# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 1 Introduction  
As part of the Earth's hydrosphere, water resources are important resources for terrestrial life and play an important role in nature and human society. Nevertheless, surface water around the world is undergoing spatial and temporal changes caused by many factors, such as land use/cover changes, climate changes, seasonal changes, and environmental changes [1]. Meanwhile, the high-precision mapping of open water can also provide useful information for flood monitoring, assessment, and disaster relief decision-making [2], as well as for studying the relationship between the hydrological conditions and the ecological environment. Therefore, it is necessary to monitor water body changes in a timely manner and with accurate information [3]. Remote sensing technology has become indispensable in monitoring changes in water bodies due to its characteristics of high spatio-temporal coverage [4]. Even with well-defined methods and with the readily available data from optical sensors for surface water mapping applications [5], its use is hindered by clouds that obscure surface observations [6]. As an active sensor, Synthetic Aperture Radar (SAR) is particularly suitable for water body mapping, as this tool allows data acquisition regardless of illumination and weather conditions [7; 8], and SAR data are also sensitive to water bodies due to the weak backscatter from the water surface [8; 9; 10]. Over the past few decades, data from SAR operating at different wavelengths and multi-mode image configurations have been used for water body mapping, including ENVISAT-ASAR [8; 9; 11; 12; 13; 14], RADARSAT/RADARSAT-2 [15; 16; 17; 18; 19; 20; 21; 22], JERS-1/ALOS-PALSAR/ALOS2 [23; 24], and Chinese GF-3 [2]. Since the launch of Sentinel-1A on 3 April 2014, and Sentinel-1B on 25 April 2016, Sentinel-1 has become the main SAR data source for water body mapping, due to its 5-7-day revisit cycle and its free data policy [7; 25; 26; 27; 28; 29; 30; 31]. Moreover, various water body mapping methods have been developed for SAR data. As a result of the radar's unique response to water, water body mapping using intensity thresholding methods on SAR images had been extensively used [21; 22; 30; 32]. Some modified Otsu thresholding [33] methods that can automatically select a threshold for surface water extraction have also been proposed for extracting water body data [11; 22; 34; 32]. Object-based image analysis (OBIA) methods have also been proposed for extracting water body information for SAR data, taking into account the shape and color information of the segmented objects; however, OBIA methods are generally computationally expensive [8; 16; 35; 36]. Meanwhile, the time-series Sentinel-1 water index (SWI) has also been used to extract water surface areas from Sentinel-1 SAR data using the threshold method [37]. Sentinel-1 is a two-satellite constellation providing wide-swath C-Band SAR data, with data being regularly acquired every 5 to 7 days over the same place, making large-area water monitoring feasible [29]. The availability of repeated acquisitions is advantageous, since multi-temporal observations allow the detection of trends in water bodies [10; 12]. More and more researchers have used time-series wide-swath Sentinel-1 SAR data to extract water surface data automatically or semi-automatically. On the premise of obtaining water and non-water mask data, fully automatic algorithms (Bayesian based thresholding [27], Support Vector Machine (SVM) [4], and Random Forest classification [5; 38]) have been developed to derive water probability and classify the extent of surface water using Sentinel data [27]. For Sentinel-1 SAR data, some surface water automatic mapping methods have been proposed for use in rapid flood mapping and event maps with increasing resolution [25; 26; 28]. However, to monitor and analyze water body changes over a long period of time, it has become popular to remote sensing big data from Sentinel-1 SAR.