Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy
(2024)
Journal Article
Hao, Z., Cai, X., Ge, Y., Foody, G., Li, X., Yin, Z., Du, Y., & Ling, F. (2024). Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy. Journal of Hydrology, 640, Article 131673. https://doi.org/10.1016/j.jhydrol.2024.131673
The distribution of land surface water bodies is constantly changing. Monitoring these changes is critical for both humanity and the ecological system. The Joint Research Centre Global Surface Water (GSW) dataset is crucial in monitoring global water... Read More about Resolving data gaps in global surface water monthly records through a self-supervised deep learning strategy.