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Colored dissolved organic matter (CDOM) in marine environments impacts primary production due to its absorption effect on the photosynthetically active radiation. In coastal seas, CDOM originates from terrestrial sources predominantly and causes spatial and temporal changing patterns of light absorption which should be considered in marine biogeochemical models. We propose a model approach in which Earth Observation (EO) products are used to define boundary conditions of CDOM concentrations in an ecosystem model of the Baltic Sea. CDOM concentrations in riverine water derived from EO products serve as forcing for the ecosystem model. For this reason, we introduced an explicit CDOM state variable in the model. We show that the light absorption by CDOM in the model can be improved considerably compared to traditional approaches where, e.g., CDOM is estimated from salinity. A prerequisite is high quality CDOM data with sufficiently high spatial resolution which can be provided by the new generation of ESA satellite sensor systems (Sentinel 2 MSI and Sentinel 3 OLCI).

Earth Observation (EO) offers spatially and temporally unique data for generating information required under various environmental regulations for assessing the status of surface waters. These requirements, which are laid down in, for example, European Union directives and the Clean Water Act in the United States, share two 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) phytoplankton chlorophyll a (chl-a) is one of the main variables considered. We analysed the benefits of using chl-a concentrations derived from EO data for the status assessments specified in the EU Water Framework Directive (WFD). Our study focused especially on EO observations' ability to capture extreme and transient events (such as instances of cyanobacteria blooms) more frequently than the monitoring-station data conventionally employs. The accuracy of EO-based chl-a assessment was studied for, in all, 129 Finnish water bodies in the area of the Baltic Sea, in Northern Europe. Natural conditions in this coastal area – particularly its multitude of small bays, numerous estuaries, and mosaic of islands – impose exceptionally strict requirements for an EO instrument's spatial resolution. The analysis revealed that an instrument with a 300 m resolution, such as the MEdium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI), can be used to estimate the water quality in 62% of these water bodies. Processing of MERIS data into chl-a concentrations by means of a FUB inversion model demonstrated good accuracy relative to monitoring stations' measurements for the open-water season in 2003–2011. This extensive dataset showed a 23% difference in modal values between EO- and station-sampling-based chl-a concentrations. The bias in EO chl-a estimates was found to increase with low Secchi disk depth, elevated turbidity, and the presence of intensive phytoplankton blooms. The monitoring-station and EO data showed similar distributions of chl-a observations for a given day and location, a finding that supports the comprehensive use of EO-derived chl-a concentrations in assessment. For determination of a water body's status, the EO data required but also allowed for statistical analysis that differs from what has typically been utilised with sparse measurements from monitoring-station data. The geometric mean or the mode of the EO observations was found to represent the main bulk of the chl-a concentrations well statistically. In contrast, the arithmetic mean of EO observations yields chl-a concentrations that are roughly 1.1–1.6 μg/l higher and hence can lead to over-estimation in the associated status assessment. This paper also presents a new approach applicable for evaluating the validity of EO-based algorithms for any coastal water area requiring assessment. With this quality-grade (QG) method, the EO chl-a estimation accuracy is rated in terms of three grades, with water bodies taken as the evaluation units. For this, the method utilises statistical differences between EO and station-sampling chl-a concentrations and applies background information on optical properties obtained from measurements at routine-monitoring stations. The QG method showed the EO-based chl-a accuracy to suffice for assessing the status of 65% of the coastal water bodies examined. At concentrations representing the threshold for the target of “good status” under the WFD, the EO approach produced 0.6 μg/l higher chl-a values than the stations' sampling did. The MERIS results point to clear benefits of using OLCI-based status assessment throughout the Sentinel-3 era.

Heat fluxes at the lake surface play an integral part in determining the energy budget and thermal structure in lakes, including regulating how lakes respond to climate change. We explore patterns in turbulent heat fluxes, which vary across temporal and spatial scales, using in situ high-frequency monitoring data from 45 glob- ally distributed lakes. Our analysis demonstrates that some of the lakes studied follow a marked seasonal cycle in their turbulent surface fluxes and that turbulent heat loss is highest in larger lakes and those situated at low latitude. The Bowen ratio, which is the ratio of mean sensible to mean latent heat fluxes, is smaller at low lati- tudes and, in turn, the relative contribution of evaporative to total turbulent heat loss increases toward the tro- pics. Latent heat transfer ranged from ~ 60% to > 90% of total turbulent heat loss in the examined lakes. The Bowen ratio ranged from 0.04 to 0.69 and correlated significantly with latitude. The relative contributions to total turbulent heat loss therefore differ among lakes, and these contributions are influenced greatly by lake location. Our findings have implications for understanding the role of lakes in the climate system, effects on the lake water balance, and temperature-dependent processes in lakes. The authors thank two anonymous reviewers who provided a con- structive review of this manuscript. R.I.W. was funded by EU Surface Tem- perature for All Corners of Earth, which received funding from the European Union’s Horizon 2020 Programme for Research and Innovation, under Grant Agreement 640171. We thank individuals who contributed to the collection of data included in this analysis: Jon Cole (Peter Lake), Ken Hinkel and Brittany Potter (Emaiksoun Lake), Peter Staehr (Hampen Soe and Grib Soe), and Hilary Swain (Lake Annie). This work benefited from participation in the Global Lake Ecological Observatory Network (GLEON) and the Networking Lake Observatories in Europe (NETLAKE). J.A.R. acknowledges funding from the Ontario Ministry of the Environ- ment and Climate Change and the Inter-American Institute for Global Change Research (grant CRN3038). J.D.L. was supported by a grant from the National Science Foundation (NSF), Arctic Observing Network (AON; grant number 1107792). Data collection for Lake Võrtsjärv was supported by the Estonian Ministry of Education and Research, grant IUT21-2. The Centre for Ecology and Hydrology, United Kingdom, funded data collec- tion from the Cumbrian lakes. We thank Kevin Rose who provided a help- ful review of an early version of this work. Researchers from various institutions provided data used this study. Please contact R. Iestyn Wool- way (riwoolway@gmail.com) for more information regarding requests for data from the relevant individuals. The authors thank two anonymous reviewers who provided a con- structive review of this manuscript. R.I.W. was funded by EU Surface Tem- perature for All Corners of Earth, which received funding from the European Union’s Horizon 2020 Programme for Research and Innovation, under Grant Agreement 640171. We thank individuals who contributed to the collection of data included in this analysis: Jon Cole (Peter Lake), Ken Hinkel and Brittany Potter (Emaiksoun Lake), Peter Staehr (Hampen Soe and Grib Soe), and Hilary Swain (Lake Annie). This work benefited from participation in the Global Lake Ecological Observatory Network (GLEON) and the Networking Lake Observatories in Europe (NETLAKE). J.A.R. acknowledges funding from the Ontario Ministry of the Environ- ment and Climate Change and the Inter-American Institute for Global Change Research (grant CRN3038). J.D.L. was supported by a grant from the National Science Foundation (NSF), Arctic Observing Network (AON; grant number 1107792). Data collection for Lake Võrtsjärv was supported by the Estonian Ministry of Education and Research, grant IUT21-2. The Centre for Ecology and Hydrology, United Kingdom, funded data collec- tion from the Cumbrian lakes. We thank Kevin Rose who provided a help- ful review of an early version of this work. Researchers from various institutions provided data used this study. Please contact R. Iestyn Wool- way (riwoolway@gmail.com) for more information regarding requests for data from the relevant individuals.

