# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 1 Introduction
Italian National Institute for Environmental Protection and Research (ISPRA) developed the IDRAIM methodology based on an integration of remote sensing, GIS, and field surveys for the hydomorphological quality assessment of Italian streams [18]. Mapping surface water bodies from remote sensing data includes several approaches suitable for different specific objectives, scales (local, regional, or global), and sensor characteristics [19; 20; 21]. Methodologies to detect water pixels from synthetic aperture radar and optical imagery include supervised and unsupervised classification [22], water index thresholding [23], object segmentation [24], spectral mixture analysis (SMA) [20; 25], multidimensional hierarchical clustering [21], and deep learning [26]. A wide number of supervised machine learning models used for water mapping are available in the literature, including methods like decision trees [27], support vector machines [28], random forests [29], multilayer perceptron [30], and convolutional neural networks [31]. Detecting water from optical imagery is more straightforward than using synthetic aperture radar, although the latter has the advantage that it is not affected by cloud or weather conditions and can detect water through a thin canopy. For automatic water detection, the most commonly used methodologies include pre-trained machine learning algorithms, while the majority of developed methods rely on thresholding in one or more spectral bands or water-related spectral indices [26]. While supervised machine learning models are preferable over thresholding for accurate results, they might not be the best option when multiple sites at a global scale are addressed [32]. This is due to different water constituents (sediments, organic dissolved matter, and chlorophyll) and atmospheric conditions at different sites, other than their dependency on the training data [21]. Remote sensing optical multispectral imagery at high and very-high spatial resolution, acquired with high revisit frequency, has great potential for monitoring small surface water bodies and water dynamics [10]. Water, vegetation, and bare ground represent the most common cover types of Earth's surface, reflecting solar radiation differently in various wavelengths. Water only reflects in the visible spectral wavelengths and has almost no reflection in the near-infrared range, making it very distinct from other surfaces. Water pixels will, therefore, be clearly detected from low reflectance values in the near-infrared range. Various spectral indices, such as normalized difference water index (NDWI), modified NDWI (MNDWI), multispectral water index (WuWI), automated water extraction index (AWEI), normalized difference moisture index (NDMI), and simple water index (SWI) have been designed to classify water pixels from remote sensing optical multispectral data. Although remote sensing provides a comprehensive database for hydrological monitoring, the utilization of satellite images necessitates the input of specialist expertise and a specific analytical approach (e.g., GIS analysis and data processing), in addition to high spatial and temporal resolution [14]. Moreover, integrating near-real-time observation with hydrological and meteorological models enables the description of hydromorphological dynamics, weather-climate-induced variations, and permanent transformations associated with extreme events [10]. The majority of developed methods rely on thresholding in one or more spectral bands or water-related spectral indices [26] to identify water pixels. The pixel classification approach, however, does not take into account the gradient of water coverage in the subpixel, reducing the percentage of water content in the pixel to a water/non-water pixel. Keeping the information about percentage water content (PWC) in pixels is a requirement for a more detailed quantitative assessment of hydromorphological processes at the metric scale when working on data with a spatial resolution of a few meters.