Abstract
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.
Highlights
Water body extent mapping is an important research topic in the field of lake change and flood disaster monitoring
To further illustrate the performance of the proposed method, Sentinel-2 optical images of Dongting Lake and Poyang Lake in November 2020 were obtained as references
The acquisition date of the Sentinel-2 image of Dongting Lake is 2 days different from the Sentinel-1 image, while the acquisition dates of the Sentinel-2 image and Sentinel-1 image of the Poyang
Summary
Water body extent mapping is an important research topic in the field of lake change and flood disaster monitoring. The emergence of remote sensing has provided an advanced technical means for flood information acquisition. With the increase in remote sensing data sources, the research and application of using optical sensor data to obtain surface water information have been increasingly developed [2,3,4,5,6,7,8]. The availability of optical data is limited by rainy weather during flood periods. Synthetic aperture radar (SAR) data can be acquired all day and under all weather conditions, and the backscattering value of water bodies in SAR images is low, making it separable from other ground objects [9]. It can be used to address the dynamic monitoring challenges present under complex weather conditions and realize the near real-time monitoring of flood expansion and extinction time series
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