Canopy-forming macroalgae living on rocky bottoms provide valuable ecosystem services but long-term eutrophication has narrowed their distribution and depth zonation in the Baltic Sea. The spatial distribution of macroalgae is shaped by many factors, such as light, salinity, nutrients and wave exposure. In addition, the lack of suitable hard substrates limits the distribution of algae in many areas. Analysing how the spatial distribution of macroalgae is modified by changes in environmental conditions is relevant for focusing management actions. To quantify the resultant distribution under various environmental and management scenarios, both current environmental conditions and substrate limitation need to be considered. We estimated the potential distribution area of bladderwrack Fucus spp. under 11 water transparency scenarios in 9 Finnish sea areas differing in morphology and eutrophication status. The prevailing averaged long-term water transparency conditions were interpreted from satellite images. Ten scenarios were calculated based on hypothetical changes in euphotic depth from −50% to +50% of the present. Species distribution modelling was used to assess the potential distribution areas of Fucus. In addition, to quantify the influence of substrate limitation, we estimated the average substrate limitation with two correction methods: (i) by using field data from underwater videos within the predicted distribution areas and (ii) by using a habitat model representing the distribution of reefs (i.e. rocky bottoms) in the study area. The decrease of euphotic depth by 50% from the present level narrowed the distribution area of Fucus by 24–53% in the Southwestern archipelago, 55–70% in the Gulf of Finland, 37–66% in the Bothnian Sea and 59–100% in Kvarken. An increase in euphotic depth significantly broadened the spatial distribution of Fucus. Decreasing share of suitable hard substrate along depth gradient however hinders broadening of the distribution area. If all areas were suitable for growth, a 50% increase in euphotic depth would expand the distribution area by 124–803%, depending on area. When only suitable substrates were taken into account, this percentage remained at 9–270%. We conclude that substrate limitation needs to be taken into account when estimating macroalgal species distribution in the marine environment. We show how this can be done also when comprehensive bottom substrate maps are not available. Our results are valuable when setting the targets for environmental management plans, and for balancing the local management measures in a cost effective manner. Highlights • Benthic light conditions affect the distribution of canopy-forming macroalgae Fucus. • Also substrate limits the distribution and zonation of Fucus. • Scenario modelling and substrate correction methods were utilised. • Sea areas differ in substrate composition which affects potential distribution area. • Substrate limitation is more pronounced in inner than in outer archipelago.

Monitoring temporal changes in phytoplankton dynamics in high latitude lakes is particularly timely for understanding the impacts of warming on aquatic ecosystems. In this study, we analyzed 33-years of high resolution (30 m) Landsat (LT) data for reconstructing seasonal patterns of chlorophyll a (chl a) concentration in four lakes across Finland, between 60°N and 64°N. Chl a models based on LT spectral bands were calibrated using 17-years (2000–2016) of field measurements collected across the four lakes. These models were then applied for estimating chl a using the entire LT-5 and 7 archives. Approximately 630 images, from 1984 to 2017, were analyzed for each lake. The chl a seasonal patterns were characterized using phenology metrics, and the time-series of LT-based chl a estimates were used for identifying temporal shifts in the seasonal patterns of chl a concentration. Our results showed an increase in the length of phytoplankton growth season in three of the lakes. The highest increase was observed in Lake Köyliönjärvi, where the length of growth season has increased by 28 days from the baseline period of 1984–1994 to 2007–2017. The increase in the length of season was mainly attributed to an earlier start of phytoplankton blooms. We further analyzed surface temperature (Ts) and precipitation data to verify if climatic factors could explain the shifts in the seasonal patterns of chl a. We found no direct relationship between Ts and chl a seasonal patterns. Similarly, the phenological metrics of Ts, in particular length of season, did not show significant temporal trends. On the other hand, we identify potential links between changes in precipitation patterns and the increase in the phytoplankton season length. We verified a significant increase in the rainfall contribution to the total precipitation during the autumn and winter, accompanied by a decline in snowfall volumes. This could indicate an increasing runoff volume during the beginning of spring, contributing to an earlier onset of the phytoplankton blooms, although further assessments are needed to analyze historical streamflow values and nearby land cover data. Likewise, additional studies are needed to better understand why chl a patterns in some lakes seem to be more resilient than in others. peerReviewed

The worldwide economic downturn and the climate change in the beginning of 21st century have stressed the need for cost efficient and systematic operations model for the monitoring and management of surface waters. However, these processes are still all too fragmented and incapable to respond these challenges. For example in Finland, the estimation of the costs and benefits of planned management measures is insufficient. On this account, we present a new operations model to streamline these processes and to ensure the lucid decision making and the coherent implementation which facilitate the participation of public and all the involved stakeholders. The model was demonstrated in the real world management of a lake. The benefits, pitfalls and development needs were identified. After the demonstration, the operations model was put into operation and has been actively used in several other management projects throughout Finland., Graphical abstract, Highlights • Ecological and economic efficiency of river basin management measures need to be evaluated • Operational model need to be more effectively automated and integrated • The web-based map services are useful for the participatory management • Consultancy services for end users ought to be tailored and provided. • More emphasis should be placed on the estimation of the economic benefits

