## 4 Results and Validation
### Selected Results
#### 4.2.2 Poco da Cruz, Reservoir  
Poco da Cruz is a reservoir in Brazil, more precisely in the state Pernambucu near Ibmirim which is located about 350 km west of Recife. The climate zone around Poco da Cruz Reservoir is arid steppe with hot temperatures [36]. The averaged yearly precipitation is about 550 mm/y [37] which is about the half as for Lake Ray Roberts. On the other hand, the annual cloud coverage increased up to about 65% [38]. This leads to more cloud coverage data gaps in the optical images which have to be considered. The average monthly data gap is 9.73 km\({}^{2}\) representing about 16% of the AOI. Figure 16 shows the resulting surface area time series (orange) with their errors (blue) and initial surface areas including data gaps (gray) of Poco da Cruz Lake. This reservoir shows very strong variations of the surface area over the last three decades. The water level as well as the surface area vary between minimum (1998-2000), where the reservoir is almost empty, and maximum (1986, 1989, 2004, 2008) where the reservoir is filled. In between, the reservoir is subjected to strong variations. In the study period of 414 months, there are 39 months (9.4%) without any optical images available. In detail, the following monthly data of Landsat-4 (0.0%), Landsat-5 (79.5%), Landsat-7 (85.0%), Landsat-8 (100.0%), Sentinel-2A (96.8%) and Sentinel-2B (100.0%) are available. In the case of 14 months (3.4%), the processing of the final surface areas failed. For 361 months (87.2%) the final surface areas could be computed successfully, wherein afterwards 133 months (32.1%) have been rejected in the post-processing step. The final surface area time series consists of data for 228 months (55.1%). One can clearly see that the area error increased compared to Lake Ray Roberts. This is caused by the higher cloud coverage and by the shape of the lake that has much longer shores compared to its surface which also affects the gap-filling process and the resulting error. Figure 15: Relation between water level and surface area (hypsometry) used for cross-validation at Lake Ray Roberts. 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). For the validation of surface area time series, in situ data and satellite altimetry are used. The water level time series from satellite altimetry is based on ERS-2, Envisat, SARAL, and Sentinel-3A. The time series contains 180 data points in the period between 1995 and 2018. Figure 16 shows the used water level time series from in situ data (red) and satellite altimetry (blue). Comparisons between water levels and surface areas (Figure 16) show a very good agreement with high correlations (Figure 17). For a more accurate quality assessment, we compute \(R^{2}\) between surface areas and water level. Figure 17 shows the resulting scatter plots of the correlations between surface area and in situ data, respectively satellite altimetry. For in situ data, \(R^{2}\) increases from 0.921 for the initial surface area (based on 44 months) to 0.981 for filled surface areas. This is a quality improvement of about 7%. \(R^{2}\) for  
Figure 16: Water surface area time series (orange) and their errors (blue) of Poco da Cruz Reservoir. Additionally, the initial surface area with data gaps (gray) and the filled area (green) are shown. Also, the data availability of monthly optical images from Landsat and Sentinel-2 is presented. Finally, the water level time series from in situ stations and DAHIT which used for validation is shown. Figure 17: Validation of surface area time series for Poco da Cruz Lake using water levels from in situ stations and satellite altimetry. The improvement of our new approach is shown by comparing initial surface areas with data gaps (**left**) and final filled surface areas (**middle**, **right**). satellite altimetry also increased significantly up to 0.977. By filling the data gaps, the quality improved by about 8%.