# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 4 Discussion
### Comparisons with Other Studies  
As the largest freshwater lake in China, Poyang Lake has been paid more and more attention, especially its water body changes. In previous studies, optical remote sensing data such as MODIS data and Landsat data were the primary data sources for mapping Poyang Lake water body [61; 75; 76; 77], mainly due to the free policy and long-term availability of these data. With the temporal resolution of 16 days, only 8 cloud-free Landsat TM/ETM+ data were used to retrieve the water body for Poyang Lake from November 1999 to October 2000, mainly due to the influences of clouds [75]. Similarly, the influences of clouds resulted in only 466 cloud-free MODIS products available to investigate the spatial-temporal distribution of inundation in Poyang Lake during 2000-2011 [76]. Meanwhile, SAR data were used to study water body mapping methods [2; 13; 78], and there were few long-term water body mapping based on SAR data due to its high price. In the past few years,  
Figure 18: The influences of floats and sediments on water extraction. The upper figures are for the Sentinel-1 SAR data displayed in VH/VV/(VH + VV) and the lower figures are for the classification results, with blue for water and green for non-water. (**a**) 27 May 2020, water level of 14.6 m; (**b**) 2 June 2020, water level of 16.0 m; (**c**) 8 June 2020, water level of 16.9 m.  
Sentinel-1 SAR cloud-free data have become another data source for Poyang Lake water body mapping as the high spatial-temporal resolution [1; 32; 37]. All available Sentinel-1 SAR data in GEE [1], 71 Sentinel-1A data (41 observations, 12 days temporal resolution) [37] and 15 Sentinel-1A SAR data (12 days temporal resolution) were used for Poyang Lake water body mapping or flooding monitoring [32]. Here, we collected 455 available Sentinel-1A/B SAR data (324 observations) for Poyang Lake water body mapping and constructed a consecutive high spatial (10 m resolution) and temporal (6 days temporal resolution) water body dataset, which can reflect the water body changes well especially in the flood season. The high spatial-temporal water body dataset in China (HSWDC) was constructed based on Sentinel-1 SAR data and GEE platform, which provided the monthly water dynamics of Poyang Lake [1]. HSWDC was construed using the backscatter threshold segmentation method, where pixels with \(\sigma_{VV}\leq-15\) dB and \(\sigma_{VH}\leq-23\) dB were regarded as water body, and a high classification accuracy (overall accuracy: 0.93, Kappa coefficient: 0.86) was achieved. Sentinel-1A water index (SWI) and the SWI threshold classification method were used to map Poyang Lake water body with the threshold of 0.2, and the overall accuracy and Kappa coefficient were 96.52%, 0.8981; 96.16%, 0.9102 for Poyang Lake in 27 September 2016 and 9 September 2015 [37]. However, the overall accuracy for OSTU and bimodal threshold segmentation (BTS) methods in [32] were 0.698 and 0.766, respectively. Thus, the threshold segmentation methods are efficient enough to map water body and easily deployed in GEE platform but depend only on the variance without considering the human prior knowledge [32] and the spatial relations between the pixel and its neighbors. The overall accuracy for HRNet, DenseNet121, SegNet, ResNet101, and DeepLab v3+ were 0.970 (highest precision and efficiency), 0967, 0.969, 0.966, and 0.956, which demonstrated that the CNNs appear to significantly outperform the traditional methods [32]. WaterUNet, based on U-Net, can take into account the human prior knowledge, the spatial relations [54] and learn high-level context features and then improve water body mapping. WaterUNet has the same structure as the conventional U-Net, but with 50% of convolution kernels for each layer and the reduced number of model parameters. The results show that WaterUNet can map water body with high accuracy (overall accuracy: 0.98, Kappa coefficient: 0.97) and a good generalization ability.