# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 2 Materials and Methods
### Remote Sensing Data Processing  
Time series of the satellite imagery acquired by MultiSpectral Instrument (MSI) onboard Copernicus Sentinel-2 satellites constellation, consisting of a set of 13 spectral bands in the visible, near-infrared, and short-wave infrared radiometric intervals at 10, 20, and 60 m spatial resolution, were collected and analyzed. Specifically, all the Sentinel-2 MSI satellite acquisitions processed at level L2A for the period 2016-2023 with cloud cover lower than 90% were collected for the entire study area, for a total of around 2700 images distributed in 5 tiles (Figure 1). For each satellite acquisition dataset, the provided raster masks corresponding to clouds, cloud shadows, and snow were used to identify and remove bad pixels from further analysis. Normalized difference water index (NDWI) was calculat \begin{table}
\begin{tabular}{c c} \hline \hline
**SDI** & **Category** \\ \hline SDI \(\geq\) 2 & Extremely wet \\ 1.5 \(\leq\) SDI \(<\) 2 & Severely wet \\ 1 \(\leq\) SDI \(<\) 1.5 & Moderately wet \\ 0 \(<\) SDI \(<\) 1 & Mildly wet \\ \(-\)1 \(\leq\) SDI \(<\) 0 & Mild drought \\ \(-\)1.5 \(\leq\) SDI \(<\)\(-\)1 & Moderate drought \\ \(-\)2 \(\leq\) SDI \(<\) 1.5 & Severe drought \\ SDI \(<\)\(-\)2 & Extreme drought \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of wet and drought period according to SDI. reflectance spectral bands in the green (B3) and near-infrared (nir) spectral intervals (B8) at 10 m spatial resolution according to Equation (3) [45]. \[NDWI=(green-nir)/(green+nir) \tag{3}\]  
Percentage water content (PWC) in each pixel was calculated from NDWI using a fuzzy transform with the following rules: PWC = 0 for NDWI \(\leq\) 0; PWC = 1000.0 \(\times\) NDWI for NDWI \(>\) 0 and NDWI \(\leq\) 1; PWC = 100 for NDWI \(>\) 1. Thresholds were identified by expert operators' visual inspection on 250 NDWI transects perpendicular to riverbanks. S2 SDB Database [46], freely accessible from the open access data repository available at link [[https://github.com/ffilipponi/S2](https://github.com/ffilipponi/S2) SDB]([https://github.com/ffilipponi/S2](https://github.com/ffilipponi/S2) SDB) (accessed on 11 September 2024), generated using of AROSICS algorithm [47] for operational image co-registration, was used to spatially co-register PWC time series, in order to deal with weak spatial coherence of Sentinel-2 MSI time series [48]. Co-registered PWC time series were stacked in large multidimensional datacubes, one for each Sentinel-2 granule, and spatially sub-set to the study area. Monthly water frequency (MWF) was calculated as the monthly average of PWC for a total of 96 months in the period 2016-2023. River shrinking analysis was based on the monthly water frequency anomaly (MWFA), which is the difference of each month from the average values of the same month calculated from a multi-year time series, and the difference from the previous month (DPM), calculated as the difference between MWF and the MWF of the previous month. MWFAs were calculated for the years 2021-2023 with respect to the monthly climatology, identified in the previous period 2016-2020. Furthermore, overall water frequency (OWF), which is the average water frequency, was calculated for the entire considered period 2016-2023. The MWFs of June, July, August, and September of a given year were compared against all the remaining years by means of \(t\)-tests (e.g., July 2022 MWF was tested against July MWFs of the remaining years). The analysis was conducted for each 1 km slice separately (n = 479). For each month and year, the number of significant \(t\)-tests (\(p<0.05\)), under the alternative hypothesis that the MWF of the given year was lower than that of the remaining years, was computed.