In recent years satellite water quality information have been used for several purposes, such as: 1) regular and specific monitoring, 2) matters that require Near Real Time information (e.g. when cyanobacterial blooms are threatening the swimming water quality) and 3) for the spatial optimisation of monitoring networks. To the aim, image processing chains need to be set up to transform the radiance recorded by satellite sensors into information on water quality, such as the concentrations of water constituents, the physical values on water surface temperature or maps depicting e.g. the spatial extent of aquatic vegetation. Despite these techniques have been developed since about three decades, there are still ongoing developments to produce accurate and consistent products across all water bodies.This is particularly the case for EOMORES, whose target lakes include a wide range of water types. Moreover, as EOMORES makes use of the latest generation of satellite sensors, consolidated image processing techniques need to be adapted according to the resolutions and spectral bands of these newest sensors. This is especially relevant for Sentinel-2 MSI,since for Sentinel-3 OLCI most of the developments are based on its precursor MERIS on Envisat (2002-2012).This document hence illustrates the image processing chain that have been adapted to produce relevant water quality information for the EOMORES study sites. A first section is dedicated to the description of the methods while the last part describe show the processors have been applied to produce maps (both of the past and recent years, including 2018) of water quality. The selected processors are now being used to set up the operational EOMORES service according to WP6.

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative transfer simulations are used for the development and training of the machine learning regression approaches. Statistics comparison with well-established polynomial regression algorithms shows optimistic results for all models and band combinations, highlighting the good performance of the methods, especially the GPR approach, when all bands are used as input. Application to an atmospheric corrected OLCI image using the reflectance derived form the alternative neural network (Case 2 Regional) is also shown. Python scripts and notebooks are provided to interested users.

We evaluated MERIS spectral inversion processors in the estimation of chlorophyll a (Chl-a), total suspended matter (TSM), absorption of colored dissolved organic matter (a CDOM at 443 nm) and Secchi disk transparency (Z SD ) in four lakes located in southern Finland. The lakes represent oligotrophic, mesotrophic and humic (high a CDOM , low TSM) lake types. Our validation data was extensive, consisting of remote sensing reflectance (R rs ) measured in the field, detailed measurements of (specific) inherent optical properties ((S)IOP) and water quality measurements. Validation measurements were collected at stations and as spatially intensive transects. The tested processors were Boreal lakes (BOR) and Free University of Berlin WeW WATER processor (FUB). In the case of BOR we included two versions, the original one available in 2008 and the most recent version. The processing chains of these versions differed in terms of MERIS archive reprocessing (2nd reprocessing (RP2) and 3rd reprocessing (RP3) were used), version of adjacency effect correction (ICOL) and rectification method. However, all these had only minor effect on the estimated R rs when compared to the effect caused by the differences in the atmospheric correction versions. The use of ICOL improved the estimation of R rs and therefore all IOP and water quality validations were made with ICOL pre-processed data. R rs was best estimated with RP2 BOR, although it somewhat overestimated R rs in blue in the humic lake. RP3 BOR greatly overestimated the R rs in blue. FUB was outside its a CDOM training range in most of the studied lakes. RP2 BOR estimated water quality with the best accuracy in mesotrophic lakes, but failed in humic and clear water lakes. BOR overestimated Chl-a, particularly with RP3. The best TSM correlation was for RP2 BOR. All processors underestimated a CDOM . The SIOP measurements showed that overestimation of Chl-a was not due to incorrect conversion factors, and that underestimation of TSM was to some extent explained by lower average conversion factor than that measured in the validation lakes. The inaccuracies in Chl-a and a CDOM (443) estimation were related to the partition of a tot to phytoplankton and CDOM absorption, which emphasizes the importance of including IOP measurements in the validation studies. Z SD was best estimated on the basis of a tot and b tot , which were separately calculated from BOR output and the bio-optical model assumed in the processor. For the needs of transparency estimation the processors should produce a tot and b tot for all MERIS bands as standard output. Atmospheric correction over humic lakes is another problem to be solved in future versions of the processors. The 2008 version of BOR provided better atmospheric correction results in humic lakes and is thus more applicable in the boreal zone where these lakes are common. This suggests that before a new processor version is published it should be validated with existing in situ data.

To analyze the applicability of direct insertion of total suspended matter (TSM) concentration field based on turbidity derived from satellite data to numerical simulation, dispersion studies of suspended matter in Lake Säkylän Pyhäjärvi (lake area 154 km2; mean depth 5.4 m) were conducted using the 3D COHERENS simulation model. To evaluate the practicality of direct insertion, five cases with different initialization frequencies were conducted: (1) every time, when satellite data were available; (2) every 10 days; (3) 20 days; (4) 30 days; and (5) control run without repeated initialization. To determine the effectiveness of initialization frequency, three methods of comparison were used: simple spatial differences of TSM concentration without biomass in the lake surface layer; averaged spatial differences between initialization data and the forecasts; and time series of TSM concentration and observation data at 1 m depth at the deepest point of the lake. Results showed that direct insertion improves the forecast significantly, even if it is applied less often.

