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New tools, such as intensive measurements, together with advanced mathematical models, are increasingly needed in water management and environmental research. The new approaches are being developed at Pyhajarvi, a large (155 km2) lake in southwest Finland. Pyhajarvi is highly valuable in terms of water supply, fisheries and recreational use. The ecological state of Pyhajarvi has been closely monitored for decades, particularly since eutrophication became a major concern in the late 1980s. Two relatively new research methods were used to assess the spatial water quality of Pyhajarvi: (i) transect measurements from a moving boat; and (ii) remote sensing data based estimates. First, a flow-through method from a moving boat was successfully used to collect high resolution transect water quality data from the lake over six field campaigns. The method is relatively accurate but costly, and its use is mostly limited to special campaigns and intensive research, but not for long-term monitoring. Second, remote sensing methods were used to retrieve water quality information which was found consistent with the surface measurements from the boat. The estimation of parameters such as turbidity and humic substance concentration is possible with simple algorithms when using remote sensing (MERIS) data. The quantitative estimation of water quality by the methods used here requires concurrent in situ measurements for algorithm training. These methods will be further developed utilizing frequent on-line water quality and weather data from a recently installed lake float. [ABSTRACT FROM AUTHOR]

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (ZSD) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and ZSD observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6–25 FNU; absorption coefficient of CDOM at 400 nm, 1.0–12.2 m−1; ZSD, 0.5–5.5 m; and chlorophyll a concentration, 2.4–80 μg L−1. The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for ZSD. If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of ZSD. Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km2), which are often not included in routine monitoring programs. [ABSTRACT FROM AUTHOR]

Algal blooms of inland and coastal waters are frequent events that should be broadcast to the general public as a warning. This is rarely done because there are few real-time monitoring systems that allow them to be detected and mapped in real time. The Finnish Environment Centre SYKE aims to build such an operational system in collaboration with Arbonaut Ltd. and Lappeenranta University of Technology. Such a system was developed and implemented in an off-line mode in 2018. It features access to many different kinds of data sources, such as satellite imagery, Chlorophyll-a and turbidity measurements by ships, by automatic water monitoring stations and by manual water sampling. All these data sources and a simple surface advection model are brought together into a data assimilation system that uses different versions of Ensemble Kalman filtering to create a coherent and continuous map of the presence of algal blooms in any inland or coastal waters in and around Finland. The system provides automatic uncertainty quantification for its estimates and allows the relevance of different data sources to be studied by omitting them selectively. The data assimilation environment also provides diagnostics in the form of point-wise time series of data, estimates and their uncertainty. The core algorithms of the environment will be provided as an Open Source library. [ABSTRACT FROM AUTHOR]

Abstract: The concentrations of chlorophyll-a (chl-a), total suspended solids (TSS) and the absorption coefficient of colored dissolved organic matter (a CDOM(400)) are estimated in Case II waters using medium resolution imaging spectrometer (MERIS) satellite (full resolution [FR] level 1b, 300m resolution) and AISA airborne spectrometer data acquired during a spring bloom in the Gulf of Finland, Baltic Sea on April 27, 2004. The accuracy of the estimation is analyzed using empirical band-ratio algorithms together with in situ observations that include water samples analyzed in a laboratory (variation ranges: 22–130μg/l, 2.9–20mg/l, and 1.29–2.61m−1 for chl-a, TSS and a CDOM(400), respectively). Additional in situ estimates (transects) on these characteristics are available through absorption and scattering coefficients measured with an ac-9 absorption and attenuation meter installed in a flow-through system. The retrieval accuracy (R 2) of all three water quality characteristics with MERIS data is close to or above 0.9, while the RMSE is 7.8μg/l (22%), 0.74mg/l (16%) and 0.08m−1 (5%), for chl-a, TSS and a CDOM(400), respectively. The validity of the chl-a algorithm is tested using nine additional data points. The BIAS-error for these points is 5.2μg/l and the RMSE is 10.6μg/l. The effects of changes in the atmospheric characteristics on band-ratio algorithms in cases where no concurrent in situ reference data are available are analyzed using the MODerate spectral resolution atmospheric TRANSmittance algorithm and computer model (MODTRAN). The additional error due to these changes is estimated to be below 20% for the applied ratio algorithms. The water quality data available in the level 2 MERIS-product distributed by the European Space Agency did not include valid results for the date investigated here. [Copyright &y& Elsevier]