## 3 Data and Methods
### The Robust Satellite Techniques (RST) Approach  
RST is an automatic change-detection scheme, already applied with good accuracy in monitoring different natural and environmental hazards (see [21] and references herein). RST identifies statistically significant variations, at pixel level, for the signal under investigation by: (i) analysing multi-year time series of cloud-free homogeneous (same calendar month, same acquisition time, same spectral channel/s) satellite records; (ii) searching for anomalies by means of a specific change detection step. The latter exploits the \"Absolute Variation of Local Change of Environment (ALICE) Index\" defined as follows:  
\[\otimes_{V}(x,y,t)=\frac{\mathrm{V}(x,y,t)-\mu_{v}(x,y)}{\sigma_{v}(x,y)}, \tag{1}\]  
where, V(\(x\),\(y\),\(t\)) is the signal measured at time t for each pixel (\(x\),\(y\)) of the analysed satellite scene, while \(\mu_{v}\)(\(x\),\(y\)) and \(\sigma_{V}\)(\(x\),\(y\)) represent respectively the temporal mean and standard deviation (i.e., the reference fields), both computed by processing the above mentioned homogeneous multi-temporal dataset of satellite imagery\" [22]. Such an index is, for its inherent design, a standardized variable that should show a Gaussian behaviour (i.e., mean \(\sim\) 0 and standard deviation \(\sim\) 1), therefore, the higher the absolute value measured, the lower the probability of occurrence [49]. Namely, the probability of occurrence of an anomalous signal decreases from about 4.5% for \(\mid\)\(\otimes\)\(\mathrm{v}\)\(\mid\)\(>\) 2 (level of statistical significance) to 0.3% for \(\mid\)\(\otimes_{V}\)\(\mid\)?3 and even lower values for increased \(\mid\)\(\otimes_{V}\)\(\mid\)[30]. The analysis at pixel level of long-term signal series acquired under the same observation conditions (i.e., same sun height,  
Figure 5: VNG products related to the ROI shown in Figure 1 for: (**a**) 04/12/2013 at 12:10 GMT; (**b**) 05/12/2013 at 11:52 GMT; (**c**) 06/12/2013 at 11:35 GMT; (**d**) 07/12/2013 at 11:12 GMT; (**e**) 08/12/2013 at 12:36 GMT. same land use/cover according to \"seasonal\" dynamics, almost similar weather/climatic conditions) automatically allows the overcoming of possible site effects [22]. For instance, any signal variation related to terrain shadows, which should be almost constant considering imagery acquired always in the same month of the year at the same time, is inherently taken into account by the proposed method. Furthermore, signal fluctuations due to spurious/random effects (sun glint contamination, suspended sediment variability, etc.) can only determine a higher standard deviation, thus resulting in a reduction of the ALICE index values and a much more selective identification of anomalous events [49; 50]. As assessed by an independent work focused on RST [51], the investigated satellite historical series should consist of about 80 images to guarantee representative background signal. Such a number, considering a monthly temporal window and a sensor offering a daily temporal resolution, roughly corresponds to a 3-year long time series, representing indeed the minimum time span for a reliable RST implementation. During the generation of the reference fields, as well as during the change detection step, cloudy pixels were identified and discarded from the detection step implementing the One Channel Algorithm (OCA) method [52; 53]. Such an approach, still based on the RST prescriptions, analyses historical series of VIIRS I3 and I5 data to identify clouds as statistically high reflectivity or low temperature objects. Concerning cloud shadows, a specific algorithm based on ALICE indices (described in the next section) has been developed and tested here for the first time, to ensure a reliable identification of flooded area by optical data.