## 4 Results
### Selection of the Most Suitable VIIRS RST-FLOOD Indicator  
As already mentioned, the first almost cloudy-free VIIRS daytime image acquired over the area under investigation soon after the event was on 4 December 2013 at 12:10 GMT, shown in RGB False Colour (R = I3, G = I2, B = I1) in Figure 1. This image has been selected as benchmark for assessing ALICE indices performances (Figure 8), being the one temporally closest to the flood peak. In each of the five maps reported in Figure 8, pixels showing statistically significant (i.e., higher/lower than \(|3|\)) ALICE values have been depicted in red. The results based on the ALICE\({}_{\text{RED}}\) index (Figure 8a) highlighted its poor reliability in discriminating flooded pixels. Although several pixels are located along the main rivers in the area, there are indeed more others randomly spread out all over the ROI. These effects disappear in all the other maps, whose analysis seems to suggest a quite similar behaviour for the other four indices. Hence, excluding the ALICE\({}_{\text{RED}}\) index (Figure 8a), the number of flooded pixels identified all over the scene by each of the other indices has been summarized in Table 2, in which also the mean and maximum ALICE values are reported. The same information has been listed for anomalous pixels detected along the Basento River. The ALICE\({}_{\text{SWIR}}\) appears as the most conservative index in identifying anomalous pixels, compared with the others (see Table 2). An in-depth investigation of anomalies discriminated by all the other indices suggests the following considerations. The ALICE index, when using the RED and SWIR reflectances, combined as difference, ratio and normalized difference, overall provide the same information on flooded area extent, flagging as anomalous a very similar number of pixels (see Table 2). In particular, among them, the ALICE\({}_{\text{RED-SWIR}}\) and ALICE\({}_{\text{RED/SWIR}}\), unlike the ALICE\({}_{\text{NDSI}}\), seem more exposed to the detection of anomalous/spurious pixels along the coastline. Accordingly, the ALICE\({}_{\text{NDSI}}\) appears as the most performing index, offering the best trade-off between sensitivity and reliability. Figure 7: (a) RGB False Colour (R = SWIR, G = NIR, B = RED) of VIIRS Imagery data acquired on 5 December 2013 at 11:52 GMT; (b) cloud and shadow masks for the image shown in (a); (c) RGB False Colour (R = SWIR, G = NIR, B = RED of VIIRS Imagery data acquired on 7 December 2013 at 11:12 GMT; (d) cloud and shadow masks for the image shown in (c). Figure 8: Anomalous pixels detected by: (**a**) ALICE\({}_{\text{RED}}\), (**b**) ALICE\({}_{\text{SWIR}}\), (**c**) ALICE\({}_{\text{RED-SWIR}}\), (**d**) ALICE\({}_{\text{RED-SWIR}}\), (**e**) ALICE\({}_{\text{NDSI}}\) on 04/12/2013 at 12:10 GMT. The analysis for the Basento River shows almost similar outcomes respect to the whole scene in terms of detected ALICE values but with the ALICE\({}_{\text{NDSI}}\) identifying the largest number of anomalous pixels (i.e., 256). Therefore, on the basis of the above discussed results, the ALICE\({}_{\text{NDSI}}\) has been selected as the benchmark index in the next analyses, resulting less affected by suspended sediments and showing a good sensitivity in detecting flooded area. A map of the detected anomalous areas for the VIIRIS image of 4 December 2013 is plotted in Figure 9 where different colours (from red to yellow) indicate increasing significance levels of the ALICE\({}_{\text{NDSI}}\) index. The pixels detected as anomalous at the highest ALICE\({}_{\text{NDSI}}\) (i.e., the yellow ones) are representative of local conditions extremely different from those expected in the ROI.