We studied the absorption properties of in-water constituents among 15 boreal lakes belonging to various lake types. One of the lakes was also monitored frequently during its growth season to reveal the seasonal variation of absorption properties. Within the studied lakes, CDOM absorption aCDOM(442) varied from 0.4 to 15 m− 1. CDOM was clearly dominating the absorption signal at low wavelengths, but even at the red-end of the spectra its contribution to total absorption was notable, varying from 48 to 99% of total absorption (excl. water) at 442 nm and from 5 to 86% at 665 nm. Slope of the CDOM absorption spectra SCDOM(400–700) varied from 12.9 to 19.3 μm− 1. Higher slopes were obtained when the wavelength range was extended to the UV-range (350–700 and 300–700 nm). Non-linear relationships were noted between aCDOM and SCDOM calculated over various wavelength ranges. Despite the significant relationship between DOC concentration and aCDOM, almost five-fold variation was noted in DOC-specific CDOM absorption at 442 nm. This variation was clearly associated with concurrent changes in SCDOM. Non-algal particulate absorption varied from 0.037 to 1.8 m− 1 at 442 nm, contributing

Recent studies indicate that inland waters play a very important role in the global carbon cycle. Inland water bodies are the main source of drinking water in many parts of the world and important resource for aquaculture and tourism. Neither determining the true role of lakes in the global carbon cycle nor monitoring lake water quality in real time are possible without using remote sensing. The optically active part of carbon that can be detected by remote sensing is colored dissolved organic matter (CDOM). This chapter discusses the importance of carbon in inland waters, its optical properties, and the performance of different empirical and model based approaches in retrieval of the amount of CDOM.

Three MERIS Case II water processors included in the BEAM software package were studied for estimating the water quality in the coastal waters of the northern Baltic Sea. The processors, named Case II Regional (C2R), boreal (BOR), and eutrophic (EUT), for the associated lake types, have been developed for different types of coastal or inland (Case II) waters. Chlorophyll-a (chl-a), total suspended matter (TSM), absorption of colored dissolved organic matter (aCDOM(443)), and signal depth (Z90) products of the BEAM processors were compared with in situ data. In addition, total absorption (aTOT) and scattering of TSM (bTSM) from different BEAM processors were compared against the results of coastal field campaign measurements. The in situ water quality data consisted of monitoring station data gathered by the Finnish environmental administration during 2006–2009 and data from coastal field campaigns with a flow-through system. AERONET-OC (SeaPRISM) data from the Helsinki Lighthouse station were used to validate the BEAM reflectance products. The comparison with the BEAM processor results and in situ data showed that the bias of the original BEAM algorithms can be decreased through adjustment of the coefficients that relate IOPs such as the absorption of pigments and the scattering of TSM to water quality constituents such as chl-a and turbidity. The TSM products of the BEAM processors can be used to estimate the turbidity measured at monitoring stations with an r² of 0.76–0.84 and an RMSE of 0.7–0.85 FNUs (Formazin Nephelometric Units) on the coast of Finland. The best functionality for turbidity estimation was observed with the EUT processor, but the C2R processor also gave a sound performance. The BOR and C2R processors proved to be the best for deriving chl-a concentration. However, the accuracy of chl-a estimations was low with both processors (r² ranged from 0.45 to 0.47 and RMSE was between 44 and 45%). Chl-a products, particularly during the phytoplankton bloom seasons of spring and summer, require further development. The Z90 product from the BOR processor was used to derive an algorithm for Secchi disk depth estimation with r2 0.48 and RMSE 0.97 m. The BOR processor was the most successful at CDOM estimation (r² 0.6 and RMSE 0.49 1/m), but a simple reflectance ratio was actually able to perform better (r² 0.75 and RMSE 0.39 1/m). In many cases, the differences between the outcomes of processors were small and related only to a part of the in situ dataset.

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 Pyhäjärvi, a large (155 km2) lake in southwest Finland. Pyhäjärvi is highly valuable in terms of water supply, fisheries and recreational use. The ecological state of Pyhäjärvi 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 Pyhäjärvi: (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.

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (Z(SD)) 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 Z(SD) 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); Z(SD), 0.5-5.5 m; and chlorophyll a concentration, 2.4-80 microg 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 Z(SD). 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 Z(SD). 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 km(2)), which are often not included in routine monitoring programs.

The effects of an atmospheric correction method for water quality estimation have been studied and validated for Airborne Imaging Spectrometer for Applications (AISA) data. This novel approach uses atmospheric input parameters from a numerical weather prediction model: HIRLAM (High Resolution Limited Area Model). The atmospheric correction method developed by de Haan and Kokke (1996) corrects the spectrometer data according to the coefficients calculated using Moderate Resolution Transmittance Code (MODTRAN) radiative transfer code simulations. The airborne campaigns were carried out at lake and coastal Case 2 type water areas between 1996 and 1998. The water quality interpretation was made using the MERIS satellite instrument wavelengths. The correction improved most of the water quality (turbidity, total suspended solids, and Secchi disk depth) estimates when data from several flight campaigns were used jointly. The atmospheric correction reduced the standard deviation of the measurements conducted on different days. The highest improvement was obtained in the estimation of turbidity.

In this paper we consider the estimation of lake water quality constituent distributions from hyperspectral remote sensing data. We present a computational approach that can be used to assimilate information from mathematical evolution models into data processing. The method is based on a reduced order iterated extended Kalman filter, and a convection–diffusion model is used to describe the movement of the water quality constituents. The performance of the technique is evaluated in a simulation study. The results show that the filter approach with an appropriate evolution model yields estimates that have better spatial and temporal resolutions than those obtained with conventional methods. Furthermore, the use of a feasible evolution model may make it possible to obtain information also on the concentrations in the lower parts of the lake.

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, 300 m 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: 22–130 μg/l, 2.9–20 mg/l, and 1.29–2.61 m −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 7.8 μg/l (22%), 0.74 mg/l (16%) and 0.08 m −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 5.2 μg/l and the RMSE is 10.6 μg/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.

Citizen observations, environmental data gathered by volunteers without professional observation capabilities, have been extensively used for Finnish water quality monitoring tasks. Recently, mobile smartphones and their digital cameras have enabled more direct measurements of transparency related water quality variables with inexpensive technology suitable for volunteers. These "Secchi3000" ideas of measurement technology by viewing known targets through multiple viewing path lengths within measured water were used to develop an iQwtr measurement device for water transparency related citizen observations. Past experiences with crowdsourcing and use of in situ water transparency data with satellite observations are reviewed and future challenges outlined.

