###### Abstract  
Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km\({}^{2}\) and 3729.19 km\({}^{2}\) during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km\({}^{2}\) (with a change rate of 35%) and 197.58 km\({}^{2}\) (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake. Poyang Lake; Sentinel-1A; Landsat; water surface areas; remote sensing 2017 9 521 10.3390/rs9060521 10.  
and low cost [6; 13; 14; 15; 16; 17; 18]. Optical remote sensing is a frequently used dataset for dynamic monitoring of changes in water surface areas due to its availability. Changes in Poyang Lake have been monitored via the moderate-resolution imaging method of spectroradiometer (MODIS) imagery at monthly and yearly intervals from 2000 to 2010 [6]. However, MODIS spatial resolution (250 m/500 m) cannot delineate the detailed outline of lakes because of the mixed pixel problem [6]. Landsat datasets have been used to monitor changes in lake distributions on the Mongolian Plateau [2]. However, due to the influence of clouds, only inter-annual changes were observed. It is difficult for optical remote sensing satellites to provide enough data for dynamic monitoring of changes in water surface areas of Poyang Lake. This is especially true for medium or high spatial resolution remote sensing, which has a longer return cycle because the Poyang Lake water surface areas experience rapid and significant short-term (one or two weeks) fluctuations. Although several methods have been introduced to fill the gaps in medium or high spatial resolution remote sensing data by combination with high temporal remote sensing data, these methods cannot fill all the gaps because of the issues related to cloud cover [19; 20; 21; 22]. Conversely, synthetic aperture radar (SAR) remote sensing has the advantages of being an all-weather and all-time tool [23; 24]. In particular, the open-access Sentinel-1A SAR imagery (S1A) has high spatiotemporal resolution (return cycle of 12 days and imagery pixel spacing of 10 m) [25] and great potential for the monitoring of these dynamic changes in water surface areas. [PERSON] et al. [26] detected the hydrological dynamics of a coastal marsh located in the Regional Natural Park (close to the French Atlantic coastline) using the threshold method with S1A data, producing an overall accuracy of 82%. Radarsat-2 and ENVISAT imagery were also utilized to determine the extent of flooding in the upper Zambezi basin in southern Africa using the threshold method with an estimated error of 8-23% compared to the classification results of Landsat-8 [24]. However, it is a difficult and complex task to determine appropriate thresholds in different periods and regions since the applicability of the threshold is unknown. Moreover, the accuracy of the above study does not meet the needs of investigating dynamic changes in Poyang Lake.