# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 1 Introduction
Many studies have deployed algorithms on Google Earth Engine (GEE) to extract water body over long periods of time [1; 39; 40], due to its data storage advantages and data processing capabilities. Since the idea of deep learning was proposed by [PERSON] [41] in 2006, deep learning has attracted more and more attention, and deep Convolutional Neural Network (CNNs) has also shown advantages in the field of image semantic segmentation [42; 43]. As commonly used models in deep learning, CNNs have become tremendously popular in recent years because CNNs can learn extremely complicated hierarchical features from massive amounts of data [42], greatly reduce the number of parameters, enhance the generalization ability, and realize the qualitative task of image recognition [44]. CNN can learn high-level context features through a large number of neurons [45], and extract features automatically, robustly in terms of image size and context among other aspects [46], which overcomes many limitations of traditional classification methods. Fully Convolutional Network (FCN) [47], SegNet [48], RefineNet [49], and U-Net [50], which include encoding and decoding blocks to perform pixel-wise image segmentation, have become the desired models for use in image segmentation tasks; in particular, FCN and U-Net are widely used in remote sensing water body extraction [51; 52; 53; 54], where the encoder is used to extract image features and reduce image dimensions, and the decoder is used to restore the size and detail of the image [55]. In U-Net, the low-resolution information in the encoding blocks (down-sampling, providing the basis for object category recognition) and the high-resolution information in the decoding blocks (up-sampling, providing the basis for accurate segmentation and positioning) are combined to provide detailed information for image segmentation. In addition, skip-connection is used to fill in the low-level information to improve the segmentation accuracy in U-Net. The compact structure of the U-Net model (small size and fewer parameters) also makes it suitable for water body extraction [46]. As the largest freshwater lake in China and its wetland as one of the most important in the world, Poyang Lake is in the middle reaches of the Yangtze River and plays an important role in the ecosystem of the Yangtze River watershed. It is a typical river-connected lake, and the water body of the lake experiences a remarkable seasonal variation due to the influence of the monsoon climate of the region. However, the Three Gorges Dam operation has altered the seasonal inundation pattern of Poyang Lake and significantly reduced the monthly inundation frequencies [56; 57]. The fluctuation of the water body directly or indirectly affects the ecological environment of Poyang Lake--e.g., the distribution of vegetation communities [58; 59] and the spatio-temporal variation in Siberian Crane habitats [60]. Meanwhile, serious floods occasionally occur in the Poyang Lake--e.g., in 1998 and 2020. Thus, changes in the water body of Poyang Lake have always attracted the attention of researchers [29; 40; 61; 62]. Defining an accurate and reliable method for monitoring water body changes, studying the relationship between water body surface area and water level, and studying wetland environment changes using Sentinel-1 SAR data are a challenge. Monitoring models using deep neural networks provide an ideal solution for monitoring water bodies. Here, in order to explore the potential of the use of Sentinel-1 in water body mapping and to study the relationship between the water area and water level for Poyang Lake, long-time-series Sentinel-1 SAR data were collected to extract water body information using the U-Net deep learning model. Water level data were also collected from Poyang gauging station.