Monitoring of water quality of inland waters is important for daily life, for drinking water, transport, recreation, agriculture (including drinking water for cattle and or irrigation) and for ecology. Water samples provide detailed information, but are limited in time and space. Earth Observation (EO) can provide a great spatial overview, which is very useful for example for ecologists and water mangers. The high spatial resolution of Sentinel-2 (S2) and the high overpass frequency of Sentinel-3 (S3) will provide unprecedented monitoring capabilities for inland waters. GLaSS developed examples of Sentinel services, to show a larger public what can be done with this new source of EO data. A core system to ingest and pre-process Sentinel data on the lakes of interest was set up. It was filled with Landsat-8 (L8), and is currently also ingesting S2 data. Also, a large database of in situ reflectance data, match-up satellite data (MERIS and L8) and HYDROLIGHT simulated S2, S3 and L8 data was created. The database included data of lakes with a large range of optical properties (from clear and blue to green and brown and from highly reflecting to highly absorbing), and was used for testing algorithms. It appeared that none of the atmospheric correction algorithms or water quality algorithms was suitable for all lakes, because of the large range of optical variation. To facilitate the pre-selection of atmospheric correction and water quality retrieval algorithms for a lake with unknown optical properties, a pre-classification tool (OWT-GLaSS) was developed. This tool selects the water type of the class which best match the remotely sensed spectrum. Also, tools were developed to easily access and handle the data. The automatic Region of Interest and time series generation tool (ROIStats) allows to aggregate valid lake pixels for time series production and extracts basic statistics for Regions Of Interest (ROIs) provided by the user. The Prediction tool allows the user to select specific pixels (e.g. lake, land, cloud), train a model and let the model select similar pixels from other imagery. During the course of the project, in situ campaigns were carried out in lakes in Finland, Estonia, Sweden, the Netherlands and Italy, further characterising the optics of these lakes, and validating L8 and S2 during their overpasses. Also, an interesting comparison was made between EO-based chlorophyll concentrations and (dissolved) nutrients that were calculated with the HYPE models. MERIS-Chl a was in good agreement with the annual fluctuations in nutrients (DIP) from S-HYPE, both within and between years, for many sub-basins in Lake Vänern. For E-HYPE, there was generally a shift in the phases. Based on a combination of a socio-economic analysis and optical classification, global lakes use cases were selected. The listed lakes were studied in detail with EO and in situ data, using the GLaSS tools and the adjusted algorithms. For the eutrophic lakes, four algorithms were chosen to describe Chl-a content and cyanobacteria presence. Time series showed the effect of e.g. meteorological conditions and differences in chlorophyll distribution over the lakes in different years. For the deep clear lakes, the focus was on long-term time series of EO data. A statistical approach showed that, although in some of the deep clear lakes there is indeed an indication that eutrophication takes place (Lakes Maggiore and Constance), there are also lakes where the overall chlorophyll concentration decreases (Lakes Garda and Tanganyika). The use case on shallow lakes with high suspended matter concentrations showed the added value of high resolution data such as S2. It shows small-scale swirls in Lake Markermeer, it demonstrates a method to indicate which glacial lakes will potentially cause dangerous runoff events (based on the colour of lakes in the Himalaya) and it follows the restoration project in Lake Böyük ?or (Azerbaijan). The highly absorbing lakes case tests a new algorithm, SIOCS, on Nordic lakes. Although the results are very good for the simulated data, the in situ reflectance data still leads to less good retrievals. The other part of the study contains a theoretical analysis on the limits of changes in chlorophyll concentrations that can be detected with EO data based on the sensor noise characteristics. In an additional use case, a method is developed to automatically locate mine tailing ponds, using L8 data. These ponds usually contain highly toxic liquids and their locations are not always well known. Incidents occurring every year show the need for locating and monitoring them globally. In the last use case, the possibility to use EO data for Water Framework Directive (WFD) reporting is demonstrated. Although the WFD is EU-wide, the approach per country with regard to EO data is very different. Examples of histograms and time series and the derived classes were presented to potential users, who were very interested. Altogether, the global lakes use cases demonstrate what can be done with the new Sentinel and other EO data with regard to monitoring, trend analysis and classification such as for the Water Framework Directive. Based on the use cases, training material was developed for students in e.g. ecology, environmental sciences, water management or GIS, to learn how to work with EO data on lakes. This material is made available via several sources, such as ESA LearnEO! and the GEO EO Capacity Building portal.
