USING MERIS DATA FOR THE RETRIEVAL OF CHL A, CDOM AND TSS VALUES IN THE GULF OF FINLAND AND LAKE LOHJANJÄRVI

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1 USING MERIS DATA FOR THE RETRIEVA OF CH A, CDOM AND TSS VAUES IN THE GUF OF FINAND AND AKE OHJANJÄRVI Sampsa Koponen 1, Jenni Vepsäläinen 2, Jouni Pulliainen 1, Kari Kallio 2, Timo Pyhälahti 2, Antti indfors 3, Kai Rasmus 4, Martti Hallikainen 1 1. aboratory of Space Technology, Helsinki University of TechnologyPO BOX 3000, HUT, FINAND. First name dot last name at hut dot fi 2. Finnish Environment Institute PO BOX 140, SF Helsinki, FINAND 3. uode Consulting OY Hietalahdenkatu 7 A 13, FIN Helsinki, FINAND 4. Division of Geophysics, University of Helsinki, P 64, University of Helsinki, FINAND ABSTRACT We present results obtained with data collected during two water quality measurement campaigns: Gulf of Finland (Baltic Sea) on 27 April 2004 and Finnish ake ohjanjärvi on 5 August The concentrations of chlorophyll a, total suspended solids and the absorption coefficient of colored dissolved organic matter are retrieved using MERIS and airborne spectrometer data. The retrieval is based on empirical algorithms developed with ground truth data. For the Gulf of Finland case the ground truth data include ten water samples analyzed in a laboratory and 5103 data points measured with a flow though instrument. For the ake ohjanjärvi the number of water samples is 11 and the number of flow though data points is The coefficient of determination of the flow through data with water sample data is over 95 % for all three optically active substances. Hence, the flow through data (in total data points) can be used as accurate ground truth for satellite and airborne observations. In the Gulf of Finland the retrieval accuracy (R 2 ) of the three parameters is % when channel ratio algorithms are used with MERIS and flow through data. With airborne data the accuracies are %. In addition to the water quality analysis, the field campaign data were used to study the usefulness of an atmospheric correction method specifically developed for Case 2 waters. The atmospheric correction method utilizes bio optical reflectance modeling and principal component analysis. Both field campaigns represent high concentrations of optically active substances such as chlorophyll a, total suspended sediment and humus. 1 INTRODUCTION The remote sensing of water quality in Case II waters is complicated. This is especially true in the fragmented coastline and lakes of Finland. Therefore, one of the main parameters of a remote sensing instrument intended for monitoring the quality of coastal and inland water is its spatial resolution. Proper channel combination with sufficient spectral resolution, frequent overpasses and good availability of data are the other important factors. The Medium Resolution Imaging Spectrometer (MERIS) onboard the Envisat satellite has 300 m resolution and 15 channels in the visible to near infrared (NIR) region (from to 900 nm). Hence, it is an ideal candidate for monitoring fragmentary water areas. The objective of this paper is to investigate the use of MERIS (and airborne spectrometer) data in the retrieval of optically active substances (OAS) such as Chlorophyll a (chl a), Total Suspended Solids (TSS) and Colored Dissolved Organic Matter (CDOM) in Finnish Case II waters. The retrieval accuracy is analyzed using empirical band ratio algorithms together with an extensive set of ground truth data. Earlier studies using AISA [1 3], simulated MERIS data [4] and real MERIS data [5] have shown that algorithms based on ratios of two bands (NIR/red) yield good results for chl a in Case II waters. For CDOM estimation, simulations of water leaving radiance reflectances of three lakes in Sweden by [6] and boat measured spectrometer data [7] show that an algorithm suitable for CDOM estimation is a ratio of a channel with wavelength > 600 nm to a channel with wavelength in the nm range. In contrast to the color indices used in this work the evel 2 MERIS product distributed by the European Space Agency (ESA) is based on the use of neural networks (NN)[8]. The input for the NN includes atmospherically corrected reflectances of eight MERIS channels. 2 DATA The data used in this study were collected during two multi sensor water quality measurement campaigns. The 1 st one was conducted at the coast of Finland, near the city of Helsinki on April 27, The campaign took place during an algae spring bloom dominated by dinoflagellates and diatoms. The spatial variations of OAS were substantial within the measurement area. The 2 nd one took place at a meso eutrophic ake ohjanjärvi on 5 August 2004 (A = 88 km 2 ). In addition to the extensive ground truth, both datasets include airborne and spaceborne remote sensing observations.