The aim of this study was to estimate the distributions of spectral diffuse attenuation coefficient, attenuation depth and subsurface reflectance of Finnish lakes. In addition, the optimum empirical water quality interpretation algorithms employing reflectance ratios were investigated for the needs of remote sensing. Estimations of the optical properties were based on simple optical models and measured concentrations of optically active substances (the sum of chlorophyll a and phaeophytin a, total suspended solids and coloured dissolved organic matter (CDOM)) at 1670 monitoring stations representing 1113 lakes. The models were parameterized using optical data from 10 lakes. The mean diffuse attenuation coefficient in PAR was 3.5 m−1 and the location of the maximum attenuation depth was in the range 564–714 nm. The simulated reflectance spectra showed a shift of the maximum value to longer wavelengths as trophic status changed from oligotrophic to hyper-eutrophic. High CDOM concentrations decrease the estimation accuracy of chlorophyll a from reflectance spectra using empirical algorithms, particularly in oligotrophic lakes. The models described can be used in studying light availability for photosynthesis at different depths, in the simulation of water temperatures, in estimating how different management alternatives affect light attenuation and Secchi depth, and in various remote sensing applications.

Given the importance of coloured dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method to estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Advanced Land Imager (ALI) images were acquired in southern Finland (in 2002) and southern Sweden (in 2003) together with in situ measurements of bio-optical properties of 34 lakes (39 measuring stations). Based on this dataset, a band-ratio type algorithm was developed using ALI band 2 and band 3 for estimating CDOM content (absorption of filtrated water at 420 nm) in lakes. Correlation between in situ measured CDOM and the remote sensing estimate of CDOM was high, r2=0.73. The CDOM retrieval algorithm obtained on the basis of two images and in situ data was validated on a third ALI image (eastern Finland, 2002) that was available in the ALI image archive. In situ water-colour monitoring data from 22 lakes (27 measuring stations) in the third image were available in a database of the Finnish Environment Administration. The water-colour data were converted to CDOM absorption values, which were then compared to the results from a third ALI image. The correlation between remotely estimated and in situ CDOM values in the algorithm validation image was high, r2=0.83. These results support the conclusion that CDOM content in lakes over a wide range of concentrations (aCDOM(420) between 0.68 and 11.13 m−1) can be mapped using Advanced Land Imager data.

Global algorithms for chlorophyll‐a (chl‐a) concentration and primary productivity retrieval from satellite observations are often not applicable to areas that are turbid and rich in coloured dissolved organic matter (CDOM), such as the Baltic Sea. Such a turbid (Case 2) water area, with its unique optical properties, often requires a parametrization to local conditions. Water quality algorithms are also sensitive to other naturally occurring phenomena, such as temporal variations in the atmospheric properties. Therefore, adequate reference data are essential for the estimation of water quality algorithm accuracy. In this study, a comparison with Sea‐viewing Wide Field of view Sensor (SeaWiFS) data and high‐frequency flow‐through fluorometer measurements is made. These so‐called ‘ship of opportunity’ data are measured with an automated continuous sampling system onboard several merchant and passenger ships in the Baltic Sea (SOOP, the Alg@line‐system). The study is concentrated on the monitoring of spring...

The traditional method used in the water quality classification of Finnish lakes includes the collection of water samples from lakes and their analysis in laboratory conditions. The classification is based on statistical analysis of water quality parameter values and on expert opinion. It is possible to acquire similar information by using radiance values measured with the Earth Observing System Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). In this letter, the classification accuracy with MODIS data is about 80%. Only about 0.2% of the 20 391 pixels were misclassified by two or more classes, as a four-class classification system is used.

The main objective of the present study is to test various methods for describing the absorption spectra of coloured dissolved organic matter (CDOM) and to determine the numerical values of some optical parameters of CDOM in lakes with diverse water quality. First, the parameters of an exponential model in different spectral intervals were determined. In addition, the suitability of some other models for the approximation of CDOM spectra was estimated. Specific absorption coefficients of CDOM were calculated from the absorption coefficients and dissolved organic carbon (DOC) concentrations. The experimental initial data were differences between spectral attenuation coefficients of filtered and distilled water. Two datasets were used: 1) for 13 Estonian and 7 Finnish lakes (altogether 404 spectra between 350 and 700 nm) measured by the Estonian Marine Institute (EMI); 2) for 10 Finnish lakes (73 spectra) measured by the Finnish Environment Institute (FEI). The spectra of CDOM absorption coefficients (aCDOM) were calculated from experimental data taking into account the correction due to scattering properties of colloids in the filtered water. The total content of CDOM in natural waters of Estonian and Finnish lakes was expressed by means of aCDOM at the wavelength of 380 nm. It varied significantly, from 0.71 to 19.5 m−1, the mean value (of all the investigated lakes) being around 6.6 m−1. Slopes of the exponential approximation varied widely, from 0.006 to 0.03 nm−1. Averaged over all lakes values of slope for the interval 380-500 nm obtained from the EMI dataset are close to those obtained from the FEI dataset: from 0.014 nm−1 (without correction) to 0.016-0.017 nm-1 (with different types of correction). These results are in good correspondence with most published data. Attempts to describe the spectra in the region of 350-700 nm by means of hyperexponential functions (∽ exp(-αλη)) show that: (1) η < 1 (in the case of traditional exponential approximation η = 1); (2) a promising idea is to seek the best fit only for wavelengths λ > λ1, where λ1 will be chosen taking into account the real shape of aCDOM spectra. The mean value of the specific absorption coefficient (a*CDOM) at the wavelength 380 nm obtained in this study (0.44 L mg−1 m−1) is close to the values published in the literature, if we assume that a*CDOM (380) is calculated using the data of dissolved organic matter (DOM). The optically non-active fraction of DOM in our study was high and therefore a*CDOM (380) was considerably higher (1.01 L mg−1 m−1) than a*CDOM (380). The results of the present work could be used in the modeling of underwater light field as well as in the interpretation of radiation measurements and optical remote sensing results.

