The monitoring of water biophysical parameters and the management of aquatic ecosystems are crucial to address the current state of degradation of inland and coastal waters. Water quality monitoring not only supports management decisions, but also provides important insights to better understand changing structural and functional processes. To date, most applications developed for inland waters have relied on multispectral and medium to coarse resolution satellites, while a new generation of spaceborne imaging spectroscopy is now available and future global missions are under development. This study aims to evaluate both the Level 1 PRISMA products, in different processing versions, and the Level 2 products based on coincident in-situ data collected in different aquatic ecosystems. Examples of water quality mapping in different use cases will then be provided, also in synergy with other missions such as EnMAP and Sentinel-2.

Lakes are crucial sources of freshwater and significant indicators of environmental change on a global level. They are reacting quickly to climate change, and it is expected that in coming decades global warming will have an even more pronounced impact on biodiversity, nutrient cycling, and hydrology. To support the comprehension of this topic at global scale as well as for understanding the complicated behaviour of lakes in a changing environment, satellite technologies provide a unique source of data. In such a context, Lakes_cci project recently provided the most complete collection of consistent satellite observations water level, water extent, lake surface water temperature, water-leaving reflectance and related chlorophyll-a concentration and turbidity and ice cover. The products span from 1992 to 2020 and quantifies over 2000 relatively large lakes, which represent a small fraction of the number of lakes worldwide but a significant fraction of global freshwater surface. To illustrate the project's scientific contribution, two use-cases are described in this study along with the main properties of the dataset.

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.

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 R2= 0.70 and encourage using PRISMA data for aquatic applications in synergy with existing multispectral and future hyperspectral data.

Earth Observation derived land cover products can provide an advance insight into coastal ecosystem monitoring to capture the fragmentation and changes of land surface. The work addresses the testing of consolidated classification algorithms on hyperspectral data in synergy with SAR and field data for the definition of biological and geophysical characteristics of surface coverage components to produce a detailed Dynamic Land Cover map. We obtain reliable land cover and land use information identifying different types of vegetation and soils condition and indicators of biogeophysical surface processes in the testing costal area of the Gulf of Oristano (Italy). The results offer further classification of specific land cover targets representing a contribution for the production of land cover products at different levels of thematic resolution to improve downstream application services and to support land policies and management.

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.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is a demonstrative spaceborne mission, fully deployed by the Italian Space Agency (ASI). To support the calibration/validation activities of the PRISMA hyperspectral mission, ASI and the National Research Council (CNR) started in 2019 the PRISCAV project (Scientific CAL/VAL of PRISMA mission). The main objective of PRISCAV is the comprehensive characterization of the performances of the PRISMA payload in orbit in different operational scenarios and the verification of the durability in time of the performances. To this end, PRISCAV created a network of 12 instrumented sites showing different land-use and surface settings (Snow; Sea; Inland and Coastal Water; Forest and Cropland) to obtain independent and traceable in-situ and airborne Fiducial Reference Measurements (FRM) simultaneous to PRISMA acquisitions in order to assess the required performance of sensor, data products, and processors at the different levels (i.e. Top-of-Atmosphere Level 1 Radiances and Bottom-of-Atmosphere Level 2 Reflectance standard products). Moreover, on some of these sites, simultaneous PRISMA and airborne AVIRISNG acquisitions were made coupling remote sensing with in-situ observations to support new mission development and in particular the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME). Recent updates on CAL/VAL activities and on AVIRSNG campaigns are presented in this contribution.

This study demonstrates the use of a novel satellite remote sensing approach to map coastal erosion vulnerability in the Italian site of Piscinas (Sardinia). We focused on the land/water transitional ecosystem, to identify potential coastal erosion phenomena. For this analysis, a synergistic approach between multi-spectral satellite data (Sentinel-2) and SAR imagery (COSMO-SkyMed and Sentinel-1B) was exploited. Two vulnerability maps were created: one longterm (2016–2018) and one short-term (wind event). The results confirm how the coastal vulnerability of this site seems to be linked to episodic events, consequently, the dune system of Piscinas might be considered safe from coastal erosion processes.