2 2.1 Ground truth data The ground truth data include transects measured with a flow through measurement system [9] installed on a boat (measurement depth 0.5 m), water samples collected along the transect (measurement depth 0.5 m), and Secchi depth (SD) measurements. The length of the transect was 28 km at the coastal site (5103 data points) while at ake ohjanjärvi the transect consisted the whole lake (27301 data points). The water samples were analyzed in a laboratory and yielded values for chl a, TSS, and the absorption coefficient of CDOM at 400 nm (a CDOM (400)). The values for both campaigns are shown in Table 1. The flow through system measures temperature, conductivity, and total absorption and scattering coefficients (a tot (λ) and b tot (λ), respectively) at 9 wavelengths (λ, between 412 and 715 nm) using the ac 9 instrument. Based on the results from the laboratory analysis it is possible to develop algorithms that transform b tot (λ) and a tot (λ) into OAS values (R 2 > 0.95). At ake ohjanjärvi study area also top of water reflectances were collected from the water sampling stations using ASD HandHeld spectrometer, which measures wavelengths between nm. To obtain water leaving reflectances, measurements of downwelling radiance were made on each sampling station using a white reference panel. Table 1. Water samples analyzed in laboratory. Gulf of Finland ake ohjanjärvi Station number Chl a TSS * (mg/l) a CDOM (400) SD (m) Station number Chl a TSS * (mg/l) a CDOM (400) SD (m) S S1 25 8,6 8, S S , S S ,2 0.8 S S4 7,9 2,6 5,73 S S5 2,9 2,2 1,8 4.8 S S6 7,6 2 3,2 3.0 S S7 4,5 1,4 3,7 3.3 S S8 5,7 3 3,7 2.2 S S9 9,3 3,8 3, S S10 8,3 4,2 3, S ,4 3, * The TSS values were obtained with GFF filters. 2.2 Airborne and spaceborne data The AISA instrument used in the airborne part of the campaign was flown at 1 km altitude onboard a Short SC 7 Skyvan research aircraft. For this campaign 32 channels that match the MODIS (EOS Terra and Aqua satellites) and MERIS channels in the wavelength range nm were selected. The pixel size is 2 by 2 m. The calibrated AISA radiances at the sampling stations were extracted by finding the AISA pixel closest to the location of each station. The average radiance values within an 11 by 11 pixel area around each station were also extracted. The same procedure was then used with the flow through data points. As the AISA data does not cover the whole boat transect at both field campaigns, the number of usable data points for the analysis with combined AISA and flow through data is 4649 and 6238 for coastal and lake campaign, respectively. The spaceborne data include evel 1b MERIS observations acquired within two hours from the start of the ground truth data collection. For the coastal site a evel 2 product was also obtained. The sensor zenith angle θ sat for the measurement area was near 18 for coastal and 31 o for lake campaign, while the solar zenith angle θ sun was 47 for both. Cloud cover prevented the observation of sampling stations S1, S2 and S3 with MERIS at the coastal site. Hence, those three data points were excluded from the satellite data analysis. For the flow through data the extraction was performed by finding the flow through values located within each MERIS pixel and averaging them. After cloudy pixels were removed, the number of data points was 73 and 58 for coastal and lake data, respectively. 3 ANAYSIS AND RESUTS 3.1 Airborne data In this study the channel combinations for the algorithms of each OAS were taken from previous studies (see the references in the Introduction). The algorithms for chl a and a CDOM (400) use band ratios while for TSS a single channel is used. The channel combinations and the regression coefficients with the present airborne dataset and the OAS values collected at the sampling stations are shown in Table 2. Stations S2 and S5 are outliers for TSS and S2 is an outlier for chl a. They were excluded from the analysis when the coefficients for the algorithms presented in Table 2 were

computed. Visual examination of AISA data shows that a ship has passed the area near S2 and disrupted the spatial distribution of OAS.

The measured chl a values are quite low near S5 when compared to the rest of the coastal area. This applies to both laboratory and flow through data as well as for airborne data.

The data points around S2 are again outliers for chl a. If those are excluded from the analysis R 2 improves from 84.1 to 94.1. For a CDOM (400) the data points around S2 are not outliers.

