Various environmental regulations for assessing the status of surface waters have been set worldwide. The requirements for defining the status of water areas, are set for example, by European Union WFD* and MSFD** directives, share common core elements with respect to status assessments:

1) The status assessment is done using discrete classes, typically for water bodies, sub-areas or critical sites representative for certain area of interest, and
2) Chlorophyll a is one of the main variables considered, although Secchi disk depth, turbidity and the level of humic substances may also be utilized as complementary information.

In Finland, the obligations set by EU for WFD reporting concern about 4500 lake and more than 250 coastal water bodies. These waters represent a large range of optically complex waters - mostly absorption dominated humic waters that form one extreme part of Case II waters. The coastal waters of Finland in the northern Baltic Sea are characterized by fragmented coastline and thousands of islands of various sizes. This increases the number of discrete water bodies to be reported. Even larger demand for fulfilling the directive reporting is that Finland has more than 56 000 lakes (larger than 1 ha) with often an irregular shape and multitude of islands. During the previous status assessment (2006 – 2012), most of the lake water bodies were considered to belong in good or high status, most notably the lakes in the Northern Finland. However, especially small lakes in Southern Finland are eutrophic and therefore have been determined to moderate, poor or bad status class. Furthermore, only about 25% of the coastal water bodies have reached the good status, the rest belong to moderate or worse thn that status class. This, in turn, sets actions by the WFD directive and requires frequent monitoring.
In the currently ongoing third WFD status assessment for the years 2011 – 2017, a Finnish national project VESISEN II and an EU Horizon 2020 project EOMORES have exploited the Sentinel satellite series instruments MSI and OLCI and Landsat-8 OLI over these numerous lakes and coastal water bodies. The algorithms to derive water quality parameters for these instruments are based on C2RCC and C2X processors. The processors’ absorption and backscattering outputs have been parameterized to account for the large variability of Finnish lake and coastal water types ranging from oligotrophic to hyper-eutrophic, and humus-poor to humus-rich. Cross-comparisons between the EO products and monitoring site observations were done as time series in reference stations, including approximately 200 lake and 30 coastal stations (available in a web application TARKKA (www.syke.fi/tarkka/en).
A novel user interface called STATUS has been created to distribute the EO-based water quality information for regional authorities responsible for the WFD assessment. The STATUS interface visualizes the statistics and the distributions of each EO instrument and provides them together with the water sampling data collected within the water body (as anticipated with MERIS instrument in Attila et al., 2018). The STATUS interface utilizes a database that is used to store water body based daily statistics of EO water quality interpretations. Currently it holds MERIS, S2A and S2B and Landsat-8 OLI products. The current chl-a dataset covers the years 2003 – 2011 (MERIS), and 2015-2018 (S2A and S2B MSI, OLCI will follow). Turbidity, Secchi disk depth and absorption by CDOM (Coloured Dissolved Organic Matter) is covered by both S2 instruments and Landsat OLI time span; starting from year 2013 with the Landsat-8 OLI and with increasing number of observations after the launches of Sentinel-2A and Sentinel-2B.Through this service the authorities responsible for the assessment have access to water quality products from approximately 40% of Finnish lake and almost all coastal water bodies. STATUS is directly linked to the national WFD assessment system used by the authorities.

We will present a summary of the EO dataset provided to WFD status assessment via STATUS-interface and end user comments collected during the WFD assessment in the early 2019. In addition, reference station validation results will be presented.

* WFD = Water Framework Directive
** MSFD = Marine Strategy Framework Directive.

References
Attila, J., Kauppila, P., Alasalmi, H., Kallio, K., Keto, V., Bruun, E. (2018). Applicability of Earth Observation chlorophyll-a data in assessment of water status via MERIS – with implications for the use of OLCI sensors. Remote Sensing of Environment, 212, 273-287. https://doi.org/10.1016/j.rse.2018.02.043

