MERIS PRODUCTS OVER LARGE EUROPEAN LAKES - COMPARISON WITH MEASURED DATA ABOUT AEROSOL AND WATER QUALITY

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1 MERIS PRODUCTS OVER LARGE EUROPEAN LAKES - COMPARISON WITH MEASURED DATA ABOUT AEROSOL AND WATER QUALITY A. Reinart (1), H. Ohvril (2), K. Alikas (1), P. Ibrus (1), H. Teral (2), K. Valdmets (1), O. Okulov (2) (1) Tartu Observatory, Tõravere, 6162, Estonia, anu.reinart@aai.ee (2) Institute of Environmental Physics, Tartu University, Tähe 4, 51351, Estonia, hanno.ohvril@ut.ee ABSTRACT Evaluation the MERIS data over large European lakes, Vänern and Vättern in Sweden and Peipsi in Estonia/Russia is made. Characteristically these lakes have coloured dissolved organic matter as major contributor to the optical properties of the water. Potentially toxic cyanobacterial blooms occur in Lake Peipsi in late summer. We have compared the MERIS products from the latest reprocessing (MEGS7.4 and IPF 5.4) with the available in situ data of water quality but also with aerosol optical thickness measured by sun photometers. There is a reasonably good correlation between the MERIS aerosol optical thickness vs. ground measurements, and algal_2 product vs. measured C Chl over all three lakes, but no correlation was found for other optically active substances. 1. INTRODUCTION Two problems reduce the utility of satellite monitoring of freshwaters in high latitudes: 1) interference of coloured dissolved organic matter (CDOM) in the estimation of chlorophyll concentration; and 2) complicated procedure for atmospheric correction because of high reflectance of turbid waters at NIR bands. The MERIS has spectral bands (centred at 65 nm, 681 nm and 75 nm) that allow the estimation of chlorophyll in the presence of CDOM, and it is using a neural network technique for turbid waters instead of band ratio algorithms (Schiller & Doerffer, 1999). The MERIS atmospheric correction procedure has especially been adapted for turbid waters using a coupled hydrological-atmospheric model (Moore at al., 1999). The present paper investigates the possibility of using the MERIS images to monitor the water quality of the European Union s three largest lakes (Table 1): Lake Peipsi in Estonia-Russia, and lakes Vänern and Vättern in Sweden. As correct atmospheric correction is a key for good water quality estimates, MERIS products about aerosol optical thickness (AOT) at certain wavelengths are compared with AERONET measurements and other data available for regions close to the lakes. Further MERIS products (chlorophyll a, suspended matter, absorption by dissolved organic matter) are validated against in situ water quality parameters obtained by long-term monitoring programs in these lakes (Strömbeck & Pierson, 21; Reinart et al., 24a). 2. MATERIAL AND METHODS 2.1. Description of lakes All three studied lakes are located almost on the same latitude ( N). The two Swedish lakes are west of the Baltic Sea and more affected by the Atlantic Ocean and the Gulf Stream while Peipsi, on the border between Estonia and Russia, is more affected by the continental climate. Table 1. Morphometric data (Nõges, 21; Kvarnäs 21), concentrations of phytoplankton pigments (C Chl ), total suspended matter (C TSS ), and absorption by coloured dissolved organic matter (means ± 1 standard deviation) measured in water samples, ranges of Secchi depth in the years 23 and 24. Parameter Peipsi Vänern Vättern Area, km Mean depth, m Max. depth, m C chl, mg m ± ± ±.3 C TSS, g m ± 4.5 ±.1.5 ±.1 a CDOM (443), m ± ±.5.3 ±.1 Secchi depth, m Lake Peipsi is a large shallow lake (Table 1). Because of the heavy nutrient load by the Estonian River Suur- Emajõgi and the Russian River Velikaya lake is mesotrophic or even eutrophic (Nõges, 21). Water transparency in Peipsi primarily depends on the development of phytoplankton in its northern part, while in other parts it also depends on the amount of humic substances. Toxin-producing cyanobacteria have been recorded in Peipsi with concentrations up to 1 mg m -3 by the end of summer. Lake Vänern (Table 1) in central Sweden is divided into two basins by a shallow archipelago area. Its water quality is classified as moderately nutrient-rich, and measurements of algal biomass indicate oligotrophic conditions (Willén, 21). The southern basin is slightly more turbid than the northern, but river inflow mainly Proc. Envisat Symposium 27, Montreux, Switzerland April 27 (ESA SP-636, July 27)

