The objective of this work was to carry out a first evaluation of the PRISMA hyperspectral data potentialities for Burned Area (BA) mapping in an operational context related to wildfire risk management (i.e., simulating a civil protection scenario). To this aim, an automatic processing chain was developed to map the area burned by the wildfire that occurred in Pantelleria Island (Southern Italy) during the Summer of 2022. The differenced Normalized Burn Ratio (dNBR) index was used to perform this task and a simple, yet effective, criterion was defined to identify the spectral bands most suitable for the analysis. Results showed the potential retained by PRISMA data to map BA ca. one month after the fire outbreak, in an operational context (Dice Coefficient: 0.88). Their discussion also provided valuable insights related to the exploitation of hyperspectral remote sensing data for similar purposes.

Monitoring the "degree of filling" of Water Bodies (WB) is of paramount importance for Civil Protection (CP) applications: e.g., flood control and hydrological drought. Within this context, this work aims at prototyping a system to monitor the spatiotemporal variations of WB extent at high spatial resolution, to support the Italian Civil Protection Department (DPC) activities. The system is based on optical Sentinel 2 data, but it also exploits Sentinel 1 SAR data as a backup source of information to take into account S2 limitations linked to the presence of clouds and shadows. The system performances were validated in a pilot area (i.e., the San Giuliano reservoir, located in Southern Italy), for which reference data were available. Results showed that the presented system has a strong potential to be a valuable tool to be used for CP applications, especially if data are analyzed in terms of monthly anomalies.

This study investigates the relations between the dynamics of satellite-based indicators of reservoir water extent and the stress of the surrounding vegetation during dry periods and in drought conditions. A qualitative and quantitative comparison was performed between the monthly values of indices as NDVI, NDMI, fAPAR and the changes in the water extent of the San Giuliano Reservoir (Southern Italy), also in terms of anomalies. The indices and the water extent were derived from Sentinel-2 data, and the analysis was performed for a four-year time period (2019–2023). The results suggest that the dynamics of the reservoir water extent can be used to detect dry periods and/or drought conditions, and that the vegetation state indicators can complement the analysis and reduce the source of uncertainty. Being based on the satellite data only, the analysis has the potential to be replicated elsewhere in the World, including data scarce environments.

A big flood affected the Emilia-Romagna region (northern Italy) starting from 16 May 2023. During this event, more than 20 rivers burst their banks and about 15 people were killed. A persistent satellite-based mapping was requested by the Italian Department of Civil Protection (DCP) to monitor the time evolution of the Emilia Romagna flood. For this purpose, the X-band SAR data provided by the COSMO-SkyMed constellations (first and second generation) were exploited. This paper proposes a new procedure to map floods using SAR data acquired with different viewing geometries in order to fully exploit the on-demand capability of satellites like COSMO-SkyMed. The procedure includes the tasking of the satellites, the collection of a set of pre-flood images, the selection for each flood image of the most suitable pre-flood ones, and the generation of the flood maps using an automatic algorithm based on change detection. These activities were performed during the May 2023 Emilia-Romagna flood and allowed us to daily provide DCP with flood maps useful to support decision making and assess the damages.

This work was conceived to analyze the impact of the different instances of the Copernicus DEM (CopDEM) dataset (named: EEA-10, GLO-30, GLO-90) on the orthorectification of Very High Resolution (VHR) optical data (spatial resolution: 2–4 m). Indeed, the CopDEM instances are characterized by different pixel sizes/spatial coverages/licenses (EEA-10: 10&12 m/European/restricted; GLO-30: 30 m/global/public; GLO-90: 90 m/global/public). Findings showed that all the CopDEM instances provided valuable topographic information that allows reaching similar values of geolocation accuracy. The latter is only slightly influenced by the different pixel sizes of the data. Moreover, this research provided valuable insights related to the existing relationships between the geolocation accuracy and: i) the spatial resolution of the VHR input products; ii) the orography and land cover of the target areas. The analysis also highlighted the importance of the GLO-30 instance for orthorectification purposes, as it is freely and globally available.