The spatial distribution of the sum of chlorophyll a and phaeophytin a concentrations (chl-a) under light wind (0–2 m s−1) conditions was studied in two lakes with an AISA airborne imaging spectrometer. Chl-a was interpreted from AISA radiance data using an algorithm based on the near-infrared (700–710 nm) to red (660–665 nm) ratio. The results of Lake Lohjanjarvi demonstrate that the use of one monitoring station can result in over- or underestimation by 29–34% of the overall chl-a compared with an AISA-based estimation. In Lake Hiidenvesi, the AISA-based estimation for the mean chl-a with 95% confidence limits was 25.19±2.18 µg l−1. The use of AISA data together with chl-a measured at 15 in situ sampling stations decreased the relative standard error of the mean chl-a estimation from 20.2% to 4.0% compared with the use of 15 discrete samples only. The relative standard error of the mean chl-a using concentrations at the three routine monitoring stations was 15.9 µg l−1 (63.1%). The minimum and maximum c...

A semi-operative approach to retrieve chlorophyll-a concentration from airborne/spaceborne spectrometer observations has been developed and tested using the airborne imaging spectrometer (AISA) data from 11 lakes located in southern Finland. The retrieval approach is empirical and requires nearly simultaneous in situ training data on water quality for the determination of regression coefficients. However, the training data does not have to be collected from every lake under investigation. Instead, the results obtained indicate that reliable estimates on the level of chlorophyll-a (chl-a) for an individual lake can be achieved without employing in situ data representing this specific lake. This enables the estimation of water quality from remotely sensed data for numerous lakes with the aid of reference data only for a few selected lakes representing the region under investigation. In addition, it is shown that the remotely sensed spectrum shape characteristics are highly affected by the trophic and humic state of the lake water.

We examined the effects of catchment characteristics and riverine processes on the concentrations of nutrients and total suspended solids (TSS) using data from the outlets of 12 tributaries (9–139 km2) and 22 main channel sites of a river draining an intensely cropped area of 1088 km2 in SW Finland. For the tributaries, the flow-weighted mean concentrations of TSS and total phosphorus (TP), which both reflect erosion, were best explained by field percentage and the mean slope of the fields of the catchment. The best model describing the concentration of dissolved reactive P (DRP) included field percentage and catchment area. Total nitrogen (TN) and nitrate+nitrite-nitrogen (NOx-N) correlated only with field percentage. Except for DRP, the regression models derived from the tributary data rather accurately predicted the concentrations in the main river channel. Our results suggest that, with regard to lakeless catchments of 10–1000 km2, the export of TP, TSS and N per unit area remains constant. Furthermore, although some P in eroded soil particles may be released into dissolved, algal-available form during the river transport, erosion control appears not to efficiently reduce dissolved P.

Agricultural phosphorus (P) loading is a major contributor to eutrophication of surface waters in Finland. Of the various forms of P in runoff from cultivated fields, dissolved reactive phosphorus (DRP) is immediately available for algal growth and can directly accelerate eutrophication. The applicability of an empirical model developed in southeastern USA was evaluated by simulating DRP in surface runoff from two cropped and grassed clayey soils (Vertic Cambisols) in southwestern Finland. The model relates DRP in a runoff event, e.g. to desorbable soil P (PD), runoff volume (V) and the concentration of total suspended solids (TSS) in runoff. The model overestimated the mean concentration of DRP by a factor of 110–1645. In addition, the observed and simulated mean concentrations of DRP in plots with different winter covers, crops and P status did not correlate with each other. The predictions improved when PD was estimated by water extractions instead of Bray extractions, and the model was simplified by excluding the dependence of DRP concentration on V. The correct level of results could, however, only be achieved by calibration. In order to improve the model fit, the dependence of DRP concentration on V, runoff duration and TSS should be assessed under Finnish conditions and the model should be modified accordingly.

A versatile PC-based lung sound analyzer has been developed for short-term recording and analysis of respiratory sounds in research and clinical applications. The system consists of two sound sensors, a flow sensor, a filtering signal amplifier and a PC with a data acquisition card and software for measurement and analysis of the sounds. The analyses include phonopneumography, time expanded waveform analysis, spectral analysis with time averaged Fast Fourier Transform, frequency analysis in time domain (sonogram), and automatic detection and waveform analysis of crackles. Short-term repeatability of spectral parameters of tracheal and lung sounds was studied in 10 healthy subjects. The coefficients of variation (CoV) of the averaged quartile frequencies (F_{25}, F_{50} and F_{75}) of lung sounds during flow-controlled tidal breathing were 3.7, 4.0 and 8.9% in expiration and 2.7, 3.5 and 4.5% in inspiration, respectively. CoVs of the averaged F_{25}, F_{50} and F_{75} of expiratory tracheal sounds were 6.9, 3.0 and 2.4%, and those of inspiratory tracheal sounds 6.3, 2.6 and 3.3%, respectively. Examples of lung sound analysis of samples containing adventitious sounds such as crackles and wheezes are presented. The results indicate that the median frequency has the best repeatability of quartile frequencies of breath sounds and they suggest that the variations of those parameters are low enough for diagnostic purposes. The results also suggest that the analyzer can be a useful new tool for pulmonary research in the fields of physiological and clinical short-term studies of respiratory sounds.

BackgroundAlgal mass occurrences are one of the most distinguishing effects of eutrophication in lakes and the coastal waters of the Baltic Sea. Algal bloom occurrence in water bodies varies greatly in terms of both space and time, even during short periods, which makes reliable monitoring of blooms difficult. In this paper, we explore the possibilities to extend the sensor network both spatially and temporally by applying participatory sensing to surface algal bloom monitoring in Finnish lakes and the coastal areas of the Baltic Sea.ResultsTwo participatory sensing systems were used to collect visual algae observations by citizens: the mobile phone application Levävahti (Algae Watch) and the collaborative web service Järviwiki (Lake wiki), during the summers of 2011–2013. Citizen observations were compared with the visual observations performed by trained expert observers, and mean correlations between citizen and expert observations were calculated using the bootstrapping method: 0.72, 95% confidence interval (CI) [0.53 0.86]; 0.65, 95% CI [0.35 0.86]; and 0.56, 95% CI [0.29 0.76] for the years 2011, 2012 and 2013.ConclusionsSurface algal bloom monitoring is needed to obtain data on algal bloom frequency and intensity, in particular in lakes where the use of satellite remote sensing has limitations and/or phytoplankton monitoring is infrequent or totally lacking. The correlations between expert and citizen observations suggest that citizen observers can provide additional information to support algal bloom monitoring of inland and coastal waters.

