# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 3 Results and Discussion
### Validation
To better understand the difference between the IEW patches detected by the algorithm and the polygons delineated on the SPOT satellite image (Table 4) and aerial photographs (Table 5), the number of inundations and their areas were compared. From the results, it can be seen that the majority of the polygons (over 70%) were in the smallest size range; however, their cumulative area was not larger than 10% of the total inundation. Because of the difference between the image resolutions, the algorithm omitted a large portion of these patches (65.6% and 94.5% in 2016 and 2018 respectively). In the larger-size ranges, the statistics of the reference and the detected IEW patches were more similar. However, the overall distribution of the polygons between the categories was comparable. The underestimation of the number of IEW patches/pixels by the algorithm can be reduced by changing the parameters for the threshold of the water detection by the radar data, the number of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference**} \\ \cline{2-9}  & \# Pixels & **No Water** & **Water** & **Total** & **Producer’s Acc.** & **Omission Error** & **User’s Acc.** & **Commission Error** \\ \hline \multirow{3}{*}{Detected} & no water & 185,712 & 13,919 & 199,631 & 99.97 & 0.03 & 93.03 & 6.97 \\  & water & 185,712 & 13,919 & 889 & 5.63 & 94.37 & 93.36 & 6.64 \\ \cline{1-1}  & Total & 185,771 & 14,749 & 200,520 & Overall Acc. & 93.03 & Kappa & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy assessment on the validation site in 2018. The color of the cells refers to the colors in Figure 12.  
Figure 13: Results of the cross-validation on a selected area in 2018 (**A**—input Sentinel-2 data, **B**—result assessment, **C**—validation aerial data). ISODATA classes that show water and the threshold of the MNDWI slicing. Also, the threshold for the relative frequency required to assign a pixel as water can be set in the algorithm. It was decided to keep the parameters such that realistic values remained for the thresholds and ISODATA water classes, and therefore excepting the underestimation. Extreme fine tuning of the parameters may be possible for small areas but is not feasible since the algorithm is meant to work for large areas. Also, the algorithm is fully automated and runs without any user intervention. Fine tuning the parameters per area and time would prevent the automation and general applicability. The results of the research show that a combination of different optical and radar satellite data based algorithms provides improved results, compared to single method based solutions. Methodology optimized for a certain area and for single-event mapping will give better results but cannot be used for monitoring of large areas like the presented methodology. Limitations are the minimum size of IEW patches; the algorithm does not adequately identify inundations with an area smaller than 1000 m\({}^{2}\). In situations with lots of clouds, reference training data for the optical data based algorithms are limited, resulting in IEW maps that are mainly based on radar data. Due to the inherent noisy nature of radar data, smaller water patches are difficult to detect. Also, wind may cause turbulence in the water and result in higher radar signals, which can prevent the radar-based algorithm to detect water. Finally, radar data from ice and snow results in omission errors, underestimating the amount of IEW. To overcome the described problems, IEW maps are derived from optical and radar data that are gathered over the period of one week. This increases the chance of having a mixture of optical and radar data as input for the algorithms.