This study reports the outcomes of the third Atmospheric Correction Intercomparison Exercise (ACIX-III Aqua), which evaluated the performance of atmospheric correction (AC) methods for hyperspectral PRISMA satellite data over inland and coastal waters. The exercise included five AC processors (ACOLITE, hGRS, iCOR, MIP, and POLYMER), the standard PRISMA Level 2C product, and an adjacency correction tool (T-Mart) tested with ACOLITE. A total of 239 cloud-free PRISMA scenes from 2019 to 2024 were compared with in situ data of remote sensing reflectance, gathered from both hyperspectral and multispectral radiometers across eight distinct optical water types (OWTs). The accuracy of each AC method varied with spectral band, but all showed largest and lowest discrepancies with in situ data at 443 nm and 560 nm, respectively. All AC methods showed the best agreement with in situ data in greenish waters (OWT 4b) and highest uncertainties were yielded in humic-rich waters (OWT 7). Consistently with the previous ACIX-Aqua study focused on multispectral data, no single AC method outperformed the others across all OWTs. The study confirmed the ongoing challenges of AC over optically complex waters, yet the exercise allowed the community to advance in developing AC methods for hyperspectral satellite images and supporting the development of future operational hyperspectral missions, such as PRISMA Second Generation (PRISMA 2G) and CHIME.

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.

Planning for Airborne Lidar Bathymetry (ALB) campaigns can benefit from knowledge of water clarity conditions of the intended survey area. Analysis of a historical time series of water clarity data to optimize Lidar penetration can assist to determine the most effective time for the survey campaign. This Water Quality (WQ) time series is extracted from satellite-derived data recorded by high resolution multispectral satellites. As the volume of spatio-temporal data produced from multiple sensors over a 30+year archived dataset can be overwhelming, a simple webapp is introduced to assist visualization and analysis. This article shows the methods of WQ analysis using high resolution satellite sensors, the webapp and how it benefits ALB survey campaigns, namely in the reduction of project risks and increased efficiency. Additionally, we highlight Satellite-Derived Bathymetry (SDB) technology as a supplemental tool to serve mapping and monitoring of shallow water zones globally. Like WQ, SDB requires scientific data analysis and a scalable and standardized IT infrastructure. We introduce SDB-Online as such a solution enabling the spatial mapping and monitoring of bathymetry and how it adds value to ALB and other survey methods.

This dataset contains samples of the Water Quality Features' Changes service of the WQeMS H2020 project.

It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), meteorological variables, and hydrological data are considered as possible drivers of the spatial and temporal variability of water quality (WQ) of the Banja reservoir (Albania). Sentinel-2 and Landsat 8/9 images are analyzed to derive WQ parameters and vegetation properties, while the HYPE model provides hydrological variables. Timeseries of the considered variables are examined using graphical and statistical methods and correlations among the variables are computed for a five-year period (2016–2022). The added-value of integrating earth observation derived data is demonstrated in the analysis of specific time periods or precipitation events. Significant positive correlations are found between water turbidity and hydrological parameters such as river discharge or runoff (0.55 and 0.40, respectively), while negative correlations are found between water turbidity and vegetation descriptors (−0.48 to −0.56). The possibility of having easy-to-use tools (e.g., web portal) for the analysis of multi-source data in an interactive way, facilitates the planning of hydroelectric plants management operations.

This dataset contains satellite-derived water quality (WQ) data of Lake Hume (Australia) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).

This dataset contains satellite-derived water quality (WQ) data of Lake Harsha (USA) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).

Satellite remote sensing provides valuable data for understanding spatial and temporal variability of water quality parameters. In particular, PRISMA and DESIS are showing increasing capabilities in water quality mapping even if further studies might be needed to fully exploit these relatively new data. In this context, high-resolution airborne hyperspectral sensors might support preliminary tests to evaluate satellite-based systems, while represent enhanced mapping tools. In the context of the H2020 PrimeWater project, this study presents the use of HySpex hyperspectral airborne data of the freshwater reservoir Mulargia (Italy) to: i) calibrate and validate algorithms to retrieve optically active parameters; ii) assess the feasibility of retrieve water quality parameters; iii) export of this knowledge on PRISMA and DESIS data. The Hyspex images were acquired on 24 September 2020 in coincidence with in situ data, both radiometric and water quality. HySpex data were geocoded with PARGE and atmospherically corrected with ATCOR code and the results showed a good consistency between water reflectance with in situ data. The retrieval of the optical properties from imagery was achieved with a spectral inversion of bio-optical modelling (namely BOMBER), parametrized with absorption and backscattering data of the site. The estimates matched well field data (r(2)> 0.8) and depicted the mesotrophic conditions of the reservoir with higher Total Suspended Matter in shallow coastal waters.

Within the H2020 project PrimeWater, EOMAP is generating operational satellite-based water quality datasets for four different case studies in Europe, Australia and the USA. The applied physics-based retrieval algorithm Modular Inversion and Processing system (MIP) accounts and corrects for a variety of environmental impacts, such as atmospheric conditions, adjacent land cover, water surface and composition of water constituents as well as considering varying observation geometries and sensor properties. It delivers main water quality parameters like turbidity, chlorophyll-a concentrations, secchi disk depth, water temperature and a harmful algae bloom indicator which is sensitive to cyanobacteria. With automated routines for satellite archive access, the fully operational water quality product processing chain as well as the automated delivery mechanisms, the datasets are ingested directly into the online accessible PrimeWater platform and are further used for assimilation with modelled data. Based on Copernicus missions of Sentinel-2A/B combined with additional data from Landsat 8, spatial resolutions down to 10m pixel size allow monitoring of very small water bodies and ponds up to larger reservoirs, such as the Western Water Treatment Plant near Melbourne in Australia, Lake Harsha in USA, Lake Hume in Australia and Lake Mulargia and Flumendosa reservoir in Italy. For these use cases, all available records from 2015 onwards have been processed, with processor-internal quality control mechanisms screening automatically for e.g. cloud shadows or high influences of aerosol, resulting in pixelwise masking of unreliable data. Next to operational monitoring of water quality, the generated time series indicate clear spatial dynamics and seasonal trends.

This dataset contains the surface reflectance Hyspex images derived with ATCOR code by CNR of Lake Mulargia (Sardinia, Italy). The acquisition was done by CGR Spa (Italy).

The EC HYPOS (HYdro-POwer-Suite) project (https://hypos-project.eu/) has the main goal of assessing the environmental impact of existing and future hydropower systems. The project will provide a suite of data analysis applications which integrates Earth Observation (EO) technologies and hydrological modelling. These include an online Decision Support Tool (DST) for investment planning and monitoring, as well as a subscription portal combining satellite data over time, current measurements and detailed estimates for present and near future assessments. A dedicated analysis on the “blue footprint” (i.e. the amount of water used to produce a service) of reservoirs is included for addressing sustainable monitoring solutions. Such analysis comprises the evaluation of the climate change effects on reservoirs management and hydropower production. For instance, extreme weather events like short-term heavy precipitations are connected with flooding and transport of large amounts of sediments in dammed reservoirs, with critical consequences for their management. Similarly, global warming can heat the surface of water bodies and induce higher evaporation rates, thus decreasing the amount of water available for energy production. In this study we present the first products from HYPOS project. These products are representative of what can be generated within the DST using elaboration techniques of EO data. Gridded products of water quality parameters (e.g. water turbidity, Chlorophyll-a concentration, suspended sediments concentration) are generated for the test sites of the project, which are small dammed reservoirs located in Switzerland, France, Albania and Georgia. These products are obtained using the Modular Inversion and Processing System (MIP), a sensor independent image processing chain based on radiative transfer models, which works in a multi-layer system, solving the light transfer in the atmosphere, at the water surface and inside the waterbody. For the assessment of the “blue footprint” of a reservoir, the water loss due to evaporation is computed by applying a consolidated mass transfer evaporation method to EO data. The resulting evaporation rates are first compared with the outputs of semi-automatic evapotranspiration EO-based models (e.g. SEBAL), and then with the estimates ​​obtained from two different numerical models: a hydrological model (E-Hype) and a 3D hydrodynamic model (Delft3D). The key parameters influencing water evaporation rates, their behavior and the issues related to each approach are analyzed. The first comparison results are made for lake Garda, where a complete set of data is available for the production of evaporation maps.

