Timely and accurate large-scale water body mapping and dynamic monitoring are of great significance for water resource planning, flood control, and disaster reduction applications. Synthetic aperture radar (SAR) systems have the characteristics of strong operability, wide coverage, and all-weather data availability, and play a key role in large-scale water monitoring applications. However, there are still some challenges in the application of highly efficient, high-precision water extraction and dynamic monitoring methods. In this paper, a framework for the automatic extraction and long-term change monitoring of water bodies is proposed. First, a multitemporal water sample dataset is produced based on the bimodal threshold segmentation method. Second, attention block and pyramid module are introduced into the UNet (encoder-decoder) model to construct a robust water extraction network (PA-UNet). Then, GIS modeling is used for the automatic postprocessing of the water extraction results. Finally, the results are mapped and statistically analyzed. The whole process realizes end-to-end input and output. Sentinel-1 data covering Dongting Lake and Poyang Lake are selected for water extraction and dynamic monitoring analysis from 2017 to 2020, and Sentinel-2 images from a similar time frame are selected for verification. The results show that the proposed framework can realize high-precision (the extraction accuracy is higher than 95%), highly efficient automatic water extraction. Multitemporal monitoring results show that Dongting Lake and Poyang Lake fluctuate most in April, July, and November in 2017, 2019, and 2020, and the change trends of the two lakes are the same.

中文翻译：

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基于自动取水和动态监测框架的洞庭湖和Po阳湖多时相取水

及时准确的大规模水体测绘和动态监测对水资源规划，防洪减灾应用具有重要意义。合成孔径雷达（SAR）系统具有可操作性强，覆盖范围广和全天候数据可用性的特点，并且在大规模水监测应用中起着关键作用。然而，在高效，高精度的水提取和动态监测方法的应用中仍然存在一些挑战。本文提出了一种用于水体自动提取和长期变化监测的框架。首先，基于双峰阈值分割方法产生了一个多时相水样数据集。第二，注意模块和金字塔模块被引入到UNet（编码器-解码器）模型中，以构建强大的水提取网络（PA-UNet）。然后，将GIS建模用于水提取结果的自动后处理。最后，对结果进行映射并进行统计分析。整个过程实现了端到端的输入和输出。从2017年到2020年，选择涵盖洞庭湖和covering阳湖的Sentinel-1数据进行水提取和动态监测分析，并选择相似时间范围内的Sentinel-2图像进行验证。结果表明，所提出的框架可以实现高精度（提取精度高于95％），高效的自动水提取。多时相监测结果显示，洞庭湖和Po阳湖在4月，7月波动最大，