Lakes and reservoirs are important ecosystems providing such services as drinking water, recreation, support for biodiversity as well as regulation of carbon cycling and climate. There are about 117 million lakes in the world and a high need for regular monitoring of their water quality. European Union Water Framework Directive (WFD) stipulates that member states shall establish a programme for monitoring the ecological status of all waterbodies larger than 50 ha, in order to ensure future quality and quantity of inland waters. But only a fraction of lakes are included in in-situ monitoring networks due to limited resources. In Latvia, there are 2256 lakes larger than 1 ha covering 1.5% of Latvian territory, and approximately 300 lakes are larger than 50 ha, but only 180 are included in Inland water monitoring program, in addition, most of them are monitored once in three to six years. Besides, local municipalities are responsible for the management of lakes, and they are also interested in the assessment of ecological status and regular monitoring of these valuable assets.
Remote sensing seems the only feasible way to monitor lakes over a large region with reasonable frequency and support the WFD status assessment process. Several ecological parameters can be monitored from space - phytoplankton biomass, chlorophyll-a concentration, water transparency and frequency/intensity of algal blooms. Ocean colour sensors (eg., MERIS, MODIS, OLCI) are specially designed for assessment of water quality parameters but have a coarse spatial resolution (250...1000 m/px) thus limiting their use only for large inland water bodies. Recently, ESA has launched a constellation of two Sentinel-2 optical satellites thus providing regular (every 5 days at the equator and 2..3 days in Latvia) and high spatial resolution (10...20 m/px) data stream for free within the Copernicus program. Sentinel-2 data is also more suitable for assessment of water quality parameters in comparison to Landsat-8 data due to additional spectral bands, thus making it more attractive for monitoring of inland water bodies, even small ones (<1 km2).
Development of Sentinel-2 based service for regular water quality monitoring in lakes is the goal of the SentiLake project (2018-2020) currently implemented within the ESA PECS for Latvia program. The pilot territory covers two regions in Latvia and includes more than 100 lakes larger than 50 ha. It is planned to develop an automatic workflow for selecting and processing of available Sentinel-2 data scenes in order to extract water quality parameters (e.g. chlorophyll-a and TSM concentrations) for each target water body. In the case study from 2018, 165 Sentinel-2 MSI Level-1C low cloud cover (<10%) scenes from six tiles were selected (total initial data amount ~110GB) and processed resulting in nearly 57k records (final data amount 9MB) - parameter per water body per date. In the next step, validation of obtained data products will be performed using acquired in situ reference data as well as national lake monitoring database. It will be evaluated if and in what cases pre-classification of water bodies according to watercolour should be performed in order to choose the appropriate processor for assessment of water quality parameters. Initially, Case 2 Extreme (C2X) processor has been selected as the default one.
Our previous study (unpublished) with Latvian lake monitoring database and Landsat-8 data archive has shown promising results in the assessment of lake bloom intensity using seasonal statistics from both spectral and in situ data archives and applying regression analysis (based on Support Vector Machine) - R2 = 0,54 and RMSE = 8,4 ug/l for chlorophyll-a concentration prediction. It is planned to apply the analogous approach to Sentinel-2 data.

This study compares Landsat 8 and Sentinel 2 atmospheric correction methods for water quality monitoring in Minnesota U.S.A.'s >10,000 lakes. We have been using Landsat imagery for water clarity assessments in Minnesota for over 20 years. For these assessments we used empirical methods using in situ Secchi data (SD) to calibrated each image individually without the need for atmospheric correction (AC). Using these methods we have completed nine statewide assessments of water clarity (>10,000 lakes each) since 1975. This was feasible due to a strong collaboration with local and state lake managers and citizen scientists who collect large amounts of SD data that we used to develop water clarity models. These assessments at five year intervals were made available to our collaborators for lake management purposes and the public in a popular (~9,000 unique visitors monthly) LakeBrowser (lakes.rs.umn.edu) that uses a Google map type format. Having water clarity data at five-year intervals was reasonable at the time with the low temporal coverage of Landsat and availability of clear imagery during the late summer index period (July 15 to September 15), which represents the seasonal maximum of chlorophyll levels and minimum water clarity. Recent advances in satellite technology (improved spectral, spatial, radiometric and temporal resolution) along with cloud and supercomputing capabilities have enabled development of automated regional scale measurements of water quality characteristics. The launch of NASA/USGS's Landsat-8, upcoming Landsat-9 and the European Space Agency's Sentinel-2 & 3 have improved the capability to measure chlorophyll, colored dissolved organic matter (CDOM) and suspended sediment (SS), the main determinants of water clarity. With these new capabilities we have the opportunity to improve lake and fisheries management by measuring more water quality variables more often.

