# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 3 Results and Discussion
### Validation  
The results of the IEW detection algorithm in 2016 were validated with a SPOT 7 image. A subsection of the area can be seen in Figure 12. Visual inspection shows that the darker colors in the SPOT natural color composite were similar in the Sentinel-2 color composite. Inland excess water was mainly detected in the deepest parts of the inundations (true positive, green in the central map). Figure 10: Distribution of detected IEW by algorithm for the total study area in 2018. Figure 9: Distribution of detected inland excess water (IEW) by algorithm for a selected area in 2018. Many pixels along the border of the inundations were not designated as water by the algorithm (false negative). Rarely pixels were wrongly designated as water (false positive). The quantification of the validation was based on a pixel-by-pixel comparison between the SPOT image and the algorithm result and shows that the overall accuracy was high, but [PERSON]'s kappa was low due to a very high omission error (Table 2). This is due to the fact that if one IEW patch was missed in the classification, a large number of pixels were misclassified and added to the omission error. Also, the classification is unbalanced due to the high number of \"no water\" compared to \"water\" pixels. The IEW inundations delineated for the 2018 period were validated using the high-resolution orthophoto. From the orthophoto mosaic, 849 inland excess water patches covering 147.5 ha were digitized; however, the presented algorithm only delineated 52 polygons (8.9 ha). As can be seen in \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**Reference**} & \\ \cline{3-10}  & **\# Pixels** & **No Water** & **Water** & **Total** & **Producer’s Acc.** & **Omission Error** & **User’s Acc.** & **Commission Error** \\ \hline \multirow{4}{*}{Detected} & no water water & 4,454,908 & 7807 & 4,462,715 & 99.92 & 0.08 & 99.83 & 0.17 \\  & water & **3105** & 4,513.5 & 8007 & 36.63 & 63.37 & 56.36 & 43.64 \\ \cline{1-1}  & no data & 118 & 0 & 118 & Overall Acc. & 99.74 & Kappa & 0.44 \\ \cline{1-1}  & Total & 4,458,520 & 12,320 & 4,470,840 & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy assessment on the validation site in 2016. The color of the cells refers to the colors in Figure 12.  
Figure 11: Aggregated maps of absolute (**left**) and relative (**right**) areas of inundations per administrative inland excess water defense area in the 13 th week of 2018. Figure 12: Results of the cross-validation on a selected area in 2016 (**A**—input Sentinel-2 data, **B**—result assessment, **C**—validation of SPOT 7 data). Figure 13, large inundations that were digitized on the aerial photograph can hardly be seen on the Sentinel-2 image and were also omitted by the algorithm. Like in 2016, the deep/dark water patches were delineated almost perfectly. The calculated accuracy and error values for the water class are shown in Table 3. They prove that the applied method indicated the occurrences of inundations to the user properly in almost every case (user accuracy was over 90%); however, the ratio of false negative cases was very high (omission error was over 90%). The difference between IEW identified by the algorithm and the reference data sets can also be caused by the difference in acquisition dates. The aim of the algorithm was to create weekly IEW maps, as earlier research showed that combining multiple daily results gives more accurate IEW maps [10]. The reference SPOT data and aerial photographs were from a single moment in time, and because IEW is a fast-changing phenomenon, differences in acquisition date of several days can lead to changes in the IEW patches. This is particularly characteristic for the boundaries of the patches where the water infiltrated or evaporated in case of reduced IEW, or when the size of a patch increased due to extra water. This makes the validation of the weekly maps with single date reference data more difficult.