## 4 Results and Validation
### Quality Assessment and Discussion  
In this paper, 32 study areas shown in Table 4 have been used for validating the proposed approach. After a detailed discussion of three selected water bodies (Ray Roberts, Poco da Cruz, Tharthar), a general quality assessment of all 32 globally distributed study areas is performed in this Section. All study areas of lakes and reservoirs have different characteristics. The long-term water level variations are between \(2.26\,\mathrm{m}\) for Clear Lake and \(43.56\,\mathrm{m}\) for Mead Lake. The smallest investigated inland water body is the Jenipapeiro reservoir whose surface area varies between \(0.0\,\mathrm{km}^{2}\) and \(9.15\,\mathrm{km}^{2}\). Here, a dam has been constructed at the turn of the millennium. Lake Tharthar is the largest study area which has the strongest area variations between \(1579.39\,\mathrm{km}^{2}\) and \(2476.11\,\mathrm{km}^{2}\). In addition to the surface area, the lake-shore lengths of the study area and its ratio are shown in Table 4. For study areas in mountainous regions which are mainly reservoirs, the ratio between surface area and shore length decreases. The ratio of natural lakes is therefore higher. For example, the shape of Salton Sea is more circular which leads to a higher ratio of 2.37 between surface area and shore length. In contrast, Jenipapeiro Lake is a reservoir located in the mountains whose topography is more frayed as the reservoir filled up the valley which leads to a longer shore line compared to its surface area. The ratio between surface area and lake shore varies between 0.11 and 2.37. In theory, the shape of lakes and reservoirs has a strong impact on the resulting quality of the surface area time series. In the land-water classification process indexing errors mainly are located at shores and not inside the lake. This fact has the potential for higher errors of reservoirs with longer shores than for natural lakes. But comparisons between ratio of surface area and shore length (Table 4) and the resulting correlations of the validation (Table 5) show no direct link. Figure 21: Relation between water level and surface area (hyposometry) used for cross-validation at Tharthar, Lake. The blue points (10% of all points) are used for the function fitting, the orange points for the validation. The two fitted curves are plotted in red (linear fit) and green (polynom). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{Data Availability} & \multicolumn{4}{c}{Monthly} & \multicolumn{4}{c}{Validation Gauge} & \multicolumn{4}{c}{Validation Alimentary} & \multicolumn{4}{c}{Area Error Net. AO} \\ \cline{2-13}
**Target Name, Country (HD)** & **L4** & **L5** & **L7** & **L8** & **S2A** & **S2B** & **Masks [s]** & **Points** & **Initial** & **Final** & **Impe. ** & **Points** & **Initial** & **Final** & **Impe. Based on the resulting monthly surface area time series, we validate our results by calculation square Spearman correlation \(R^{2}\) with respect to water level time series from gauging station respectively satellite altimetry depending on the availability. In order to demonstrate the improvement of our new approach for filling the data gaps, we also compute \(R^{2}\) for all initial monthly masks with data gaps with water level data sets. Depending on the length of the different water level time series, the number of used points differs for each study area. But the comparison of initial areas and final areas is always based on the same number of data. Figure 5 shows the detailed validation of the results for all 32 study areas with water level time series from in situ data and satellite altimetry. For each study area, the number of used points, correlations coefficient \(R^{2}\) with initial areas, correlations coefficient \(R^{2}\) with final areas and the performance are given for each used validation data set. In addition to the study areas geometry, the location and its climate condition it is expected to have an impact on the results.