2 into the northern basin makes it more humic. In this lake, the bloom of phytoplankton in spring (May - early June) is often more pronounced than the second bloom in August. Lake Vättern is about 2 km east of Vänern and comprises only one rather narrow basin (width less than 15 km). It has a relatively small watershed, and seasonal variations in the tributaries discharge have very little impact on the lake's water quality. Ultra-oligotrophic conditions prevail in this lake (Willén, 21). The water transparency measured by Secchi depth is highest in Vättern, m, it is lower in Vänern ( m) and the lowest in Peipsi ( 4.8 m). All together these three lakes represent a wide range of optical variability in water bodies (Table 1) Laboratory analyses of water samples The first sources for in situ data are the regular state monitoring programmes. In lakes Peipsi (state monitoring database: Vänern and Vättern (Swedish University of Agricultural Sciences, SLU database: regular monitoring includes the measurement of chlorophyll a concentration. Data about suspended sediments and absorption by CDOM, however, are available only occasionally. Regular sampling in Peipsi in the years 23 and 24 was performed 2-3 times per month (May-November) at 6 points, but only 5 times per year in lakes Vänern (5 points) and Vättern (2 points). Therefore dataset for Peipsi included much more data than for L. Vänern and L. Vättern (Reinart et al., 24 a, b) Lindell et al. (1999) have suggested methods for remote sensing of lakes. For chlorophyll concentration, water was filtered through Whatman GF/F-filters, and the chlorophyll a + pheophytin a concentration (C Chl ) was measured with a spectrophotometer on ethanol extracts of the filters. The concentration of total suspended matter (C TSS ) was measured gravimetrically (precision of.1 mg) after filtration of a measured volume of water through pre-weighed and pre-combusted Whatman GF/F-filters. Absorption by coloured dissolved matter (a CDOM ) was measured in the spectral region of 4-75 nm in water filtered through µm filters; the value at 443 nm is reported here Satellite data Access to MERIS reduced resolution (RR) images (processor IPF 5.4) have been possible through a Category 1 user project (ID 318, PI A. Reinart) Testing the applicability of MERIS L2 products for monitoring humic coastal and lake waters in Baltic Sea region. We have collected a total of 297 images from lakes Peipsi, Vänern and Vättern during the ice-free period (May-September) in 23 and 24. Because of the frequent cloud cover and rather rare in situ measurements, only 38 images could be used to compare satellite products with direct measurements. Images were visualised and analysed using the software tool BEAM 3.5 (Brockmann Consult/ESA). In situ measurement points were marked with a pin in the MERIS RR L2 images and a 3 x 3 pixel area around the pin was examined. Average concentrations and standard deviations were calculated at every measurement point for every date based on nine pixel values in algal_1, algal_2, total suspended matter and yellow substance s products Description of model for AOT calculations Although the number of solar photometers has considerably increased during the last years, there is no one just next to the studied lakes. Fortunately, several approximate methods for evaluation of Aerosol Optical Thickness at wavelength λ (AOT (λ)) have been elaborated. Suitable accuracy of approximate methods would enable an alternative evaluation of AOT (λ) for locations or periods where/when spectral observations are/were not available. In this work, we have used data from two stations measuring broadband direct solar beam Tiirikoja in Estonia on the coast of L. Peipsi and Karlstad in Sweden on the coast of L. Vänern. Closest AERONET stations located in Tõravere (Estonia), where also broadband direct solar beam measurements are made and Gotland (Sweden) ( For transition from classic broadband direct solar beam measurements to the spectral AOT(λ), a model created by Gueymard (1998) is used. As input, it uses the values of direct solar beam S m, optical mass m, precipitable water W, columnar O 3 and NO 2 contents, but a fixed Ångström wavelength exponent; α = 1.3. As output, the model gives values of AOT(1), i.e. aerosol optical depth at a basic 1 nm. Transition to any other wavelength e.g. to AOT(5) is available using the Ångström s formula. 3. RESULTS AND DISCUSSIONS 3.1. Validation of aerosol optical thickness estimated by broadband measurements For evaluation of Gueymard s model for our conditions we have used 81 days with clear solar disc at Tõravere. Joining spectral and broadband databases, i.e. data for AOT(5) from AERONET and for broadband direct