3 computed. Visual examination of AISA data shows that a ship has passed the area near S2 and disrupted the spatial distribution of OAS. The value of a CDOM (400) in turn decreases much more steadily as the distance from shore grows and seems not to be as susceptible to local changes as chl a and TSS are. The measured chl a values are quite low near S5 when compared to the rest of the coastal area. This applies to both laboratory and flow through data as well as for airborne data. The AISA radiances were compared with the flow through data using the same channels as used with the laboratory samples. The resulting R 2 values are shown in Table 2. The data points around S2 are again outliers for chl a. If those are excluded from the analysis R 2 improves from 84.1 to For a CDOM (400) the data points around S2 are not outliers. Filtering (running average with 1*40 sample window) improves the results, especially for a CDOM (400). Table 2. Retrieval algorithms for AISA and water sample (WS) as well as flow through (FT) data for the Gulf of Finland. xyz is the radiance (in W m 2 sr 1 µm 1 ) detected at channel with a central wavelength of xyz nm. WS: Chl a WS: TSS GFF (mg/l) WS: a CDOM (400) FT: Chl a FT: TSS GFF (mg/l) FT: a CDOM (400) Algorithm R 2 (%) (no S2) R 2 (%) (filtered FT) n 8 (no S2) 7 (no S2&S5) (no S2) /4649 Fig. 1a and Fig. 1b present Chl a and a CDOM (400) estimations for ake ohjanjärvi with AISA data. The algorithms for the estimation are Chl a = 130.8*(704)/(670) (R 2 =0.5)and for a CDOM (400) = 20.34*(704)/(490) (R 2 =0.95) Fig.1a) Chl a (in µg/l) with AISA data, ake ohjanjärvi. Fig. 1b). a CDOM (400) (in 1/m)with AISA data, ake ohjanjärvi. 3.2 Spaceborne data The analysis of the MERIS data was similar to the one performed with airborne data. The biggest difference is that now the algorithm for TSS is also based on a band ratio since the R 2 value for the single channel algorithm was less than 0.8. The algorithms for MERIS and flow through data are shown in Table 3. The low values near S2 are also visible in the MERIS image, although the magnitude of the change is not as clear. The values of TSS and a CDOM (400) also change but not as dramatically. Fig 2a shows the scatter plot for chl a with MERIS data and Fig. 2b shows the same for a CDOM (400). The amount of flow through points within MERIS pixels varies from 1 to 269 (average is 45). The results improve significantly if the MERIS pixels that contain less than 20 flow through points are excluded. These values are also shown in Table 3. Fig. 3 shows the values of TSS, chl a and a CDOM (400) retrieved with MERIS and flow through data as a function of the distance from the beginning of the boat transect. Fig. 4a shows the thematic map for chl a with MERIS data. Airborne data are also included in the image. Fig. 4b shows the same for a CDOM (400). Table 3. Retrieval algorithms and the coefficient of determination (R 2 in %) for MERIS and flow through data. Chl a TSS GFF (mg/l) a CDOM (400) Algorithm All data (n = 73) Some pixels excluded (n = 50)* * These results include only those MERIS pixels that contain 20 or more flow through data points

120 100 Flow through data Water samples 2.6 Flow through data Water samples 2.4 Chl a 80 60 40 253 709 / 665 189 a CDOM (400) 2.2 2 1.8 1.6 20 1.4 0 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 709 / 665 1.

For the water sample data: n = 7 and R 2 = 84.3 %. Fig. 2b) a CDOM (400) vs. MERIS channel ratio ( 665 / 490 ) (n = 73, R 2 = 95.3 %). For the water sample data: n = 7 and R 2 = 98.4 %. Fig. 3.

4 Flow through data Water samples 2.6 Flow through data Water samples 2.4 Chl a / a CDOM (400) / / 490 Fig. 2a) MERIS algorithms for chl a. Chl a vs. MERIS channel ratio ( 709 / 665 ) (n = 73, R 2 = 81.6 %). For the water sample data: n = 7 and R 2 = 84.3 %. Fig. 2b) a CDOM (400) vs. MERIS channel ratio ( 665 / 490 ) (n = 73, R 2 = 95.3 %). For the water sample data: n = 7 and R 2 = 98.4 %. Fig. 3. TSS, Chl a and a CDOM (400) values with MERIS and flow through data averaged within each MERIS pixel. Fig. 4a) Chl a (in µg/l) with MERIS and AISA data. The values over 100 µg/l are shown in red. The small window shows an overview of the area, while the larger one shows the northern part of the airborne data where the most interesting features are present. Fig. 4b) a CDOM (400) (in 1/m) with MERIS and AISA data. The values over 2.5 1/m are shown in red.

3.3 Effect of atmospheric correction The bio optical model [6] is one of the key elements in developing advanced methods for atmospheric correction.

