Alice Fabbretto

Graphical abstract workflow diagram. Left: Satellite map of Lake Trasimeno with the WISPstation location. Center: List of 14 multi- and hyperspectral sensors. Right: Data processing steps from Level-2 images to BOMBER outputs, showing intra-day maps of SPM and CHL variations. Lake Trasimeno (Italy) is a shallow, highly dynamic lake that exhibits intra-day variability in optically active parameters such as suspended particulate matter (SPM) and chlorophyll-a (CHL), posing challenges for monitoring through traditional field-based techniques or a single satellite sensor. This study investigates the spatio-temporal dynamics in SPM and CHL using a synergistic approach that combines in situ data from an above-water fixed spectroradiometer (WISPstation) with multisensor satellite imagery collected between 2019 and 2024. Remote Sensing Reflectances derived from 14 satellites showed good agreement with in situ data, enabling the application of a bio-optical model, in three configurations based on the lake's trophic conditions, to retrieve SPM and CHL concentrations with high accuracy. Statistical analysis identified wind speed and air temperature as the primary drivers of SPM and CHL variability, respectively. Daily mapping highlighted the lake's high dynamism, while intra-day analysis revealed limited SPM changes influenced by local perturbations, whereas CHL exhibited a progressive increase throughout the day consistent with circadian algal growth patterns. These results demonstrate the capability of a multisensor satellite approach to capture rapid intra-day variations, suggesting promising applications for continuous, large-scale monitoring of other lake ecosystems.

This study investigates the potential of spaceborne imaging spectroscopy to support the analysis of the status of two major Estonian lakes, i.e., Lake Peipsi and Lake Võrtsjärv, using data from the PRISMA and EnMAP missions. The study encompasses nine specific applications across 12 satellite scenes, including the validation of remote sensing reflectance (Rrs), optical water type classification, estimation of phycocyanin concentration, detection of macrophytes, and characterization of reflectance for lake ice/snow coverage. Rrs validation, which was performed using in situ measurements and Sentinel-2 and Sentinel-3 as references, showed a level of agreement with Spectral Angle < 16°. Hyperspectral imagery successfully captured fine-scale spatial and spectral features not detectable by multispectral sensors, in particular it was possible to identify cyanobacterial pigments and optical variations driven by seasonal and meteorological dynamics. Through the combined use of in situ observations, the study can serve as a starting point for the use of hyperspectral data in northern freshwater systems, offering new insights into ecological processes. Given the increasing global concern over freshwater ecosystem health, this work provides a transferable framework for leveraging new-generation hyperspectral missions to enhance water quality monitoring on a global scale.

This study applied a decision theory approach to quantify the potential economic Value of Information (VoI) of Earth Observations (EO) based monitoring and forecasting services developed in PrimeWater project, for managing harmful algal bloom events at a recreational lake. VoI was estimated by comparing the expected costs when decisions are taken with limited information relying on regular monitoring campaigns, against the outcomes of decisions taken with "better information" conveyed by the PrimeWater services. Expected costs considered health impacts, monitoring costs, and lost recreational revenues in case of false alarms. Four PrimeWater monitoring and forecasting services were evaluated based on their accuracy in assessing bloom conditions against in-situ data for 2015-2019. Results suggest VoI varies seasonally and between services depending on accuracy metrics and underlying bloom probabilities. Forecasting solutions provide the greatest potential savings, highlighting needs for balanced metrics to avoid excess false alarms. Findings support flexible use of EO information to complement existing programs and inform science-based management aimed at reducing societal vulnerability to HABs.

This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R² > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

Shallow water habitats are among the most important and productive ecosystems on the planet. These ecosystems, which include seagrass meadows, are important biodiversity hotspots. The spread of seagrass can be managed by looking at the seabed cover and how it changes over the year. To this end, satellite-derived products of substrate and vegetation cover at different spatio-temporal resolutions can help water managers and users to better understand and manage seagrass beds in shallow waters (Kutser et al., 2022). The main objective of this study is to test and apply algorithms to obtain bottom substrate, canopy cover together with water quality maps (e.g. SPM, Chl-a) from hyperspectral (e.g. PRISMA) and multispectral (e.g. Sentinel-2) satellite data. The study is developed in the framework of the PRISMA SCIENZA OVERSEE project and focuses on the coastal areas of the Gulf of Oristano (Sardinia, Italy). Changes in substrate cover have been tracked for the period May-October 2022. Spatio-temporal variability of Posidonia oceanica has been studied and discussed in relation to environmental features and human activities.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

Climate change is having a significant negative impact on freshwater systems, which provide multiple ecosystem services. In this context, the present study aims to show an overview of the main objectives achieved by exploiting the hyperspectral reflectance data provided by the PRISMA sensor to map aquatic ecosystems. Water quality products were generated using three different approaches: the bio-optical model BOMBER, adaptive semi-empirical algorithms, and machine learning models. These methods were tested in very different waterbodies worldwide: five lakes, two lagoons and one river. To assess the accuracy of the water quality products, comparisons were performed with reference measurements. The results showed an average R ² = 0.70 and encourage using PRISMA data for aquatic applications in synergy with existing multispectral and future hyperspectral data.

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.