This work was conceived for assessing the potential of the Copernicus DEM (CopDEM) dataset to identify (flash) flood-prone areas by using a geomorphological approach. This dataset is distributed in different instances (named: EEA-10, GLO-30, GLO-90) characterized by different pixel sizes/spatial coverages/licenses (EEA-10: 10&12 m/European/restricted; GLO-30: 30 m/global/public; GLO- 90: 90 m/global/public). The analysis compared the performances of the different instances for the abovementioned application. An exemplifying Mediterranean catchment located in Italy was used as test area. Results showed that the CopDEM dataset was successfully capable of identifying (flash) flood-prone areas. The best results were obtained by the EEA-10 and GLO-30 instances. The analysis also highlighted the importance of the GLO-30 instance to map flood prone-areas, as it is freely and globally available. Moreover, the GLO-30 instance outperformed other freely available DEMs distributed with a pixel size of 30 m (i.e., ALOS AW3D30, ASTER GDEM, SRTM) that were analyzed for comparative purposes.

The recent surge in interest towards hyperspectral imagery has the potential to unlock a new range of applications for the scientific community. However, compared to traditional multi-spectral images, the workload required to process such high-dimensional data is dramatically increased, to the point that new and more flexible strategies must be developed in order to properly monitor the quality of this type of products. In the particular case of sharpness assessment, traditional procedures based on the edge method tend to be extremely time-consuming due to their reliance on visual analysis performed by human operators, and would make proper processing of all bands a daunting task to perform on a large scale. In this paper we propose a flexible and fully automatic approach to edge method-based sharpness assessment that can be applied inde-pendently from the number of spectral bands. We then present the results of the application of the methodology on the visible and near-infrared and shortwave infrared spectral cubes of a selection of PRISMA L2D images, which confirm the relia-bility of the methodology and suggest further improvements.

The Copernicus Coordinated data Quality Control (CQC) service is in charge of monitoring the quality of Earth Observation (EO) products and datasets generated by the Copernicus Contributing Missions (CCMs) and distributed within the Copernicus programme. To this aim, it develops and applies rigorous quality assessment procedures. Within this context, this paper describes the workflow designed by the CQC service for evaluating the quality of EO-derived Digital Elevation Models (DEMs). The paper also summarizes the outcome of the quality analysis performed on the Copernicus DEM dataset to assess its absolute vertical accuracy in Spain. This location was selected because of the availability of reliable reference elevation data to use for the assessment in areas characterised by different land cover and slope classes. Results showed that the dataset is compliant with respect to its nominal quality specifications.

The monitoring of the freeze/thaw dynamic of high-latitude Earth regions is of paramount importance for the study of the carbon cycle and the climate changes. Current approaches essentially rely on the use of active and passive microwave remote sensing, while limited work has been dedicated to study the potential of the Global Navigation Satellite System Reflectometry technique based on spaceborne platforms. In this contribution, reflectivity values derived from the TechDemoSat-1 data have been collected and elaborated, to be compared against the SMAP freeze/thaw product. The proposed analysis indicates a significant seasonal cycle of freeze/thaw state in the calibrated reflectivity, thus opening new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state.

The metrics traditionally used for assessing the sharpness level of optical imagery acquired by spaceborne sensors (e.g., relative edge response, point spread function, full width at half maximum of the line spread function, modulation transfer function) are usually measured before launch using a set of standard simulated inputs. However, vibrations occurring during launch and satellite deployment, as well as sensor degradation through time, may alter the nominal characteristics significantly. Therefore, post-launch assessment analyses are necessary for monitoring data quality. To this end, on-orbit measurement techniques for sharpness assessment - e.g., the edge method (EM) - were developed. Selection of suitable targets to be used for such techniques is a crucial step for carrying out the assessment successfully. The objective of this paper is to describe an automatic method for identification of suitable edges to be used for on-orbit sharpness assessment via the EM by taking into account the widespread presence of natural targets - like agricultural fields - on Earth. The presented approach is expected to ease the continuous monitoring of the sharpness level of optical data acquired by spaceborne sensors.

GeoSTARe is a proposed GEOsynchronous satellite mission designed to carry aboard a Synthetic Aperture Radar (GEOSAR) payload, which can provide radar images with unprecedented temporal resolution. The research activity described in this paper aimed at investigating the potentialities of such system for soil moisture (SM) monitoring finalized to hydrological applications. The objective was defining the GEOSAR requirements, in term of spatio-temporal resolution, for SM mapping. GEOSAR is capable to produce images with different sub-daily temporal resolution, with associated spatial resolution, according to the length of the synthetic antenna focused on ground. To this aim, three GEOSAR-derived SM products, characterized by different spatio-temporal resolutions (matching the expected performances of GEOSAR), were simulated. Then, these products were used in a synthetic hydrological SM data assimilation experiment to evaluate their impact on model discharge predictions. Results showed that, for such system, the best performances were obtained when the highest spatial resolution was used.

