## 5 Discussion  
Floods are natural disasters that, due to their clear spatial and spectral markers in different regions of the electromagnetic spectrum, are more easily investigable by means of satellite data [14]. Among the different satellite-based contributions useful for the flood risk management cycle, is the generation of reliable multi-temporal flood maps is fundamental in all the different phases [8; 60; 61]. Optical sensors onboard polar satellites can assure the better trade-off among spectral/spatial/temporal resolutions suitable for a near real time and continuous monitoring of flooded area [62] although cloud coverage can fully or partially hamper the acquisition of useful data in this spectral region, possibly limiting their applicability [7]. Among the currently operational optical sensors, VIIRS onboard SNPP is one of the most recent and advanced, assuring a long-term continuity of observation (up to the 2031, [38; 39]), that is crucial to assess its potential in investigating floods as well as other natural disasters [40; 54]. In this work, the RST-FLOOD approach, previously applied to AVHRR and MODIS VNIR data, has been fully implemented for the first time on historical series of VIIRS Imagery records for evaluating its capability in detecting inundated area. The adoption of dynamic and local-scale self-adaptive thresholds and the fully independence on any kind of auxiliary/ancillary information are the main RST-FLOOD advantages with respect to traditional techniques [14; 23]. Fixed thresholding schemes or ancillary dataset are indeed often used for flooded area detection applications [11; 54] as well as for facing issues related to cloud and terrain shadows, representing the main challenges for a fully reliable flood detection [11; 54]. The results performance of inundated area identification can indeed largely change depending both on the availability and the accuracy of the used auxiliary/ancillary information [22]. In addition, specific site/local-setting of the investigated scene, as well as seasonal/ meteorological variations of the studied signals, may negatively affect the sensitivity of fixed thresholds schemes [14; 22]. Focusing on the RST-FLOOD-based application shown in this paper, the flood event that affected the Basilicata and Puglia regions in the first week of December 2013 has been investigated, producing satisfactory results, which further confirm the great potential of the proposed approach in detecting flooded areas. Food evolution in the spatiotemporal domain has been accurately identified, well over the spatial extent and temporal interval suggested by other works [37; 31]. A total area of about 73 km\({}^{2}\) was \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**All Scene Pixels Number**} & \multicolumn{3}{c}{**Black Box Pixels Number**} \\ \hline
**VIIRS Data** & **RST-FLOOD** & **VNG** & **MATCHING** & **RST-FLOOD** & **VNG** & **MATCHING** \\ \hline
04/12/2013 & 638 & 454 & 109 & 510 & 184 & 96 \\
05/12/2013 & 357 & 481 & 36 & 293 & 111 & 31 \\
06/12/2013 & 267 & 234 & 26 & 226 & 84 & 18 \\
07/12/2013 & 345 & 196 & 4 & 317 & 71 & 4 \\
08/12/2013 & 260 & 137 & 3 & 211 & 10 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of pixels detected by RST-FLOOD, VNG and by both the algorithms (as shown in Figure 13) for the analysed days. identified as inundated during the investigated period, with a good level of reliability, as highlighted by the validation analysis performed through the comparison with the PAI map. About 68% of the detected anomalies falls within the PAI, 6.5% refers to the borders of the San Giuliano and Monte Cotugno dams, 11.5% takes into account pixels close to the coastlines and rivers but outside the above mentioned zone, representing possible false positives. The remaining 14% represents a \"for sure\" flooded area located in correspondence of the Castellaneta Marina town and between the Bradano and Basento mouths.