For these automated methods to be successful it is important to have a consistent remote sensing reflectance product (Rrs) so that universal water quality models can be developed and applied to similarly corrected imagery. To that end this study evaluated five commonly used atmospheric correction (AC) methods: EROS Surface Reflectance product (SR), EROS Analysis Ready Data SR (ARD), ACOLITE (AC), SeaDAS I2gen, and ESA Case-2 Regional / Coast Color (C2RCC), and two recently introduced AC methods (Modified Atmospheric correction for INland waters (MAIN) and ATmospheric REMoval (ATREM) for Landsat 8 imagery using in situ SD data and five paths (3-5 rows from same date) of clear Landsat 8 imagery. For comparison of the different AC methods we used SD data within one day of the image acquisition date with a total of 631 data points.

The resulting Rrs values varied considerably among the different AC methods and some produced negative Rrs values. It should be noted that these methods are under active development and for some of the methods the Rrs values have changed considerably (e.g., no negative values) since earlier versions of the method. Even with negative values (if rescaled to positive values) all methods were able to produce good to strong (R2 = 0.71 to 0.81) water clarity models that are consistent between different dates of imagery. While some methods are superior to others, all methods could be used for image processing for water quality products if the models were tuned to the specific AC method. Methods that also had the capability for AC correction of Sentinel 2 imagery were used on coincident Landsat 8 OLI and Sentinel 2 MSI Imagery to determine how well the data from the different sensors could be synchronized to Rrs. Changes in Rrs values with different versions of the same AC method, the best performing and synchronizing methods for water quality monitoring and performance of different AC methods with different optical water types will be discussed.

There is substantial evidence that the frequency and magnitude of harmful algal blooms (HABs) in coastal and inland waters around the world have been increasing. Often, however, the lack of consistent lake-wide and long term in situ datasets on HABs impedes our ability to resolve their short and long term variance and develop robust management strategies for mitigating their impact. Earth observation satellites are increasingly being used to deliver quantitative measures of inland water algal blooms. We will present satellite derived indices for algal bloom intensity, spatial extent, severity, and duration, for three shallow lakes in Canada which experience annual, often severe, algal blooms; Lake Winnipeg, Lake Erie, and Lake of the Woods. Chlorophyll retrieval algorithms using wavebands in the red and near-infra-red (NIR) are considered optimal for these turbid, eutrophic waters. Chlorophyll concentrations and subsequent bloom indices are derived using the Maximum Chlorophyll Index, a line-height algorithm targeting the peak in reflectance in the 708nm band available on the European Space Agency (ESA)'s Sentinel-3A OLCI and its predecessor Envisat MERIS. Results will be presented on the derivation of these bloom indices and their validation against in situ observations. These satellite derived algal bloom indices are now used semi-operationally by Environment and Climate Change Canada to provide objective, consistent measures of algal bloom conditions that are vital for large scale assessments of lake health, change detection, and setting targets for lake management actions. Time series of bloom conditions on each lake are analysed with respect to known climate and watershed drivers of algal blooms in order to further understand the seasonal and inter-annual variability in bloom timing and severity. Through a combination of in situ observations of apparent and inherent optical properties and radiative transfer modelling, we also investigate the sensitivity of MCI-derived chlorophyll to both mineral sediments and variations in phytoplankton community composition. Observations demonstrate that the contribution to backscatter in the red-NIR from mineral sediments and gas vacuoles associated with buoyant cyanobacteria such as Microcystis lead to enhanced MCI and potential uncertainties in derived chlorophyll.

In lake rich regions, protecting water quality is critically important because of the ecological and economic importance of recreational activities and tourism. To ensure the health of inland aquatic ecosystems on both a local and regional scale, more comprehensive monitoring techniques to complement conventional field sampling methodologies are needed for effective management. For over 20 years, our previous statewide water quality mapping in Minnesota, USA has primarily relied on Landsat satellites. However, measurements have been limited to water clarity and colored dissolved organic matter (CDOM) due to inherent Landsat sensor spectral band configurations. Sentinel-2 Multispectral Imager (S2/MSI) on the other hand offers several red-edge bands that increase the accuracy of water clarity measurements and have shown the potential to estimate surface water chlorophyll concentrations. The increased temporal coverage of S2/MSI enable more frequent monitoring of Earth’s inland water bodies, and permit routine mapping of water quality parameters.