3 solar beam S m, we selected only these where observations are made within a time interval of 1 minutes for both - a spectral and a broadband parameter. The joint database lists 1741 integrated (spectral + broadband) observations. Another input parameter of Gueymard s model, the amount of precipitable water, W, usually changes only slightly during a 24-h period and has a good correlation with surface humidity parameters. In this research we applied a parameterization for Tõravere, developed for 12 UTC clear sky radio soundings (Okulov et al., 22): W(cm) =.148e +.4 (1) where e is the 12 UTC water vapor pressure in hpa (mbar). For the trace gases next fixed values were used: O 3 =.34 cm, NO 4 (tropospheric) =.1 cm, NO 2 (stratospospheric) =.1 cm. A plot of calculated AOT(5) by Gueymard s values against the measured ones by AERONET is seen in Fig. 1. from south and east directions (April). Forest and bog fires in the Baltic states, Poland and Russia increased AOT in July, August and September. The fires influenced mainly the two Estonian locations and Gotland. In all these three locations the August 22 monthly means of AOT(5) were higher than 5. The fires had less impact to air quality in Karlstad and Norrköping. AOT (5) Karlstad Norrköping Gotland Tõravere Tiirikoja April May June July Aug Sept AOD (5) by Gueymard y =.87x R 2 = AOT (5) by AERONET Fig. 1. Modelled (using Gueymard s model) versus measured (by AERONET) AOT(5) for summer conditions at Tõravere in 22. Despite of the high correlation between the predicted and measured AOT(5) values (R 2 =.992) the modelled values of the AOD (5) are systematically underestimated by 13%. One possible reason for model s underestimation is its fixed value for α (1.3), as by measurements for this dataset the average value was α = 1.41 and climatological average for 22 summer is 1.45 (Teral et al., 24) Seasonal variation of AOT(5) at five stations around Baltic Sea. Example of evolution of monthly mean AOT(5) values is shown (Fig. 2) for April-September, 22 in given 5 locations. Specific for this year was very hot and dry weather and intervention of contaminated air Fig. 2. Monthly means of AOT(5) in 2 Estonian and 3 Swedish locations in year Comparison of measured and MERIS derived AOT values Aerosol optical properties has major effect for atmospheric correction over water. One indicator for successfulness of atmospheric correction is also, when derived aerosol properties fit to the real one. MERIS aerosol product over water contains AOT values at 55 and 865 nm. We have extracted pixel values in one sampling point in both lake (closest to AOT measurement station), and compared it with AOT measured in coastal station. The best correlation (R 2 =.52) and slope value close to 1, is observed for Vänern dataset at 55 nm (Fig. 3), while lower correlation and overestimation about 4% is observed at 865 nm. In turbid Peipsi however the correlation was lower for both bands (R 2 =.34 and.31, accordingly at 55 and 865 nm), and overestimation up to 84%. Sometimes very high values appear on MERIS AOT product which are not supported by measurements and calculations. It is also typical that lower AOT values coincidence better with measured values than high values, which accompanies with low alpha. More likely this is related to large particles in atmosphere, and may be caused by clouds, not detected by MERIS cloud mask. Over lakes water pixels are flagged often as invalid aero_alpha, aero_opt_thickness also Uncertain aerosol type. Flag aerosol model is out of

4 aerosol model database is raised over turbid areas of Peipsi. _tiirikoja.8 MERIS AOT 55 (Vänern) MERIS AOT 865 (Vänern) y = 1.6x R 2 = Measured AOT 55 (Karlstad) y = 1.37x R 2 =.1.3 Measured AOT 865 (Karlstad) Fig. 3. Comparison of AOT(55) and AOT(865) estimated by MERIS pixels values over Vänern and calculated by broadband measurements in Karlstad Comparison of measured and MERIS derived optically active substances Satellite data confirm a notable spring bloom (C Chl up to 45 mg m -3 ) over the entire Peipsi. The bloom is slightly lower in open waters than close to the coast, where water heats up quicker. In June-July, C Chl decreases to 5 mg m -3 in the middle of lake, but in narrow parts (Lämmijärv) the midsummer bloom (mainly cyanobacteria) starts early in July. The