# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 4 Discussion
### Lessons Learned and Future Perspectives  
Despite its multiple strengths, the proposed methodology is not without intrinsic limitations. For instance, some errors may occur, above all in heavily vegetated or urbanized regions, due to reflection patterns (adjacency effect) or object shadows similar to water spectra [56]. Cloud cover may limit the availability of satellite observations, usually during autumn and spring periods. Moreover, snow, ice, and frozen river systems can generate additional complexities [33]. Sun-glint affects water pixel detectability, since it may contaminate Sentinel-2 imagery acquired during late spring and early summer periods. A metric of robustness based on missing data would also be useful. However, the use of a standardized pre-processing procedure to remove bad pixels is essential to have consistent data quality and maximize information [15]. Concerning the choice of spectral index to use, the NDWI can enhance information about water bodies and restrict information about vegetation and soil features, but it cannot completely distinguish built-up features from water bodies. Short-wave infrared-based spectral indices have a lower impact from subpixel vegetation components, even if such radiometric interval is not available in most of very-high-resolution satellite optical multi-spectral sensors or it is available at a lower spatial resolution. Moreover, NDWI thresholds vary depending on the proportions of subpixel water and non-water components. The proposed approach could be applied elsewhere, provided that possible adjustment of the threshold values of NDWI used for fuzzy transform, based on actual site conditions, is taken into account. It should be considered that using a threshold value larger than 0.1 on the NDWI calculated from Sentinel-2 MSI imagery, leads to a severe decrease in water maps classification performances [57]. NDWI threshold could, for example, be adjusted to match a reference dataset that has a finer spatial resolution [52]. Field surveys are still required to validate and integrate with the hydromorphological analysis conducted using satellite data. This will mitigate the misclassification errors resulting from the automated procedure and enhance the precision and efficiency of monitoring, particularly in the case of smaller river systems [58]. The reference period for the calculation of anomalies in MWF was limited to the period 2016-2020 due to the unavailability of Sentinel-2 MSI satellite acquisitions during previous years. Such a reference period is far from being considered a climatology, which is typically calculated over a 30-year period. Copernicus Sentinel-2 satellites constellation offers a relatively high temporal resolution due to dense revisit time satellite acquisitions. However, the spatial resolution of 10 m limits the applicability of the proposed methodology to watercourses with active channels wider than 50 m [15; 59]. Therefore, the narrower river networks, like most mountain and small river watersheds, where the effects of climate change could be even more dramatic, are excluded from the analysis using Sentinel-2 MSI imagery [1; 15]. The analysis focused on the last river stretches of two tributaries before the confluence with the Po, namely Trebbia River and Oglio River, demonstrates the effectiveness of using PWC to account for the gradient of water cover within pixel, extending the use of datasets at 10 m spatial resolution to rivers with active channels less than 50 m wide. The exploitation of higher spatial resolution satellite imagery acquired with optical multispectral sensors, which is increasingly available and can even be used in synergy with the Copernicus satellite acquisitions, would overcome the spatial resolution limitation. Many of the newer constellations of CubeSat satellites provide optical multispectral data at a very high spatial resolution, even at daily revisit frequency.