We demonstrate the implementation of S2/MSI and Landsat-8 Operational Land Imager L8/OLI image processing techniques in an automated pipeline built in a high-performance computing environment that generates statewide Level-3 (L-3) satellite data products for lake water quality monitoring and management. Machine-to-machine access to ESA Copernicus and U.S. Geological Survey servers allows for the synergistic acquisition of L-1 S2/MSI and L8/OLI imagery to supply the demand for near-real time data. Newly acquired imagery is immediately sent through multiple scripted processing modules, which include (1) identifying and omitting potentially contaminated pixels caused by clouds, cloud shadow, atmospheric haze, and specular reflection, and (2) classification of water pixels through a normalized difference water index (nNDWI) to delineate a scene specific water mask. The combined masks result in qualified pixels which advance to (3) a modified SWIR-based aerosol atmospheric correction to the retrieval of remote sensing reflectances (Rrs). The atmospheric correction produces a harmonized reflectance product between S2/MSI and L8/OLI pixels in which modeled L-3 type water quality data products are derived. Calibrated L-3 water quality models including water clarity, CDOM, chlorophyll-a, and suspended solids (SS) rely heavily on field validated datasets to account for the dynamics of optically complex lake systems of the region. To this extent, sampling efforts in the summer months constrain uncertainties between satellite-derived and surface water properties caused by varying atmospheric conditions and calibrate/validate water quality retrieval algorithms to yield verifiable water products. As new field validation data become available at season-end, scripted modules within the processing chain can be modified accordingly and applied to incoming and previously processed imagery if any resulting water quality product models need improvement. Finally, the data are made available to the public in an online map viewer linked to a spatial database that will allow for statistical summaries at different delineations and time windows, temporal analysis and animations of water quality variables.

This unique data source will dramatically improve data-driven resource management decisions and will help inform agencies about evolving water quality conditions statewide. In terms of decision-making, the production of frequent, near-real time data on water clarity, chlorophyll-a, CDOM, and SS lakes across Minnesota will enable water quality and fisheries managers to better understand lake ecosystems. The improved understanding will yield societal benefits by helping managers identify the most effective strategies to protect water quality and improve models for increased fisheries production.

Our planet’s surface waters are a fundamental resource encompassing a broad range of ecosystems that are core to our water, food and energy security in addition to biodiversity and global biogeochemical cycling. Despite this, these same waters are impacted by multiple conflicting demands, the effects of which are compounded further by the pressures and drivers of environmental change. Today, the resulting declines in quantity and quality of water are recognised by the OECD as the pre-eminent risk to society and the global economy.

In recognition of the very limited data available on the status and change in the quality of our inland waters globally, the UK’s NERC-funded six-year GloboLakes (www.globolakes.ac.uk) research programme was developed to investigate the state of lakes and their response to climatic and other environmental drivers of change. This was achieved throughthe realization of a near-real time satellite-based observatory (Sentinel-3) and archive data processing (MERIS) to produce a >10-year time-series of observed ecological parameters and lake temperature for more than 1000 lakes globally.

This presentation will describe the challenges and solutions in the development of a global observation platform, including the diverse and complex optical properties of inland waters and the dynamic algorithm selection procedure required to deliver reliable data. The development and validation of the GloboLakes processing chain (Calimnos) was supported by access to an extensive in situ dataset from more than thirty partners around the world. These data are now held in the LIMNADES community-owned database (www.limnades.org). This approach resulted in a step-change in our ability to produce and validate regional and global water quality products for optically-complex waters. Coupled with measures of environmental change at the catchment scale and lake surface water temperature, the presentation will also provide a summary of the findings to date in understanding the differential sensitivity of lakes to environmental change.

Local and global examples of the data outputs will be briefly explored and the opportunities in how these data can be embedded within local and national monitoring schemes to facilitate better management of water and sanitation will be discussed with a particular focus on the opportunities in delivering on the monitoring and reporting requirements of Sustainable Development Goal 6.