The precise delineation of coastal areas subject to past, present, and future erosive processes plays a fundamental role in coastal risk management. Within this framework, satellite data represent a valuable synoptic and multi-temporal information source. Therefore, this research integrated remote sensing and GIS techniques for mapping and modeling shoreline evolution through time. Long-term shoreline’s proxy rates of advance and retreat were determined using Landsat data from the mid-1980s to 2011 and subsequently, a short-term scenario (3 years) was predicted and validated. Two different coastal environments, Oceanic and Mediterranean, were investigated. In the first, different proxies were analyzed, thereby enabling a multi-proxy analysis. Findings showed that the method provided more accurate results in higher energy environments (Oceanic) and where the coastline is not urbanized. Results also highlighted the importance of performing multi-proxy analyses in given study areas, to more reliably define shoreline modeling. Importantly, during the analyses, particular attention was given to assessing uncertainty, which is crucial when outcomes of scientific research are considered for management.

The study of the freeze/thaw dynamic of high-latitude Earth surfaces is extremely important and informative for monitoring the carbon cycle, the climate change, and the security of infrastructures. Current methodologies mainly rely on the use of active and passive microwave sensors, whilst very few efforts have been devoted to the assessment of the potential of observations based on signals of opportunity. This work aims at assessing the performance of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) for high-spatial and high-temporal resolution monitoring of the Earth-surface freeze/thaw state. To this aim, reflectivity values derived from the TechDemoSat-1 (TDS-1) data have been collected and elaborated,and thus compared against the SMAP freeze/thaw fraction information. Shallow sub-surface soil temperature values recorded by a network of in situ stations have been considered as well. Even if an extensive and timeliness cross-availability of both types of experimental data is limited by the spatial coverage and density of TDS-1 observations, the proposed analysis clearly indicates a significant seasonal cycle in the calibrated reflectivity. This opens new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state, as well as to support the development of next-generation of GNSS-R satellite missions designed to provide enhanced performances and improved temporal and spatial coverage over high latitude areas.

A reliable estimation of soil moisture conditions is fundamental for rivers' discharge predictions, especially in small catchments where flash floods occur. In this context, microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using data assimilation techniques. Flash flood early warning systems can, thus, be improved. This study tested the effect of the assimilation of three different ASCAT-derived soil moisture products, processed and distributed within the EUMETSAT H-SAF framework (SM-OBS-1, SM-OBS-2, SM-DAS-2), into a distributed physically based hydrological model (Continuum). The study areas were three Italian catchments, representative of the typical Mediterranean small basins prone to flash floods. The products were first preprocessed in order to be comparable with the model soil moisture state estimate. Subsequently, they were assimilated using three Nudging-based techniques. Then, observed discharges were compared with the modeled one in order to understand the impact of the assimilation. The analysis was executed for a multiyear period ranging from July 2012 to June 2014 in order to test the assimilation algorithms for operational purposes in real-cases scenarios. Findings showed that the assimilation of H-SAF soil moisture products with simple preprocessing and assimilation techniques can enhance discharge predictions; the improvements significantly affect high flows. Although SM-OBS-2 and SM-DAS-1 are added-value products with respect to SM-OBS-1 (respectively, higher spatial and temporal resolution), they may not necessarily perform better. The impact of the assimilation strongly relies on the permanent catchment characteristics (e.g., topography, hydrography, land cover).

Satellite remote sensing data are often used to extract water surfaces related to extreme events like floods. This study presents the Multi INDEx Differencing (MINDED) method, an innovative procedure to estimate flood extents, aiming at improving the robustness of single water-related indices and threshold-based approaches. MINDED consists of a change detection approach integrating specific sensitivities of several indices. Moreover, the method also allows to quantify the uncertainty of the Overall flood map, based on both the agreement level of the stack of classifications and the weight of every index obtained from the literature. Assuming the lack of ground truths to be the most common condition in flood mapping, MINDED also integrates a procedure to reduce the subjectivity of thresholds extraction focused on the analysis of water-related indices frequency distribution. The results of the MINDED application to a case study using Landsat images are compared with an alternative change detection method using Sentinel-1A data, and demonstrate consistency with local fluvial flood records.

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR.

A reliable estimation of soil moisture conditions is fundamental for discharges prediction and, consequently, for flood risk mitigation. Microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using Data Assimilation techniques and to reduce model uncertainties. This research tested the effects of the assimilation of three different satellite-derived soil moisture products (obtained from ASCAT acquisitions) in a distributed, physically based, hydrological model applied to three small Italian catchments. The products were firstly preprocessed, in order to be to be comparable with the state variables of the model. Subsequently they were assimilated by using different techniques: a simple Nudging applied at both model and satellite scale and the Ensemble Kalman Filter. Finally, observed discharges were compared with the modelled ones. The reanalysis was executed for a multi-year period ranging from July 2012 to June 2014.