# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 4 Discussion  
### Proposed Methodological Approach  
While a number of alternative methodologies for the classification of water, vegetation, and sediments in riverine systems are available and commonly deployed, the proposed approach specifically aims at quantitatively monitoring the river water component and represents a novel contribution to the field, offering a reproducible, relatively user-friendly solution that can be applied with diverse sensors and extended to any site. It relies on the use of a simple spectral index, calculated from optical multispectral imagery, to generate a PWC time series. NDWI spectral index is designed to maximize the reflectance of a water body by using green wavelengths, minimize the low reflectance in the near-infrared of water bodies, and take advantage of the high reflectance in the near-infrared of vegetation and bare ground features. From the use of a random forest classification model to map water bodies, NDWI resulted as one of the predictor variables exhibiting higher importance. It could alone classify water pixels with an overall accuracy higher than 90%, comparable to the one obtained by the random forest classification model [29]. The use of a short-wave infrared spectral band in place of a near-infrared spectral band, in combination with the green wavelength, can provide a more accurate detection of water bodies [50]. Nevertheless, it has been demonstrated that using Sentinel-2 MSI, the NDWI spectral index could classify  
Figure 10: (**a**) Overall water frequency (OWF) of Trebbia River tributary. (**b**) Monthly water frequency anomaly (MWFA) during July 2022 of Trebbia River tributary. (**c**) Overall water frequency (OWF) of Oglio River tributary. (**d**) Monthly water frequency anomaly (MWFA) during July 2022 of Oglio River tributary. the water body better than the modified MNDWI, which is based on short-wave infrared spectral bands [23; 50]. A comparative analysis of normalized difference spectral indices to map water surface is provided in [51]. The choice to select the NDWI spectral index, based on green and near-infrared wavelengths, is motivated by the aim of maximizing the applicability of the proposed procedure to a large number of remote sensing optical multispectral sensors. In fact, spectral bands in the green and near-infrared radiometric ranges are typically available in both high and very-high-resolution optical multispectral sensors, unlikely other radiometric ranges such as short-wave infrared, making the methodology applicable to a wide number of remote sensing datasets. In addition, green and near-infrared spectral bands, unlike those in other radiometric ranges such as short-wave infrared and red-edge, are available at higher spatial resolution in Sentinel-2 MSI satellite acquisitions. When the percentage of water content in a pixel is lower than 100%, the water-related spectral indices values show significant variations, which depend on the relative fractions of bare ground and vegetation within the pixel [52]. Although this is a critical issue in the classification of water bodies, it could be considered as relevant information in monitoring hydomorphological conditions using time series. In fact, the use of a continuous variable (PWC) instead of a discrete variable (classified pixel) can provide a more detailed quantitative assessment of water cover within the pixel and consequently improve monitoring over time. This particularly applies during periods of slow but high variability, like those characterized by drought conditions. Among other existing data analysis techniques that allow us to estimate fractional water in pixels, SMA is a good candidate. Nevertheless, it does not represent a simple but effective method because it is computationally more demanding, requires a minimum number of spectral bands to run, needs a sensor-specific training set that makes it less easily portable to other sensors, and does not necessarily lead to a better result than the use of a fuzzy transform function on NDWI values.