## 5 Discussion
This last finding is particularly significant because it highlights an area that, although clearly flooded in 2013, has not yet included in the current flood hazard map, revealing the added value that robust satellite algorithms may provide in improving flood risk management. Comparison with the results provided by implementing another VIIRS-based method [11] seems to confirm the good capability of RST-FLOOD to detect inundated area. Although some differences are inherently due to the different approach at the basis of the two algorithms, some others are attributable to the assumption that VNG has been developed to work at global scale (between 80 \({}^{\circ}\)S and 80 \({}^{\circ}\)N) and set more towards reliability than sensitivity. Moreover, a few of the static ancillary databases used within this software might not be updated respect to the local conditions faced in this work. Despite these satisfactory achievements, a few possible sources of inaccuracies (i.e., the above cited 11.5% of possible false positives) have been identified and are briefly discussed as follows. Cloud shadows, representing the main issue limiting flood detection reliability, can be considered as random and spurious signals perturbing the reflectance field measured in the VIS-SWIR spectral region. Therefore, in this paper, their identification has been addressed developing an original RST-based approach exploiting VIIRS I1, I2 and I3 bands reflectance (see Section 3.4). In detail, the combination of the signal acquired in these three bands has been used to discriminate cloud shadows from flooded area and other features. Preliminarily results here presented seem to confirm the feasibility of this approach, even if, a few omission errors have been identified, producing false positive detections. Further analyses aimed at assessing the accuracy of these achievements will be performed in the future, in order to better investigate if other possible signal combinations may provide any improvement. Residual geo-location errors or the use of a land-sea mask not perfectly co-located with the analysed data may produce a few false positive along coastlines. In these zones, the concurrent presence of water and land produces a mixed spectral signature at pixel level that is generally taken into account by standard deviation in the RST-FLOOD approach. When a seawater-like signal is compared with a land-like one a few errors may be detected, thus suggesting a check on the geo-location data accuracy. The inflow of anomalous concentration of suspended sediments from lakes ( dams) within the ROI has affected the signal measured by VIIRS especially at low wavelengths (i.e., I1 band) especially on the RED and SWIR bands combination (i.e., Ratio and Difference), also in terms of ALICE index implementation (see Section 4.1). Within the RST framework, this is an expected and obvious result because it highlights the occurrence of conditions extremely different from the expected one, in terms of RED reflectance variation. On the other hand, within the RST-FLOOD application, the identification of anomalous \"flooded\" pixels on the lake border could represent for sure a limitation. The NDSI signal is inherently affected by the increase in the RED signal due to suspended sediments but its implementation within RST did not generate evident signal anomalies, thus indicating that the adoption of the normalized signal coupled with the differential approach enables to face any possible issue. A possible further limitation that could influence the NDSI signal is the presence of snow, which would produce a spectral signature similar to the flooded areas [7] due to the spectral position of VIIRS I3 band (i.e., 1.58-1.64 um). Concerning this issue, at least two possible scenarios can be defined in the RST context, depending on whether the investigated area is characterized by the presence/absence of permanent snow.