, an annual, species-level 10m resolution coastal wetland vegetation...
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