MERIS data also show an increase in C Chl in August-September in central part of lake, in Lämmijärv such an increase is not notable and the values are even lower than in spring. However, from field observations it is known, that cyanobacteria may form a dense layer on the surface and concentrations may rise up to several hundred mg m -3. In Vänern a spring bloom is notable in May over entire lake, and these values are much lower than in Peipsi. After that, the C Chl values remain rather low ( 2-6 mg m - 3 ) and do not vary much in the open lake, but close to coast the MERIS data result in rather high values in June and again later in August-September (occasionally up to 3 mg m -3 ). Such high values are not supported by measurements. The in situ dataset gives an average C Chl for Vänern of 3.6 ± 1.9 mg m -3. One or two samplings during summer, however, may easily miss rapid changes in water quality. In Vättern there is basically no difference between the northern and southern part of lake, and algal_2 values stay very low without a clear seasonal trend. The MERIS algal_2 is the lowest with the smallest variation in Vättern (1.9 ±.9 mg m -3 ) and the highest in Peipsi (15.1 ± 11.6 mg m -3 ). This order agrees with field measurements (Fig. 4). However, the average values from the MERIS results are higher than the measured values in lakes Vättern and Vänern, and lower than the maximal values measured in Peipsi. Despite the fact that all points are located around the 1:1 line in linear correlation plots, the scatter is rather wide. A good correlation can only be obtained when all lakes are included (y[meris]=1.16x[in situ], R 2 =.52, N=76). In Peipsi, it was observed that in the case of extremely high measured C Chl (>45 mg m -3 ) the MERIS results were particularly low (~1 mg m -3 ). These values are not included in the regression analyses above as these values exceed the algorithm limit. No correlation was found between measured and satellite based estimations of C TSS in the studied lakes. The in situ C TSS in Peipsi varied between 1 and 26 g m -3 with higher values in the southern shallow regions towards autumn. The overall mean value was 5.9 ± 4.5 g m -3 which is remarkably higher than in the two deep Swedish lakes. The MERIS results are lower than the measured values for Peipsi, but they are higher than the measured values for lakes Vänern and Vättern. In Vänern, measurements are in the range of -.8 g m -3, but the MERIS gives values up to 6.4 g m -3 for the same points. Average value for MERIS and in situ datasets are rather close. For Vättern, there is only very few in situ data available (ranging -.5 g m -3 ) and they actually match the MERIS results (mean g m -3 ). However MERIS often gives much higher values which have never been sampled in Vättern and these are flagged commonly as uncertain values after neural network procedure. Values of a CDOM (443) in lakes Peipsi and Vänern are higher in samples from river mouths or shallower areas close to the shore. Minimal values measured in Peipsi are comparable with maximal values in Vänern, while in Vättern a CDOM (443) was much lower and less variable than in the other two lakes.

5 Frequency Frequency L. Peipsi More C chl (mg m -3 ) 11 flag PCD_ 17 applied in situ L. Vänern C chl (mg m -3 ) flag PCD 17 applied in istu More Comparison of measured values with MERIS estimates for a CDOM (443) did not show any correlation. The MERIS data largely underestimated a CDOM (443) in lakes Peipsi and Vänern, and there was also a high pixel to pixel variation. For Vättern, however, the satellite estimates are close to the in situ data (-.5 m -1 ), but this result is only based on four data points. Based on satellite data, there is no difference of a CDOM (443) between lakes Vänern and Vättern, even it is clearly notable in in situ data (Table 1). There is a special flag to indicate CDOM-loaded waters in the MERIS processing (case2_y), but this was not activated over the lakes. A flag indicating sedimentdominated turbid waters (case2_s) was active over the entire Peipsi and the southern bays of Vänern. Sometimes it was also active in the central area of Vänern where scattering cannot be high. Flag PCD_1_13, which indicates invalid results after atmospheric correction at any of the spectral bands, was actually activated almost everywhere over the lakes. Probably this is the reason why the algal_1 product was always invalid over the lakes. Flag invalid algal2_tsm_ys (any of the case_2 water products