Freshwater as one of the most relevant resources for life is facing increasing human made pressures. Suitable information about the status of the water quality in lakes and rivers is sparse, although required for environmental assessments and impact monitoring: There is a vast demand on actual data in many countries, where water policies and management decisions are based on scarce and unreliable information. Satellite data with newest data analytics technologies can already contribute to this today with regular mapping and monitoring in freshwater systems: Consistent information of valuable water quality products are derived for single applications in small lakes, covering extended river basins or the whole world, as provided by the UNESCO World Water Quality Portal. A number of examples from this first global water quality portal is discussed, addressing ecological and economic issues in Africa. At the conceptual level, UNESCO and EOMAP advocate the long-term consistency of the data of these new measurement capabilities: Both satellite sensing and data processing technologies are rapidly evolving. Hence, nowadays concepts should already ensure that the data products are globally intercomparable and in future, even if the accuracy of information products become better and better. This ensures that the sustainable development goals can be supported with meaningful, comparable indicators over time.

Providing relatively fine spatial resolution multispectral data, Landsat-8, Landsat-7 (L8 and L7, respectively) and Sentinel-2 (S2) from 2013 to 2018 have been used in this study for enabling high-frequency monitoring of water quality of two small (the smaller with an area of 1.6 km2) freshwater dammed reservoirs. Located in Sardinia (Italy) and Crete (Greek), respectively, Mulargia and Aposelemis represent vital resources to supply drinking water in downstream valleys. A total of 400 cloud-free satellite images were turned into information on water quality by using an image processing chain implementing physically based methods for retrieving chlorophyll-a concentration (Chl-a), turbidity, Secchi disk depth (SDD) and surface water temperature. These estimates have been successfully validated (the lower Pearson correlation r was 0.88 for Chl-a) with 23 match-ups of in situ and satellite data. Results of the multi-temporal analyses showed a decrease of SDD due to the increase of Chl-a in Aposelemis or an increase of turbidity in Mulargia. For both freshwater reservoirs, the satellite-derived trophic state index assigned both lakes to mesotrophic conditions. The results finally suggested the effectiveness of S2 and Landsat in increasing, for the latest investigated years, the frequency of observations.

Satellites that capture large areas with high spatial and temporal resolution allow extensive analyses of water bodies and thus represent an ideal supplement to existing in situ point measurements. In the joint project WasMon-CT (Water Monitoring of Chlorophyll and Turbidity) the usability of satellite data for official monitoring of flowing waters and lakes was examined. The subproject at the Institute for Lake Research of the LUBW focused on satellite-based monitoring of chlorophyll a, an important indicator for water quality, in lakes. Freely available data from spatially reasonable high-resolution satellites, e.g. Sentinel-2, open up new possibilities for monitoring the water quality of a larger number of small lakes. The aim of the comprehensive validation study presented here was to get information about applicability and potential limitations of remote sensing techniques for different types of lakes. EOMAP processed the satellite data used in the validation (Sentinel-2/3, Landsat 7/8 and MODIS) by applying its Modular Inversion and Processing System MIP. Results extracted from satellite data between 2000 and 2017 were compared with in situ measurement data of about 20 lakes in Baden-Wuerttemberg, including Lake Constance, for water quality parameters such as chlorophyll a and Secchi depth. First results of the validation study show that in general the statistical values, e.g. annual mean values of in situ and remote sensing retrieved chlorophyll a and Secchi depth data, agree well, but some systematic differences occur. Further validation and data interpretation steps take into account methodical differences as well as time differences between in situ and satellite measurements.

The contribution of mineral-rich suspended matter (MSM) to the optics of water bodies is still less treated by bio-optical modeling than that of other water constituents. However, with the increasing number of remote sensing studies on inland waters, optical properties of terrestrial particles gain importance for accurately estimating particle concentrations. We compared two current simulation tools, Hydrolight and WASI, for high MSM concentrations within the realistic context of catchments with glacial erosion. The study area is an extreme form of suspended sediment-dominated Case2 water. We simulated Rrs(0-) spectra with MSM concentrations varying from 5 to 200 g m-3. In a second step, WASI-2D was applied to invert Landsat8. In-situ measured concentrations and reflectance spectra served to assess model performance. Thus, we tested the suitability of the analytical model WASI for high MSM concentrations and point out necessities for future adaptations to (extremely) turbid environments.