The European Union's Earth Observation program, Copernicus, aims to create vast amounts of global data from satellites and ground-based, airborne and seaborne measurement systems with the goal of providing information to help service providers, public authorities, and other international organizations improve European citizens' quality of life. With the aim of reaching this goal, a new family of missions called Sentinels has been developed by the European Spatial Agency, ESA, specifically for the operational needs of the program. These missions carry a range of cutting-edge technologies, such as radars and multi-spectral imaging instruments, for land, ocean and atmospheric monitoring. Multiple kinds of high-resolution data are now available to the scientific community, which is working to adapt and develop the existing models and algorithms to the new information.With this objective, two of the most important variables that contribute to the water cycle, rainfall and river discharge, were selected in this thesis to be estimated by the use of the information obtained from Sentinel-1 and Sentinel-2 sensors. The monitoring of these two variables is fundamental in many hydrological applications, like flood and landslide forecasting and water resources management, and their impact is clearly visible from space. In-situ measurements are the traditional data source of them, but the worldwide declining number of stations, their low spatial density and the data access problem limit their use. Satellite sensors have been therefore adopted to support and, in some cases, substitute the existing gauge network in estimating river discharge and rainfall, thanks to the strong growth in technologies and applications. Two valuable examples of this are SM2RAIN algorithm, which allows to estimate rainfall from Soil Moisture (SM) observations by exploiting the inversion of the soil water balance equation, and the CM approach, a non-linear regression model capable of linking the ground measurements of river discharge to the near-infrared (NIR) reflectance ratio between a dry and a wet pixel chosen around the border of a river. Notwithstanding their usefulness, until now several limitations affected these two methodologies. The main issue with satellite derived rainfall data was their low spatial resolution which could not overcome 10 km, a quantity insufficient to obtain accurate information over many areas and posing important constraint on their use for many applications and fields, which require more and more detailed information. Similarly, the resolution of the available NIR data was not suitable to provide information for narrow rivers (< 250 m wide), nor to study river features and patterns that were here averaged within a single pixel. The recently available high-resolution data from Sentinel Missions of the Copernicus program offer an opportunity to overcome these issues.The data from Sentinel-1 mission can be used to obtain a high spatial resolution SM product, named S1-RT1, which is adopted in this thesis to derive 1 km spatial resolution (500 m spacing) rainfall data over the Po River basin from it, through the algorithm SM2RAIN. The rainfall derived from the 25 km ASCAT SM product (12.5 km spacing), resampled to the same grid of S1-RT1, is compared to the latter to evaluate the potential benefits of such product. SM2RAIN algorithm needs to be calibrated against a benchmark, which poses important limitations on the applicability of the analysis in data scarce regions. In order to overcome this issue, a parameterized version of SM2RAIN algorithm is previously developed relying on globally distributed data, to be used along with the standard approach in the high-resolution rainfall estimation. The performances of each obtained product are then compared, to assess both the parameterized SM2RAIN capabilities in estimating rainfall and the benefits deriving from Sentinel-1 high spatial resolution.For the river discharge estimation, the use of Sentinel-2 NIR reflectances within the CM approach is investigated to support the hypothesis that a higher satellite product’s spatial resolution, i.e., 10 m (vs. a medium-resolution, i.e., 250 m), is able to better identify the periodically flooded pixels, more related to the river dynamics, with obvious advantages for river discharge estimation. Moreover, the improved resolution allows both a finer distinction between vegetation, soil and water and the characterization of water turbidity in the river area, which is important to correctly estimate the river discharge using this approach. A new formulation enriched by the sediment component is proposed along with a procedure to localize the periodically flooded pixels without the intake of calibration data, which is a first step towards a completely uncalibrated procedure for the river discharge estimation, fundamental for ungauged rivers.The obtained results show that the high-resolution information from Copernicus actually increase the accuracy of the satellite derived products. Good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 day, since to the low temporal resolution of this sensor (from 1.5 to 4 days over Europe) prevents its application to infer daily rainfall. In particular, the rainfall estimates obtained from Sentinel-1 sensors outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high spatial resolution information in obtaining spatially detailed rainfall. The use of a parameterized version of SM2RAIN produces performances similar to those obtained with SM2RAIN calibration, attesting the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide even without the availability of a rainfall benchmark product. Similarly, the river discharge estimation from Sentinel-2 reflectances from selected stations along two Italian rivers, the Po and the Tiber, confirms that reliable performance can be obtained from high-resolution imagery. Specifically, over both the stations the new formulation improves the river discharge accuracy and over the Po River the best performances are obtained by the uncalibrated procedure. Google Earth Engine (GEE) platform has been employed for the data analysis, allowing to avoid the download of big amounts of data, fostering the reproducibility of the analysis in different locations.
