## 5 Discussion  
Timely and continuous information about flood dynamics is fundamental to ensure an effective implementation of relief and rescue operations. Using data acquired by optical sensors onboard meteorological satellites for flood detection and monitoring may be detrimental due to cloud cover that can hamper any kind of acquisition. Despite this limitation, there are several advantages that make optical data a good complement to other satellite-based systems, such as: (i) the large swath that allows for both large area coverage and high temporal resolution; (ii) a medium spatial resolution (in the order of hundreds of meters) useful for detecting medium-major flood events; and (iii) the deployment in satellites constellation that further enables an increase in revisiting time. In addition, sensors operating in different spectral regions that can complement the information acquired using optical data are often present aboard meteorological satellites [33]. Both MODIS and VIIRS, the optical sensors considered in this work, show almost all of the above-cited features, making them suitable for flooded area detection. While the MODIS capability in this framework has been largely demonstrated [11], there are only a few works based on VIIRS I-band data [9; 34; 35]. In this work, we implemented the RST-FLOOD approach on MODIS and VIIRS daytime data to analyze a few days of the flooding event that occurred in the Metaponto plain (southern Italy) in the first week of December 2013. RST-FLOOD has been already applied using 1 km MODIS visible and near infrared data, with only a preliminarily feasibility analysis of the potential of the same data when  
Figure 8: (a) Flooded areas detected within the Region of Interest (ROI) by integrating results achieved by ALICE\({}_{\text{NIR-VIS}}\) implemented on MODIS and VIIRS data for the two investigated days; (b) as in (a) using the ALICE\({}_{\text{NIR/VIS}}\). acquired at 250 m of spatial resolution [12]. In this work, we further assess the capability of this latter specific configuration of RST-FLOOD in detecting flooded areas, and also test its performance when implemented on VIIRS imagery data at 375 m of spatial resolution. The main advantages of RST-FLOOD with respect to traditional techniques are: (i) the use of local (i.e., at the pixel scale) adaptive and dynamic thresholds; and (ii) no dependence on any kind of auxiliary/ancillary information [12]. Ancillary datasets or fixed thresholds on the signal under investigation are often used to face the main challenges, such as cloud and terrain shadows and the discrimination of flooded areas [34; 35], limiting flood detection reliability. The accuracy/availability of auxiliary information can directly affect the quality of the achieved results, while fixed thresholds may suffer from sensitivity/accuracy limits because of the signal variability due to the specific site/local setting of the scene under investigation [14]. The only constraint for RST-FLOOD implementation is the availability of a satellite historical series long enough to guarantee a consistent identification of the expected values in terms of temporal mean and standard deviation. An independent work focusing on RST [36] found that such a historical series should consist of at least of 80 images to produce reliable reference fields, corresponding to at least three years of data, considering a monthly temporal window and a daily frequency of observation. Results achieved by applying the RST-FLOOD indices on the two sensors suggest the benefit of their integration for a continuous monitoring of the ongoing phenomenon at large spatial scale. Such an integration enables a clear increase in the observational frequency, improving the flood evolution monitoring capability of each single sensor. This result is fundamental for decision makers, especially during the crisis, in order to better identify critical situations and ensure an effective implementation of relief and rescue operations.