is invalid) is most commonly activated over lakes Vänern and Vättern. In Peipsi, typically -3 pixels were flagged in the coastal regions and, more often, for the southern lake regions. Overall MERIS Case2 processing resulted in invalid reflectance over the lakes and invalid concentrations of chlorophyll and suspended sediments or absorption by CDOM in lakes Vänern (7-9% of pixels), Vättern (9-1% of pixels) and Peipsi (5-49% of pixels). Frequency 1) L. Vättern C chl (mg m -3 ) flag PCD_17 applied in situ More Fig. 3. Histograms of in situ and MERIS algal_a values (when pixels values where flag showing uncertain neural network results are removed) over three studied lakes. 4. CONCLUSIONS On the basis of our data we can conclude that the MERIS Case2 chlorophyll concentration product (algal_2) and the in situ measured C Chl are correlated reasonable well (R 2 =.52). The MERIS results make a clear difference between the three studied lakes, with Peipsi having the highest and Vättern the lowest values, and seasonal variations (blooms) can be detected. The MERIS strongly underestimates very high chlorophyll values during the autumn bloom in Peipsi (>45 mg m -3 ), and it overestimates values in lakes Vänern and Vättern almost twofold. For suspended sediment and CDOM there were no correlations between the MERIS and measured data. Total_susp still provides a reasonable spatial distribution for most of the lake areas (excluding the coastal region of Vänern). Absorption by CDOM is strongly underestimated in lakes Vänern and Peipsi. From the limited amount of data presented here, we see that MERIS has already shown its capability and great potential to detect changes in optical properties of lakes.

6 Before MERIS images will be used in long-term programs for monitoring the water quality of large lakes, validation of the MERIS products should continue including the validation of the relation between OAS and the inherent optical properties to specify local algorithms. This will allow to improve estimates especially for the parameter most often included into monitoring programs and also directly related to ecological conditions. Acknowledgements We thank ESA/ENVISAT, EO Help Desk and Brockmann Consult for the production and distribution of images. This study is supported by the Estonian Science Foundation Grant s 6814, 5738 and the Swedish National Space Board grant 132/3. Aerosol data over Sweden are processed in SMHI (T. Carlund). 5. REFERENCES Gueymard, C., Turbidity determination from broadband irradiance measurements: a detailed multicoefficient approach. J. Appl. Meteor., 37, Kvarnäs, H., 21. Morphometry and hydrology of the four large lakes of Sweden. Ambio 3: Lindell, T., D. Pierson, G. Premazzi & E. Ziliolli (eds), Manual for monitoring European Lakes using remote sensing techniques. Office for Official Publications of the European Communities, Luxembourg. Moore, G. F., J. Aiken & J. Lavender, The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: application to MERIS. International Journal of Remote Sensing 2: Nõges, T. (ed), 21. Lake Peipsi. Meteorology, Hydrology, Hydrochemistry. Sulemees Publishers, Tartu, Estonia. Okulov, O., Ohvril, H. and Kivi, R., 22. Atmospheric precipitable water in Estonia, Boreal Environment Research, 7, Reinart, A., B. Paavel, D. Pierson & N. Strömbeck, 24a. Inherent and apparent optical properties of Lake Peipsi. Estonian Boreal Environment Research 9: Reinart, A., B. Paavel & L. Tuvikene, 24b. Effect of coloured dissolved organic matter on the attenuation of photosynthetically active radiation in Lake Peipsi. Proceedings of the Estonian Academy of Sciences. Biology. Ecology 53: Schiller, H. & Doerffer, R., Neural network for emulation of an inverse model operational derivation of Case II water properties from MERIS data. International Journal of Remote Sensing 2: Teral, H., Ohvril, H., Laulainen, N., 24. Variability of Ångström coefficients during summer in Estonia. In: Fourth Study Conference on BALTEX, May 24, (24 28 May 24, Gudhjem, Bornholm, Denmark), Conference proceedings, H.-J. Isemer (Ed.). International BALTEX Secretariat, 29, pp Strömbeck, N. & D. Pierson 21. The effects of variability in the inherent optical properties on the estimations of the chlorophyll a by remote sensing in Swedish freshwaters. The Science of the Total Environment 268: Willén, E., 21. Phytoplankton and water quality characterization: Experiences from the Swedish large lakes Mälaren, Hjälmaren, Vättern and Vänern. Ambio 3: