# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 4 Results
### Accuracy Metrics and Extent of the Flooded Area  
The results of the comparison between AUTOWADE-derived and CEMS-derived maps, expressed in terms of \(\kappa\) and F1-score, are reported in Table 5. The former metric is in the range [0.80-0.90] with an average value (over the 10 case studies) of 0.85, while the F1-score is in the range [0.87-0.94] with an average value of 0.90. Table 5 also includes the F1-score computed as the average of the F1-scores of the two water classes only. In this case, it ranges between 0.80 and 0.92 with an average value of 0.85. For what concerns the errors, they are reported in Table 6 for the class of permanent water and Table 7 for the class of open floodwater. Regarding \(\epsilon_{\text{comm}}\), it ranges between 0.6% and 21.4% with an average value (over the 10 case studies) of 9% for the class of permanent water, and between 8.5% and 25.3% with an average value of 17.5% for the class of open floodwater. For what concerns \(\epsilon_{\text{om}}\), it is in the range [2.5-32%] with an average value equal to 17.8% for permanent water, and in the range [1.7-21.2%] with an average value equal to 13.6% for floodwater.  
Figure 12: Full example of the results of the BFE approach implemented in AUTOWADE for the EMSR359 test case. Panel (**a**): RGB false color image produced using the S1 data (red: pre-flood image, green/blue: flood image); panel (**b**): output of the ISODATA applied to \(\sigma^{0}_{VV}\) (CDWA in red); panel (**c**): output of the ISODATA applied to \(\Delta v^{0}_{VV}\) (CDFA in red); panel (**d**): map of open floodwater (cyan)/permanent water (blue); panel (**e**): TDWA (in red); panel (**f**): TDFA (in red). All the maps are zoomed on the area affected by the flood.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CEMS Activation Code** & \(\kappa\) & **F1-Score** & **F1-score for Water Classes** \\ \hline EMSR359 & 0.87 & 0.91 & 0.87 \\ EMSR411 & 0.84 & 0.90 & 0.85 \\ EMSR417 & 0.90 & 0.91 & 0.86 \\ EMSR429 & 0.84 & 0.90 & 0.85 \\ EMSR437 & 0.80 & 0.88 & 0.83 \\ EMSR445 & 0.87 & 0.90 & 0.86 \\ EMSR492 & 0.84 & 0.87 & 0.80 \\ EMSR496 & 0.87 & 0.94 & 0.92 \\ EMSR501 & 0.83 & 0.89 & 0.84 \\ EMSR502 & 0.86 & 0.89 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between CEMS-derived and AUTOWADE-derived open floodwater/permanent water maps in terms of kappa coefficient and F1-score. The latter has been computed as the arithmetic mean over individual F1-scores considering all the classes (3 rd column), or only the two water classes (4 th column).  
Figure 13: Example of the results of the method to map flooded vegetation implemented in AUTOWADE for the Vercelli test case. Panel (**a**): RGB false color image produced using the S1 data (red: pre-flood image, green/blue: flood image) with, superimposed in red, the boundaries of the flooded area detected using S2 data; panel (**c**): output of the ISODATA applied to \(\Delta\sigma^{0}_{VV}\) (green: cluster of the objects with high \(\Delta\sigma^{0}_{VV}\) ); panel (**e**): map of open floodwater (cyan), flooded vegetation (sea green) and permanent water (blue); panels (**b,d,f**): zooms on flooded vegetation.  
A second exercise has been carried out by calculating the errors for all the pixels included in the 10 areas of interest. The quite low values of the errors confirm the efficiency of AUTOWADE: \(\epsilon_{\mathrm{comm}}=9.4\%\), \(\epsilon_{\mathrm{om}}=25.6\%\), for permanent water and \(\epsilon_{\mathrm{comm}}=14.8\%\), \(\epsilon_{\mathrm{om}}=13.9\%\) for floodwater.  
Table 8 reports, for each test case, the total extent of the flooded area estimated by CEMS and by AUTOWADE. The extent is expressed in ha and in percentage with respect to the size of the AOI.