The objective of this research was to develop a method for water level retrieval in natural and artificial lakes. It was thought to be applied for monitoring purposes and flood control applications, especially in data-scarce environments. The method is based on a combined GIS, remote sensing and statistical modeling approach. It was tested on both optical (Landsat 8) and SAR (Cosmo-SkyMed®) data. The topographic information, required by the method, were obtained from freely available digital elevation models (SRTM and ASTER) to compare their performances. The Place Moulin Lake, an Alpine reservoir, was selected as study area since it represents a very challenging case study for developing the proposed methodology. The results showed that: i) the method provided reasonably accurate results when the degree of filling of the reservoir was high. ii) The accuracy of the results strongly relied on the accuracy of the topographic information. iii) The combination of Cosmo-SkyMed® and SRTM data provided more reliable results. Further analyses are required to evaluate the method in different environmental conditions.

As part of the Copernicus Programme, Sentinel 1 (S1) synthetic aperture radar (SAR) mission represents a unique monitoring tool whose potentialities for hydrological risk mitigation need to be evaluated. To this aim, S1-A derived soil moisture maps with high spatial resolution (100 m) and moderate temporal resolution (12 days) were assimilated within a time-continuous, spatially-distributed, physically-based hydrological model (Continuum) with the specific objective to evaluate the impact on discharge predictions and (flash) flood modelling. A Nudging assimilation scheme was chosen for the DA experiment due to its computational efficiency, particularly useful for operational applications. Results were evaluated in the Orba River catchment (Italy) in the time period October 2014 — November 2016, corresponding to the first two years of activity of the S1-A mission.

First results of the assimilation of high-resolution Sentinel-1A based soil moisture products in a distributed, physically based, hydrological model are presented. A comprehensive evaluation of the assimilation's impact on discharge predictions is provided. Results are further compared to those obtained when assimilating the lower-resolution ASCAT-based soil moisture product. The exercise was carried out within the MIDA project framework (funded by the Italian Space Agency) aiming at producing root zone soil moisture maps useful for flood risk management applications. The experimental site is the Orba River Catchment (Italy). The period of investigation is October 2014-February 2015. Using a relatively simple data assimilation technique (Nudging) the results of our case study show that overall the assimilation of currently available Sentinel-1 data only marginally improves discharge simulations. However, the impact becomes more significant when specifically considering predictions of high flow. Further improvements are expected when both Sentinel-1A and B data will be available.

Coastal zones are fragile and dynamic environments, most of the time largely urbanized and particularly vulnerable to natural hazards. Therefore, coastal areas are often exposed to high risk and shoreline position monitoring and modelling is required to mitigate it. In this context, satellite data are fundamental to provide synoptic and multitemporal information useful to map and model shoreline position through time. The aim of this work was to study the shoreline evolution of two selected areas, in Portugal and in Italy. Shoreline historical rates were obtained by analyzing Landsat images from mid-80's up to 2011. Subsequently, short-term scenarios (2014) were predicted and their accuracy was assessed by comparing 2014 modelled and observed shoreline positions. After that, Landsat 8 and Sentinel 1 images were exploited to extract and compare 2015 shoreline positions in a Data Fusion context. Finally, results were interpreted for their implications in the coastal risk reduction framework.

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.

Coastal areas represent relevant zones for environmental monitoring. They are characterized by several habitats that coexist and interact in a condition of dynamic equilibrium. Moreover they are sites of human settlements and important economic and commercial activities. Therefore, an accurate environmental characterization of these complex systems require a large amount of information and different levels of analysis. To date, the contribution by remote sensing to study coastal zones is widely accepted as it provides high quality tools and products to investigate and monitor these fragile ecosystems. In this framework, the aim of this work is to test the performance of the multispectral Daedalus Airborne Thematic Mapper (ATM-2) sensor for the interpretation and analysis of geo-environmental features of the Migliarino, San Rossore, Massaciuccoli Regional Park's coastal area.