In the era of global challenges and big Earth data computation it’s becoming increasingly important to have proper interoperable solutions for describing, cataloguing, finding, accessing, and distributing highly valuable datasets. The usability and reproducibility of data under FAIR and GEO Data Sharing and Data Management Principles, with accurate description of datasets in terms of semantics and uncertainty, can make data more valuable. EC is pushing Data Spaces as a tool to manage data and generate and provide knowledge ready to use for managers and decision makers. The contribution presents a standard-based Data Space for automatically monitoring Water Quality specifically designed for European Lakes, based on remote sensing derived datasets, in-situ monitoring stations and web services. A web map browser gives access to water quality time series products (turbidity, Chl-a, floods, hydroperiod, etc) based on EO in Cloud Optimized GeoTIFF and in-situ observation stations connected using OGC STAplus standard. The map browser integrates the overall set of capabilities: data and metadata visualization, data analytics, quality indicators linked to the QualityML dictionary; semantic tagging of the Essential Water Variables; and OGC Geospatial User Feedback (GUF). The system is accessible through the OpenID-connect authentication standard which extends the OAuth 2.0 authorization protocol that allows different rights for different users to guarantee the preservation of data. This approach has been developed and tested under the Horizon 2020 WQeMS - Copernicus Assisted Lake Water Quality Emergency Monitoring Service (nº 101004157). Some parts of the solution have been developed under the HORIZON-CL6 AD4GD - An Integrated, FAIR Approach for the Common European Data Space (nº 101061001) co-funded by the European Union, Switzerland and the United Kingdom.

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.

The Copernicus Assisted Lake Water Quality Emergency Monitoring Service platform (WQeMS) is an outcome of the H2020 WQeMS project. It leverages on experimentation and service development relatively to lakes and open surface water reservoirs located in Finland, Germany, Greece, Italy, and Spain. Four service lines have been realized concerning ‘Water Quality Features Changes’, ‘Bloom Events Detection’, ‘Land-Water Transition Zone Change Detection’, and ‘Extreme Events Detection’. Furthermore, a crowdsourcing mobile app allows for collecting timely in-situ information, crucial during crisis' management. WQeMS flexibility and interoperability are advantageous for interfacing with Copernicus Services and the Group of Earth Observation System of Systems (GEOSS) platform. These features support proposing WQeMS as an evolution element of the Copernicus Emergency Management Service (CEMS). In addition, WQeMS modularity makes it possible to test its services with new satellite data and improve the processing chains. Finally, WQeMS platform can serve both, expert users wishing to test alternative methods, and users non familiar with Earth Observation data. Access to some water utilities and water domain engaged parties will be considered towards the end of the project (June 2023) in view of service commercialization. WQeMS sustainability should be attained by providing commercial services, still leaving room for research studies

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).

Phytoplankton blooms threaten aquatic ecosystems worldwide, with implications going beyond their apparent ecological aspects. Management solutions are needed to control the appearance of phytoplankton blooms and alleviate their impacts. Such solutions are supported by scientific results, many of which derive from modeling approaches. Data-driven models are now routinely deployed for the short-term (day to weeks) forecasting of phytoplankton dynamics. Nonetheless, such data-oriented efforts are often plagued by two issues, i.e., the lack of sufficient data and interpretability. On one hand, insufficient data result in overfitting, which produces poorly generalizable models that are unreliable under extrapolating conditions. On the other hand, the lack of interpretability hinders the contribution of such models in decision-making, since acting upon model predictions relies heavily on understanding of the model hypothesis. These two challenges motivated the present work, which aspired to investigate the suitability of multi-spectral satellite imagery as a source of phytoplankton-related data for the development of credible and accountable data-driven models. To this end, first, satellite-derived chlorophyll-a times series were created using Sentinel-2 and Landsat 8 imagery and a physics-based modular inversion and processing system. Then, two machine learning algorithms, i.e. a Random Forest (RF) and a Gaussian Process (GP) regression algorithm, were trained to map hydrometeorological drivers to the satellite-derived chlorophyll-a time series. The two algorithms were benchmarked against each other and against a naïve alternative, i.e., the persistence method, in terms of accuracy, uncertainty, and interpretability in three cases: (a) the mesotrophic Mulargia reservoir in Italy, (b) the hypereutrophic Harsha Lake in the USA, and (c) Lake Hume in Australia, a reservoir facing an increasing number of algal bloom events over the last 10 years. Results indicate that both machine learning models forecasted surface phytoplankton dynamics more accurately compared to their naïve alternative up to ten days ahead in the future. It should be noted though that forecasting accuracy deteriorated with increasing forecasting windows, mostly due to the uncertainty of meteorological forecasts. When the machine learning methods were compared to each other, the RF-based models were marginally better compared to their GP counterparts; they produced slightly more accurate and more certain chlorophyll-a predictions. RF-based models are also preferable in terms of interpretability. Their predictions unveiled specific patterns in hydrometeorological data that could explain phytoplankton dynamics in each case. On the contrary, it remained obscure how chlorophyll-a predictions were made by the GP regression models. More importantly this work offers evidence supporting that multi-spectral satellite data allow for the development of theory-guided, data-driven models for the forecasting of phytoplankton dynamics in lakes and reservoirs.

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).