# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 5 Conclusions  
Timely water monitoring, especially flood dynamic monitoring, is very important for water resource regulation, disaster assessment, and disaster mitigation. Due to its high temporal-spatial resolution, Sentinel-1 SAR is very suitable for detecting water as well as emergency flood mapping. As the largest freshwater lake in China, Poyang Lake's water area variation can reveal the occurrence of flooding in the upstream area of its lake basin and the Yangtze River. In order to monitor the change in the water area, we collected 455 scenes of Sentinel-1 SAR data from 2014 to 2021.  
In this study, we provide a multi-temporal water body extraction framework for Sentinel-1 SAR data, and the SNAPPY and Google Tensorflow frameworks used in the proposed framework make it easy to implement and transfer at the code level. The data clipping method enhances the proposed framework. Basically, this framework relies on the scattering characteristics of water bodies, and the stability of water body scattering also ensures that it is applicable in other lakes. The preliminary result shows that the WaterUNet model realized accurate water extraction, with an F1 score of up to 0.99. The water area variation trend shows that Poyang Lake enters a flood period in July. In 2015, 2016, 2017, 2019, and 2020, the water areas in the study area were over 2500 km\({}^{2}\), and they reached 2714.08 km\({}^{2}\) in 2020. The IF distribution map for the Sentinel-1 SAR data clearly shows the seasonal water area, perennial water area, and non-water area, as well as the water tanks and water transfer channels in Poyang Lake. The IF distribution maps for different years and months show the interannual and annual variation in the water area. The IF distribution map for 2020 shows that a severe flood occurred in July, and the IF distribution maps for different months show the obvious wet season that occurs from April to September. The quadratic polynomial fitting results show that there is a good correlation between the water level from Poyang gauging station and the water area, with an \(R^{2}\) of up to 0.88, which can be used to estimate the water area or the water level of Poyang Lake.  
Due to the large amount of Sentinel-1 SAR data, data pre-processing takes a long time. Thus, in the future, we will try to transfer our framework to the GEE platform to reduce the pre-processing time for the Sentinel-1 SAR data and provide rapid emergency response services.  
**Author Contributions:** [PERSON] proposed and designed the technique roadmap, contributed to the creation of the algorithmic methods, and collected the image data. [PERSON] proposed the technique roadmap and helped with the results analysis. [PERSON] and [PERSON] helped with the results analysis and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070102).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Sentinel-1 SAR data listed in Table A1 are available in https:// sci-hub.copernicus.eu/ (accessed on 18 April 2021). The other data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors are grateful to ESA for providing the Sentinel-1 SAR data and [[http://www.jsssw.gov.cn/](http://www.jsssw.gov.cn/)]([http://www.jsssw.gov.cn/](http://www.jsssw.gov.cn/)) (accessed on 1 July 2021) website for providing the water level data. We would like to express thanks to the anonymous reviewers for their voluntary work and the constructive comments to improve this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.