GLaSS final report
2016
Abstract
Monitoring of water quality of inland waters is important for daily life, for drinking water, transport, recreation, agriculture (including drinking water for cattle and or irrigation) and for ecology. Water samples provide detailed information, but are limited in time and space. Earth Observation (EO) can provide a great spatial overview, which is very useful for example for ecologists and water mangers. The high spatial resolution of Sentinel-2 (S2) and the high overpass frequency of Sentinel-3 (S3) will provide unprecedented monitoring capabilities for inland waters. GLaSS developed examples of Sentinel services, to show a larger public what can be done with this new source of EO data. A core system to ingest and pre-process Sentinel data on the lakes of interest was set up. It was filled with Landsat-8 (L8), and is currently also ingesting S2 data. Also, a large database of in situ reflectance data, match-up satellite data (MERIS and L8) and HYDROLIGHT simulated S2, S3 and L8 data was created. The database included data of lakes with a large range of optical properties (from clear and blue to green and brown and from highly reflecting to highly absorbing), and was used for testing algorithms. It appeared that none of the atmospheric correction algorithms or water quality algorithms was suitable for all lakes, because of the large range of optical variation. To facilitate the pre-selection of atmospheric correction and water quality retrieval algorithms for a lake with unknown optical properties, a pre-classification tool (OWT-GLaSS) was developed. This tool selects the water type of the class which best match the remotely sensed spectrum. Also, tools were developed to easily access and handle the data. The automatic Region of Interest and time series generation tool (ROIStats) allows to aggregate valid lake pixels for time series production and extracts basic statistics for Regions Of Interest (ROIs) provided by the user. The Prediction tool allows the user to select specific pixels (e.g. lake, land, cloud), train a model and let the model select similar pixels from other imagery. During the course of the project, in situ campaigns were carried out in lakes in Finland, Estonia, Sweden, the Netherlands and Italy, further characterising the optics of these lakes, and validating L8 and S2 during their overpasses. Also, an interesting comparison was made between EO-based chlorophyll concentrations and (dissolved) nutrients that were calculated with the HYPE models. MERIS-Chl a was in good agreement with the annual fluctuations in nutrients (DIP) from S-HYPE, both within and between years, for many sub-basins in Lake Vänern. For E-HYPE, there was generally a shift in the phases. Based on a combination of a socio-economic analysis and optical classification, global lakes use cases were selected. The listed lakes were studied in detail with EO and in situ data, using the GLaSS tools and the adjusted algorithms. For the eutrophic lakes, four algorithms were chosen to describe Chl-a content and cyanobacteria presence. Time series showed the effect of e.g. meteorological conditions and differences in chlorophyll distribution over the lakes in different years. For the deep clear lakes, the focus was on long-term time series of EO data. A statistical approach showed that, although in some of the deep clear lakes there is indeed an indication that eutrophication takes place (Lakes Maggiore and Constance), there are also lakes where the overall chlorophyll concentration decreases (Lakes Garda and Tanganyika). The use case on shallow lakes with high suspended matter concentrations showed the added value of high resolution data such as S2. It shows small-scale swirls in Lake Markermeer, it demonstrates a method to indicate which glacial lakes will potentially cause dangerous runoff events (based on the colour of lakes in the Himalaya) and it follows the restoration project in Lake Böyük ?or (Azerbaijan). The highly absorbing lakes case tests a new algorithm, SIOCS, on Nordic lakes. Although the results are very good for the simulated data, the in situ reflectance data still leads to less good retrievals. The other part of the study contains a theoretical analysis on the limits of changes in chlorophyll concentrations that can be detected with EO data based on the sensor noise characteristics. In an additional use case, a method is developed to automatically locate mine tailing ponds, using L8 data. These ponds usually contain highly toxic liquids and their locations are not always well known. Incidents occurring every year show the need for locating and monitoring them globally. In the last use case, the possibility to use EO data for Water Framework Directive (WFD) reporting is demonstrated. Although the WFD is EU-wide, the approach per country with regard to EO data is very different. Examples of histograms and time series and the derived classes were presented to potential users, who were very interested. Altogether, the global lakes use cases demonstrate what can be done with the new Sentinel and other EO data with regard to monitoring, trend analysis and classification such as for the Water Framework Directive. Based on the use cases, training material was developed for students in e.g. ecology, environmental sciences, water management or GIS, to learn how to work with EO data on lakes. This material is made available via several sources, such as ESA LearnEO! and the GEO EO Capacity Building portal.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