Study objective To obtain a basis for assessment of changes in breath sound spectra in patients with pulmonary diseases, short-term and day-to-day repeatability of spectral parameters was studied. Design Breath sounds were recorded simultaneously from the trachea and from the chest twice at an interval of 15 min (short-term repeatability) and of 1 to 3 days (day-to-day repeatability). During recordings, air flow at the mouth was controlled, the target inspiratory and expiratory peak flow being 1.25 L/s. Inspiratory and expiratory breath sound spectra were averaged over 7 to 10 successive respiratory cycles. The repeatability of sound intensity (RMS), frequency of maximum intensity (Fmax), and median frequency (F50) was analyzed with analysis of variance. Participants Short-term repeatability was studied in 10 healthy nonsmoking men (age 25 to 44 years), and day-to-day repeatability was studied in 10 healthy nonsmoking men (age 23 to 41 years) and in 12 patients with clinically stable fibrosing alveolitis (age 35 to 82 years). Results Short-term coefficient of variation (CoV) of Fmax and F50 was 2.6 to 6.7% when recorded from the chest, and 6.2 to 8.7% when recorded from the trachea. Day-to-day CoV of Fmax and F50 in healthy subjects was 4.7 to 8.5% and 5.0 to 8.7% recorded from the chest or from the trachea, respectively. Inspiratory day-to-day variation in those parameters was higher in patients with fibrosing alveolitis. CoV of RMS was high, ranging from 18 to 47% in different subject groups and sampling situations. Conclusions Repeatability of F50 of averaged flow-controlled lung sound spectra is good both in healthy subjects and in patients with fibrosing alveolitis. Thus, F50 of respiratory sound spectra may be useful in monitoring of changes induced by respiratory diseases and interventions. These results emphasize the importance of standardization of recording conditions and of analyzing techniques.

Summary. The performance of the self-organizing map (SOM), an artificial neural network, was evaluated in the classification of lung sounds. Patients with asthma (n = 8), emphysema (n = 8) and fibrosing alveolitis (n = 8), and patients with healthy lungs (n = 8) were selected for the study. Fast Fourier transform (FFT) spectra from midinspiratory breath sounds recorded at the right lower lobe area were used to construct feature vectors in the learning and classification process of SOM. The sound segments did not contain wheezing sounds. The lung sounds of 25/32 (78%) patients were classified correctly, with an overall kappa (K) value of 0.71. The agreement between the clinical and proposed diagnoses based on classification of lung sounds was good among patients with emphysema (K = 0.92) and those with healthy lungs (K = 0.83), but only moderate among patients with asthma (K = 0.52) and fibrosing alveolitis (K = 0.54). This is due to the limitations in distinguishing breath sounds of asthmatics without wheezing sounds from those with crackles in fibrosing alveolitis by the spectral pattern alone. The results indicate that SOM based on FFT spectra is potentially useful in the classification of lung sounds, e.g. in health screening or in differential diagnosis of pulmonary disorders. To enhance the performance of SOM, other features of lung sounds should be combined with FFT spectra.

A series of laboratory experiments was carried out to quantify physical and chemical factors that control the level of soluble P in soil suspensions. The objective was to identify the variables needed in models simulating the load of dissolved reactive P (DRP) in the surface runoff from cultivated land. Release of soil P as a function of water-to-soil ratio and ionic strength was investigated with soil samples collected from two experimental fields. The dynamic relationship between soil and solution P was studied by sorption-desorption isotherms. The results of the laboratory tests were compared with the mean flow-weighted concentration of DRP in the runoff water from the fields. The DRP concentration was too high to have originated solely from the eroded soil material transported in the runoff. It was concluded that DRP arose mainly by the desorption of P from the surface soil during a rain or snow-melt period. The mean DRP concentration seems to be primarily controlled by the P status of the surface soil. The DRP concentration of soil extracts obtained at wate-to-soil ratios of 250–1001 kg −1 (soil concentration range of 4–10 g l −1 ) corresponded to the flow-weighted mean DRP concentrations of surface runoff water and may thus be used to quantify the removal of DRP by surface runoff. The variations in the ionic strength and water-to-soil ratio in the surface soil may control the temporal variation of the DRP in the runoff water. In the present soils, a series of water extractions at different water-to-soil ratios was found to be a more reliable procedure in assessing the potential P load than the parameters derived from sorption-desorption isotherms.

The aim of the study was to develop a method for assessing phosphorus transport from a large drainage basin. A tentative conceptual model of watershed dynamics was developed consisting of two components: a runoff model and a phosphorus transport model. A similar approach has also been used in previous studies (e.g. Kallio 1992). As the runoff model, a modification of the HBV-model (Vehviläinen 1992) was used. The statistical P transport model was developed in this study. The models were tested against the data of eight subcatchments of the watercourse of Lake Längelmävesi, central Finland. Runoff calibration was made against total discharges, though in the output data the runoff from forested and agricultural areas were presented separately. A good fit was obtained, the correlation coefficient squared R2 against water level being 0.964. The calibration against total phosphorus transport was made using simulated discharge values. In the present version the correlation coefficient squared R2 was 0.901.

The paper deals with the development of a general optimization modeling system with which arbitrary models can be generated. This consists of 1. Organizing the model data base in a way best suitable for modeling purposes (relational data base structure) 2. developing a model definition language, with which model construction can be easily expressed and built (based on predicate calculus).

[1] Satellite observations and surface-observed transect data are combined to assess the spatial characteristics of water quality in the Baltic Sea, northern Europe. The technique that optimally assimilates the two data sources is based on Bayes' theorem. The method considers the spatial accuracy characteristics of both transect and satellite data. The mathematical formulation of the method is generally applicable. Hence it can be applied to other environmental monitoring problems as well. This investigation demonstrates its feasibility for water quality mapping. The characteristic under investigation is chlorophyll (chl a) concentration, which is currently operationally monitored in the Baltic Sea at shipborne Alg@line transects, using flow-through fluorometer sampling systems. In order to optimally extrapolate transect observations the developed method benefits from the full spatial coverage of spaceborne optical spectroradiometer data. The results show that the estimation accuracy of regional chl a concentration during the spring bloom period substantially improves as satellite observations are assimilated to transect data. The assimilation results show significantly higher accuracies than those obtained using either extrapolated transect data or stand-alone retrieval algorithms for satellite data.

