## 4 Results and Comparisons with Other Surface Water Products
### Evaluation of the SAR NN Classification Method  
Figure 9 shows the results of the classification applied over the Tonle Sap Lake in Cambodia (top) and over the Mekong river in Vietnam (bottom), in February 2016. Figure 9a,d show the SAR-predicted surface water maps, Figure 9b,e present the reference Landsat-8 surface water maps, whereas the differences between these two surface water maps are shown in Figure 9c,f. Over the Tonle Sap Lake, both Sentinel and Landsat images were acquired on the same day (3 February 2016). The spatial correlation between the two surface water maps is 94%. The confusion matrix for this area is given in Table 7 (left). Overall accuracy of the classification is 99%, with a true positive water detection of 93.5%, and a false negative percentage of 6.5%. The classification correctly detects more than 99.6% of non-water pixels compared to the reference map. The classification slightly underestimates the surface water coverage by \(\sim\)2.5%. This is 961 km\({}^{2}\) compared to the reference surface water map derived from the Landsat-8 images of 986 km\({}^{2}\). The second case study is carried out over the Mekong river and its surrounding areas (latitude range [10.8\({}^{\circ}\)N-11.8\({}^{\circ}\)N] and longitude range [104.6\({}^{\circ}\)E-105.6\({}^{\circ}\)E]). The optical Landsat-8 images were taken on 19 February 2016 and the SAR Sentinel-1 images were taken 3 days later, on 22 February 2016. These Sentinel and Landsat images were not acquired on the same day, but within 3 days in the middle of the dry season when land surfaces in this area are not expected to change much. Similar to the first case study, the classification works well, even though the environment here is rather complex, with rivers and vegetated wetlands. The overall accuracy is 98.8%, with a spatial correlation of nearly 82% \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **BS\_VH** & **BS\_VV** & **STD\_VH** & **STD\_VV** & **ANGLE** \\ \hline
**BS\_VH** & 100\% & & & & \\
**BS\_VV** & 84\% & 100\% & & & \\
**STD\_VH** & 24\% & 20\% & 100\% & & \\
**STD\_VV** & 21\% & 21\% & 88\% & 100\% & \\
**ANGLE** & 25\% & 22\% & 11\% & 6\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Linear correlations among the five potential NN inputs. with the Landsat-8 reference surface water map. Confusion matrix for this area is shown in Table 7 (right) where the true positive percentage is 85.7%, the false negative percentage 14.3%, and 99.2% of non-water pixels are classified correctly. The total surface water area derived from Landsat data is 325 km\({}^{2}\), and it is 355 km\({}^{2}\) predicted from the NN. The same results are found when applying the NN classification to other areas. To conclude this comparison, the proposed NN methodology correctly detected \(\sim\)90% of the water pixels observed by Landsat-8, with a spatial correlation of \(\sim\)90%. The NN works better over open water bodies than over other heterogeneous environments. For instance, the NN has difficulties detecting small river branches (Southeast of the Tonle Sap Lake in Figure 9--top panel) although they are clearly detected with Landsat-8 images. The NN can provide water maps with high accuracy compared to the reference Landsat-8 water maps; there are differences between them. Errors could come from the following factors: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Tonle Sap Lake** & & & **Mekong River** & \\ \hline  & **Non-water(0)** & **Water(1)** & & **Non-water(0)** & **Water(1)** \\  & **(Predicted)** & **(Predicted)** & & **(Predicted)** & **(Predicted)** \\ \hline
**Non-water(0)** & 11,641,078 & 44,493 & **Non-water(0)** & 10,983,583 & 85,096 \\ (Actual) & (99.6\%) & (0.4\%) & (Actual) & (99.2\%) & (0.8\%) \\ \hline
**Water(1)** & 71,884 & 1,023,457 & **Water(1)** & 51,611 & 309,982 \\ (Actual) & (6.5\%) & (93.5\%) & (Actual) & (14.3\%) & (85.7\%) \\ \hline \hline \end{tabular}
\end{table}