## 5 Discussion  
### Comparisons with Existing Datasets  
The GSWE dataset, which is based on the long-term Landsat TM, ETM, and OLI images, provides the global monthly surface water area from 1984 to 2018. However, due to the impact of clouds and the long revisit period (16 days) of Landsat satellite, GSWE cannot actually provide monthly water dynamics across most areas of China, such as in the southern China where it has a subtropical humid monsoon climate and cloudy weather occurs frequently. Figure 6 shows the monthly water dynamics of Poyang Lake (which is located in southern China) in 2018 based on the GSWE dataset and the HSWDC respectively. As expected, the GSWE cannot provide the surface water maps during January, June, and December of 2018 because of the above limitations of Landsat satellites. Furthermore, we can find GSWE only extracts part of the actual water body surface in some months, such as May, September, October, and November.  
Choosing GSWE in August 2018 as the standard, it can be seen that the water surfaces extracted by HSWDC and GSWE have high consistency (gray in Figure 7). However, due to the high spatial resolution of Sentinel-1, HSWDC can present more narrow rivers and small ponds (orange in Figure 7). However, the limitations of the SAR image itself will cause some water surfaces to be missed in  
Figure 6: Comparison of monthly water dynamics of Poyang Lake during 2018 based on the GSWE dataset (blue) and the HSWDC dataset (red).  
HSWDC (blue in Figure 7). Another possible reason for the inconsistency between the two datasets may come from the different observation date of the two satellites in the same month. The more frequent observation of waters can address this issue in the future.  
Taking Dongting Lake as a sample, we also analyze the temporal consistency between the HSWDC, the GSWE and lake water level from 9 January 2016 to 25 December 2018 with a 10-day temporal resolution. The water level from satellite altimetry comes from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, and the root mean square difference between it and in situ data ranges from 4 to 36 cm [21]. [PERSON] et al. [35] also used the water level dataset to make a comparison with the water body dataset they produced. The reason why the water area detected by HSWDC is larger than GSWE is that HSWDC has a higher spatial resolution. With the decrease of water storage, the area of some small water bodies becomes smaller, so that GSWE cannot detect them. The difference between the water surface detected by HSWDC and the water surface monitored by GSWE in winter are more significant. Since the changes in water area and elevation are inconsistent, we can only compare the trends of water extent detected by HSWDC and water level in Figure 8. The HSWDC and the water level have highly consistent seasonal variation (Pearson correlation coefficient is 0.92), while the consistency between GSWE and water level is relatively poor (Pearson correlation coefficient is 0.84) (Figure 8). The results show that the HSWDC can effectively reflect the dynamics of the water body.  
Figure 7: Comparison of Poyang Lake in August 2018 from the HSWDC and the GSWE. The region where both HSWDC and GSWE are recognized as water is shown in grey. Based on GSWE, the over-extracted water surface of HSWDC is shown in orange, and the under-extracted water surface of HSWDC is shown in blue. (**a,b**) are two local regions of Poyang Lake.