## 4 Results
### Comparison of Water Area Extracted Using Different Method and Different Data  
Table 3 shows that the quantity accuracy and overall accuracy of water extracted from SWI imagery was higher than that of the original imagery of S1A using the four methods based on the ground-truth water area that was extracted from OLI acquired on 27 September 2016.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Category Method**} & \multirow{2}{*}{**Data**} & \multicolumn{6}{c}{**Accuracy of Water (\%)**} & \multicolumn{2}{c}{**Running**} \\ \cline{3-8}  & & **QA** & **OA** & **UA** & **PA** & **Kappa** & **Time (s)** \\ \hline \multirow{3}{*}{Threshold Classification Method (TCM)} & SWI & 98.84 & 96.52 & 92.58 & 91.51 & 0.90 & 2 \\  & VH & 93.02 & 94.50 & 85.07 & 91.01 & 0.84 & 2 \\  & VV & 94.38 & 95.99 & 93.35 & 88.10 & 0.88 & 2 \\  & VH and VV & 87.05 & 94.52 & 83.25 & 94.03 & 0.85 & 3 \\ \hline \multirow{3}{*}{IsoData Unsupervised Classification (IUC)} & SWI & 98.01 & 96.55 & 93.03 & 91.18 & 0.90 & 4 \\  & VH & 46.01 & 87.39 & 63.89 & 98.38 & 0.69 & 4 \\  & VV & 79.37 & 92.57 & 77.48 & 93.46 & 0.80 & 4 \\  & VH and VV & 72.27 & 92.27 & 75.40 & 96.31 & 0.80 & 5 \\ \hline \multirow{3}{*}{Minimum Distance Classification (MDC)} & SWI & 96.38 & 96.59 & 93.85 & 90.45 & 0.90 & 3 \\  & VH & 82.07 & 93.58 & 80.04 & 94.40 & 0.82 & 3 \\ \cline{1-1}  & VV & 94.78 & 95.15 & 87.06 & 91.61 & 0.86 & 3 \\ \cline{1-1}  & VH and VV & 96.66 & 96.06 & 89.74 & 92.73 & 0.89 & 3 \\ \hline \multirow{3}{*}{Support Vector Machine (SVM)} & SWI & 99.57 & 96.42 & 91.69 & 92.08 & 0.90 & 13 \\  & VH & 97.11 & 94.58 & 86.65 & 89.16 & 0.84 & 193 \\ \cline{1-1}  & VV & 94.63 & 95.13 & 86.96 & 91.63 & 0.86 & 52 \\ \cline{1-1}  & VH and VV & 97.84 & 96.19 & 90.49 & 92.44 & 0.89 & 18 \\ \hline \end{tabular}  
* QA = quantity accuracy; OA = overall accuracy; UA = user’s accuracy; PA = producer’s accuracy. The test zone location in the Poyang Lake region with an area of 110 km \(\times\) 70 km. The ground-truth water area was extracted from OLI acquired on 27 September 2016. The last column shows the time needed to extract the water extent using different methods based on the same hardware. The unit is seconds.  
\end{table}
Table 3: The accuracy of water extraction using different methods and datasets acquired on 27 September 2016.  
Figure 7: The number of lakes with different areas that constitute the Poyang Lake. The black line represents the lakes with an area of 0.1–1 km\({}^{2}\), the red line represents the lakes with an area of >1–10 km\({}^{2}\), and the blue line represents the lake with area of >10–100 km\({}^{2}\). The background color is used to distinguish the rainy season and dry season: the light green color represents the rainy season, and the light yellow color represents the dry season.  
The water surface areas of Poyang Lake extracted from OLI and SWI are plotted in Figure 8. There were only eight OLI scenes that were cloud-free from 24 May 2015 to 14 November 2016 (Figure 8), and many variation characteristics of the water were not detected by OLI compared with the results from the SWI images.  
Water extracted from MODIS on 27 September 2016 is shown in Figure 9b. The classification accuracy was assessed based on the ground-truth water area from OLI. The quantity accuracy was 67.56% (omission 537 km\({}^{2}\)). The overall accuracy was 88.96% (kappa coefficient 0.64) and the producer's accuracy was only 58.71%, which indicates that 41.29% of the water was omitted (see Figure 9c). The quantity accuracy and overall accuracy from the SWI image were 31.28% and 7.56% higher, respectively, than that from the MODIS image.  
Figure 8: Water surface area extracted from SWI and OLI from 24 May 2015 to 14 November 2016.  
Figure 9: MODIS classification accuracy based on OLI.