# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 3 Results and Discussion  
### Maps and Statistics  
Weekly inland excess water maps were created for the complete study area for the two inland excess water periods in 2016 and 2018 (Figure 6). Parameters were tweaked to adjust the sensitivity of the algorithm to water but to stay within realistic boundaries. Visual inspection of the results, overlaying the IEW maps on different color composites of the Sentinel-2 images, shows that the  
Figure 5: Processing of validation data from SPOT-7 image (**A**) and from aerial survey (**B**). Both images show a small subsection of the validation areas. algorithm properly delineated IEW inundations. The largest inundations were detected in regions with clayey soils with unfavorable water management characteristics and on the former floodplains, delineating the former fluvial geomorphology like oxbows and river meanders. The border of the Maros alluvial fan was clearly shown on the 2018 result, in fact even on its higher areas IEW was detected. Figure 6: Detected inland excess water on the total study area (**top**) and on subareas (**middle**) in 11 th week of 2016 (**left**) and 13 th week of 2018 (**right**) and natural color composites of the input optical data (bottom). During the moderate IEW period of 2016, the presented algorithm detected a little over 600 ha of IEW mainly in the northwest and east parts of the total study area. More than half of the detected IEW polygons were small, in this research not larger than 3 pixels (=\(<\)300 m\({}^{2}\)). Often in pixel-based classifications, these pixels are automatically attached to the statistically nearest bigger class. In the presented algorithm, this procedure did not happen because the contrast between water surfaces and dry surfaces is usually quite big, and these small patches are often located close to each other or part of a larger patch, together defining a larger flood area in the IEW map. In 2016, 72% of the IEW patches consisted of large, minimum 1 ha inundations, the largest patch was even larger than 25 ha. The results show that over half of the IEW inundations occurred in agricultural areas, to a large extent arable land, which has a large effect on the agricultural production. A further one-third of the IEW patches were located on natural grassland. In 2018, almost four times as much inland excess water was detected (3082 ha) as in 2016, and also the spatial distribution of the patches was more spread out than in the earlier research period. In fact, the floods increased everywhere except on the higher regions of the central and south-eastern parts of the study area. The complexity of the issue of inland excess water formation and the importance of high-resolution monitoring was also demonstrated by the fact that in 2016, there were small IEW patches that were not inundated in 2018 (e.g., the arable land northwest of Oroshaza). For 2018, it was tested how the algorithm responded to different settings of the integration parameter (Figure 7). Thresholds for 0.05 to 1 were specified with an increment of 0.05. The final setting that was used for the calculations of inland excess water in 2016 and 2018 was 0.30. It was determined that lower settings resulted in a large amount of cloud and shadow misclassification, and higher settings reduced the amount of detected water too much. It should be mentioned that the number of available input images in 2018 was much larger than that in 2016; therefore, the chance to detect water was also larger. This issue only occurred in the earlier years of Sentinel data acquisition when fewer satellites were in orbit. In both years, the large and often deeper inundations that were part of non-permanent wetlands (in the south center and east) were detected almost perfectly. There was also a difference in land use at the inundation between the two years under study.