# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 4 Conclusions  
The presented algorithm is capable of detecting inland excess water on very large areas based on a combination of active and passive satellite images. The base data are freely available and can be downloaded with a high temporal resolution. The workflow is fully automated, so no human \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference**} & \multicolumn{4}{c}{**Detected**} \\ \hline \multirow{3}{*}{**Category**} & \# & \multirow{3}{*}{**Area**} & \# & \multirow{3}{*}{**Area**} & \multicolumn{3}{c}{**Polygons**} & \multicolumn{3}{c}{**Area**} & \# & \multirow{3}{*}{**Area**} \\  & & & & & & & & & & **\% of** & **(m\({}^{2}\))** & **\% of** & **(\%)** & **(\%)** \\ \cline{1-1}  & \multirow{2}{*}{**Polygons**} & \multirow{2}{*}{**(m\({}^{3}\))**} & \multirow{2}{*}{**(\%)**} & \multirow{2}{*}{**(\%)**} & \multirow{2}{*}{**(\%)**} & \multirow{2}{*}{**(\%)**} & \multirow{2}{*}{**Ref.**} & & & & & \\ \cline{1-1}  & & & & & & & & & & & & \\ \hline \(<\)1000 m\({}^{2}\) & 399 & 148,668 & 71.4 & 12.1 & 177 & 44.4 & 47,136 & 31.7 & 72.0 & 5.9 \\ \(1000\) m\({}^{2}\)–1 ha & 143 & 405,829 & 25.6 & 32.9 & 59 & 41.3 & 228,774 & 56.4 & 24.0 & 28.6 \\ \(1\) ha–2 ha & 7 & 93,453 & 1.3 & 7.6 & 5 & 71.4 & 75,057 & 80.3 & 2.0 & 9.4 \\ \(2\) ha–5 ha & 5 & 156,609 & 0.9 & 12.7 & 2 & 40.0 & 59,145 & 37.8 & 0.8 & 7.4 \\ \(5\) ha\(<\) & 5 & 427,619 & 0.9 & 34.7 & 3 & 60.0 & 391,194 & 91.5 & 1.2 & 48.8 \\ Total & 559 & 1,232,177 & 100 & 100 & 246 & 44.0 & 801,306 & 65.0 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the reference and the detected water polygons on the validation site in 2016. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference**} & \multicolumn{4}{c}{**Detected**} \\ \hline \multirow{3}{*}{**Category**} & \# & \multirow{3}{*}{**Area**} & \# & \multirow{3}{*}{**Area**} & \multicolumn{3}{c}{**Polygons**} & \multicolumn{3}{c}{**Area**} & \# & \multirow{3}{*}{**Area**} \\  & & & & & & & & & & **\% of** & **(m\({}^{2}\))** & **\% of** & **(\%)** & **(\%)** \\ \hline \(<\)1000 m\({}^{2}\) & 703 & 168,423 & 82.8 & 11.4 & 39 & 5.5 & 8414 & 5 & 75.0 & 9.4 \\ \(1000\) m\({}^{2}\)–1 ha & 126 & 403,596 & 14.8 & 27.3 & 11 & 8.7 & 33,975 & 8 & 21.2 & 38.1 \\ \(1\) ha–2 ha & 12 & 170,725 & 1.4 & 11.6 & 1 & 8.3 & 17,113 & 10 & 1.9 & 19.2 \\ \(2\) ha–5 ha & 4 & 109,980 & 0.5 & 7.5 & 1 & 25.0 & 29,722 & 27 & 1.9 & 33.3 \\ \(5\) ha\(<\) & 4 & 623,258 & 0.5 & 42.2 & 0 & 0.0 & 0 & 0 & 0.0 & 0.0 & 0.0 \\ Total & 849 & 1,475,982 & 100 & 100 & 52 & 6.1 & 89,224 & 6 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the reference and the detected water polygons on the validation site in 2018.  
intervention is needed to generate the weekly IEW maps. Two IEW periods and an area sensitive to the phenomenon have been selected to demonstrate and validate the methodology, but the algorithm can be applied to any area affected by IEW floods. Clearly, the quantitative validation shows that, compared to the validation data of the high-resolution satellite data and aerial photographs, the weekly IEW detection algorithm underestimates the amount of IEW in the study area. The algorithm is not able to fully detect smaller patches, and soil saturated with water and IEW patches with vegetation are often not detected. Water retention on arable land flooded by IEW is beneficial to agriculture, while ecosystem restoration in flooded nature areas can be planned for nature conservation. Therefore, it is important to develop robust algorithms to map and monitor the phenomenon. The presented algorithm supports efficient water management activities to mitigate the negative effects of IEW and to increase the sustainable use of the surplus water in periods of deficit. A version of the algorithm is being implemented in the framework of the National Hungarian Earth Observation Information System (FIR), which is currently under development.