The state of surface waters is affected by the quantity and quality of various suspended and dissolved substances. Loading from sources such as agriculture, fish farming, industry and municipalities can cause eutrophication and have other adverse effects on water quality. In Finland the quality of lake water is traditionally determined by collecting water samples and analyzing the samples in a laboratory and by making on-site measurements (e.g. Secchi depth, temperature). However, sampling is slow and expensive and does not include all lakes. Remote sensing instruments can cover large areas quickly and repeatedly and they are also relatively cheap to use once the retrieval methods have been developed into operational level. However, the revisit time for Landsat is 16 days so it is not suitable for operational monitoring especially in areas where cloud cover is frequent. Another interesting instrument is SeaWiFS. It produces daily data but only with 1000-m pixels. One of the latest spaceborne remote sensing instruments, the MODIS spectrometer, produces daily data with two 250-m channels. The objective of this paper is to assess the capabilities of MODIS for monitoring turbidity in Finnish lakes.

Remote sensing methods for water quality assessment are discussed. Secchi depth, turbidity and chlorophyll-a concentration are important variables. Remote sensing offers some advantages such as good spatial and temporal coverage and the possibility of measuring many lakes simultaneously. With remote sensing some variables of lake water quality could potentially be assessed up to several times per year. Remote sensing instruments can also measure lakes that are not currently monitored and possibly reduce the amount of water sampling measurements needed for the assessment. In order to study the usability of remote sensing for water quality measurements, the Laboratory of Space Technology of the Helsinki University of Technology together with the Finnish Environment Institute (FEI) conducted four airborne remote sensing and ground truth measurement campaigns in Southern Finland during 1996-1998. The campaigns were part of the EU-funded Salmon project and national water quality remote sensing projects. This paper investigates the feasibility of remote sensing data for operational lake water classification. While numerous papers describe remote sensing methods used for water quality estimation, the classification of lake water quality has not been previously investigated using as many variables and as large data set as the present study.

Chlorophyll-a (chl-a) concentration of lake water can be measured with airborne (or spaceborne) optical remote sensing instruments. The rmse obtained here with empirical algorithms and 122 measurement points was 8.9 microg/l (all points used for training and testing). Airborne Imaging Spectrometer for Applications (AISA) was used in four lake water quality measurement campaigns (8 measurement days) in southern Finland during 1996-1998 with other airborne instruments and extensive in situ data collection. As empirical algorithms are employed for chl-a retrieval from remote sensing data, temporally varying factors such as surface reflection and atmospheric effects degrade the estimation accuracy. This paper analyzes the quantitative accuracy of empirical chl-a retrieval algorithms available as methods to correct temporal disturbances are either included or excluded. The aim is to evaluate the usability of empirical chl-a retrieval algorithms in cases when no concurrent reference in situ data are available. Four methods to reduce the effects of temporal variations are investigated. The methods are: (1) atmospheric correction; (2) synchronous radiometer data; (3) wind speed data; and (4) bidirectional scattering model based on wind speed and sun angle data. The effects of different correction methods are analyzed by using single-date test data and multi-date training data sets. The results show that the use of a bidirectional scattering model and atmospheric correction reduces the bias component of the measurement error. Radiometer data also appear to improve the accuracy. However, if concurrent in situ reference data are not available, the retrieval algorithms and correction methods should be improved for reducing the bias error.

The aim of the study was to test the feasibility of the band combination of the TERRA MODIS and ENVISAT MERIS instruments for operational monitoring of lakes and coastal waters in Finland. Also simulated LANDSAT TM data were tested. Satellite bands were simulated using airborne measurements with AISA imaging spectrometer. Semi-empirical algorithms with simulated satellite data were tested against field observations using regression analysis. Interpretation of chlorophyll a, suspended matter, turbidity and secchi-disk depth was included in the analyses. The data for this study were gathered in campaigns carried out in May and August 1997 and August 1998 both for lakes in southern Finland and coastal waters of the Baltic Sea. The data set included 85 in situ observations for lakes and 107 for coastal waters. Our results show that the band combination to be included in the ENVISAT MERIS instrument enables the interpretation of water quality, including chlorophyll a concentration using semi-empirical algorithms both for lakes and coastal waters. MERIS band 9 centred at 705 nm is proven to be of vital importance for the detection of chlorophyll a in local surface waters.

A hyperspectral model was developed for the interpretation of remote sensing data collected above inland waters. Specific absorption and scattering coefficients proposed by other authors were not suitable for modelling of the irradiance reflectance in 12 studied lakes. Therefore, special studies were carried out to estimate absorption and scattering coefficients as well as backscattering probability of suspended matter in turbid waters. AC-9 and Li-1800UW results were used for these purposes. The algorithms obtained were used to improve the model, which was then tested in forward and inverse modes.

The suitability of the AISA airborne imaging spectrometer for monitoring lake water quality was tested in four surveys carried out in southern Finland in 1996-1998. Altogether, 11 lakes were surveyed and the total number of stations with concurrent remote sensing and limnological measurements was 127. The ranges of the water quality variables were: the sum of chlorophyll a and phaeophytin a 1-100 microg l(-1), turbidity 0.4-26 FNU, total suspended solids 0.7-32 mg l(-1), absorption coefficient of aquatic humus at 400 nm 1.2-14 m(-1) and secchi disc transparency 0.4-7 m. For the retrieval analyses, 24 AISA channels in the 450-786 nm range with a channel width of 6-14 nm were used. The agreement between estimated and observed water quality variables was generally good and R2 for the best algorithms was in the range of 0.72-0.90 over the whole dataset. The channels used for May were, in most cases, the same as those for August, but the empirical parameters of the algorithms were different. After seasonal grouping, R2 varied from 0.84 to 0.95. The use of apparent reflectance instead of radiance improved the estimation of water quality in the case of total suspended solids and turbidity. In the most humic lake, the empirical algorithms tested were suitable only for the interpretation of total suspended solids and turbidity.