This study presents an assessment of PRISMA (PRecursore IperSpettrale della Missione Applicativa) for water applications, by including a general description of the mission and by focusing on the standard Level 1 (L1) and Level 2 (L2) products acquired on different aquatic systems. A preliminary analysis on the estimation of signal-to-noise ratio (SNR) from L1 PRISMA products in the visible near infrared spectra (VISNIR) is indicating values comparable to Sentinel-2 MSI from literature, apart at short wavelengths where PRISMA SNR is lower. Few examples of L2 products in terms of water reflectance are also presented to encourage the exploitation of PRISMA for aquatic ecosystem mapping. The results of this study are indicating that even if some applications might be developed with the standard L2 products, an ad-hoc processing might be necessary to include the specific requirements of atmospheric correction of water targets. Further analyses with a higher number of imagery data are however recommended to fully characterize the PRISMA products.

PRISMA, in orbit since March the 22nd 2019, opened the user access in spring 2020. After one year, many hundreds of users have gained the capability to program new acquisitions or download image products from the online archive containing more than 67k datasets, under a quasi-open & free data policy and licensing scheme. During this time frame and despite the COVID-19 related difficulties, PRISMA performed normal operations delivering not only nominal verified quality data to users but establishing collaborations with other space agencies, in order to enable and support synergies with other hyperspectral missions. This paper describes the mission and the results achieved in this first period of full operational usage.

This work deals with the retrieval of distribution of seabed vegetation cover (essentially Posidonia Oceanica) in the coastal waters (1-10m) of the Gulf of Oristano. A physical approach was used to derive bottom cover map from airborne and satellite imagery (MIVIS, KOMPSAT-2 and RapidEye) acquired in summer 2001, 2011 and 2013. A bathymetric map was also derived from the RapidEye image of 2003. A field campaign was held on 1-8 August 2013 to parameterize the physical model and to validate the 2013 results. Accuracy of bottom cover map and regression between measured and modelled water depths was good providing an overall accuracy of 0.88% and a correlation coefficient of 0.84, respectively. The retrospective analysis revealed that P. oceanica meadow seems to be stable over the 12 years showing only very small variations (-0.2km2). Sea bottom morphology and vegetation cover were also related.

This letter presents an application of continuous wavelet analysis, providing a new semi-empirical approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal to estimate concentrations of optically active water quality constituents. However, they cannot cope easily with shifts in reflectance features caused by multiple interactions between variable absorption and backscattering effects that typically occur in optically complex waters. We use continuous wavelet analysis to detect Chl-a features at various wavelengths and frequency scales. Using the wavelet decomposition, we build a 2-D correlation scalogram between in situ pond reflectance spectra and in situ Chl-a concentration. By isolating the most informative wavelet regions via thresholding, we could relate all five regions to known inherent optical properties. We select the optimal feature per region and compare them to three well-known narrow band ratio models. For this experimental application, the wavelet features outperform the NIR-red models, while fluorescence line height (FLH) yield comparable results. Because wavelets analyze the signal at different scales and synthesize information across bands, we hypothesize that the wavelet features are less sensitive to confounding factors, such as instrument noise, colored dissolved organic matter, and suspended matter.

In this study, in situ data and hyperspectral MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) images collected over a period of 14 years were used to assess changes in submerged macrophytes colonization patterns in southern Lake Garda.

The applicability of MERIS data for the retrieval of water constituent concentrations in oligo- to mesotrophic perialpine lakes is demonstrated by means of two different algorithms. The C2R algorithm is an easily applicable neural network processor which is bound to MERIS data. The MIP algorithm is a complex, coupled inversion program, which can be used with a variety of remote sensing sensors, but requires extensive parameterization. Both algorithms were applied with the ICOL adjacency effect correction. C2R's potential for automatic processing of large data quantities is validated in a time series study with water quality monitoring data. Individual C2R image products are then compared to MIP, which will allow for an extended comparison with new APEX imaging spectrometry data in the near future.