5 3.3 Effect of atmospheric correction The bio optical model [6] is one of the key elements in developing advanced methods for atmospheric correction. It enables the use of OAS in the atmospheric correction procedure and, thus, reliable estimates of the Top Of Atmosphere (TOA) reflectance can be modeled for complex CASE II waters. First, an estimate of the reflectance detected by satellite instrument must be achieved via modeling. Then, the atmospheric correction can be done by inverting the model and calculating the water leaving reflectance from the TOA reflectance [10]. Fig. 5a shows the underwater reflectances using bio optical model for both study areas for MERIS channels 1 9. In Fig. 5b, a comparison of biooptical model and handheld spectrometer is presented for ake ohjanjärvi water sample data points using MERIS channels 1 9. The results for the atmospheric correction method are shown in Figs. 6a and 6b. Fig. 6a presents the modelled TOA (Top of Atmosphere) reflectances (ρ TOA' ) compared to MERIS observed (ρ TOA ) for ake ohjanjärvi and Gulf of Finland flow through data points. The atmospherically corrected MERIS reflectances are compared with bio optical model results in Fig 6b. for both Gulf of Finland and ake ohjanjärvi. Fig. 5a) Underwater reflectances using bio optical model from coastal Vuosaari (blue) and ake ohjanjärvi (red) study areas for MERIS bands 1 9. Fig. 5b) Comparison of bio optical model (ρ +,black lines) and ASD handheld spectrometer (ρ HH, grey lines) from ake ohjanjärvi water sample data points for MERIS bands 1 9. Fig. 6a) Modelled ρ TOA ' plotted against MERIS observed ρ TOA Vuosaari (black) and ake ohjanjärvi (grey) flow through data points for wavelengths between 490 nm and 709 nm. Fig. 6b) Atmospherically corrected ρ + ' plotted against modelled ρ + Vuosaari and ake ohjanjärvi flow through data points for wavelengths between 490 nm and 709 nm. 4 DISCUSSION The values of OAS measured in a laboratory have high correlation coefficients with the airborne and spaceborne data (R 2 close to or above 0.9 in most cases). The results with the flow through data also show high correlations. With airborne data the coefficients of the algorithms for water sample and flow through data are very close to each other. Filtering the data in the flow through vs. airborne analysis improves the results. The improvement was largest for a CDOM (400) (R 2 increased from 92.5 to 97.1 %). The main reason for this is that the a CDOM (400) data from the flow

6 through instrument are noisy. For the flow through vs. satellite data analysis the filtering is not necessary since the spatial averaging caused by the 300 m pixel size reduces the noise in any case. However, removing pixels that contain a low number (less than 20) of flow through data points improves the results with MERIS data (Table 3). This is due to the fact that the MERIS pixels cover larger areas than the flow through data and can contain larger variations in water quality. Hence, only when MERIS pixels are sufficiently covered with flow through data can proper analysis take place. This is a problem also for the water sample data since each sample covers only one small point in the MERIS pixel. The spatial changes in the satellite data are much less dynamic. This is due to the larger pixel size the spatial changes are averaged out as the pixel size increases. However, the smaller scale changes in the OAS values can still be observed with satellite data in addition to the overall change from high values near the coast to lower values in the open sea. A comparison with bio optical model and field measured ρ HH ensured that the bio optical model is able to model the water leaving reflectance ρ + with sufficient accuracy. Although there are some wavelengths where the modelled ρ + do not give similar results as the field measured reflectance above the water (ρ HH ), the model can give sufficient information about the water leaving reflectance for the atmospheric correction. The first wavelengths of MERIS (413 nm and 443nm) are most problematic, as they are also in modelling the atmosphere. The results show that the atmospheric correction method is usable in the Case II waters, although it needs further improvements in the future. The MERIS evel 2 ocean product data that contain the concentrations of phytoplankton and suspended sediments, and the absorption coefficient of CDOM were flagged as invalid over the measurement area (and also most of the Gulf of Finland). Hence, it was not possible to compare our results with the standard MERIS retrieval procedures used by ESA. The likely reason for the failure of the standard products is that the water in the Baltic Sea in general and in this case in particular, is more turbid and contains more CDOM than the waters used in the development of the standard MERIS algorithm. 4 CONCUSIONS The objective here was to investigate the feasibility of using MERIS for estimating water quality in an area where the coastline is fragmented and the spatial variation of the water quality is large. The retrieval of chl a, TSS and CDOM was successful even thought the standard MERIS processing failed to provide reliable data. The accuracy of the estimation (R 2 ) for each OAS was close to or above 0.9. The results indicate that a satellite sensor with characteristics similar to MERIS is suitable for remote sensing of water quality in fragmented coastal regions. However, the retrieval algorithms still need to be refined. The algorithms should be adjustable for other study areas with the development of atmospheric correction. REFERENCES 1. Härmä P., Vepsäläinen J., Hannonen T., Pyhälahti T., Kämäri J., Kallio, K., Eloheimo K., Koponen S. (2001), Detection of water quality using simulated satellite data and semi empirical algorithms in Finland. The Science of Total Environment, 268 (1 3), Kallio, K., Koponen, S., and Pulliainen, J. (2003), Spatial distribution of chlorophyll a in two meso eutrophic lakes as estimated by airborne imaging spectrometry. International Journal of Remote Sensing, 24 (19), Koponen, S., Pulliainen, J., Servomaa, H., Zhang, Y., Hallikainen, M., Kallio, K., Eloheimo, K., and Hannonen, T. (2001), Analysis on the feasibility of multi source remote sensing observations for chl a monitoring. The Science of the Total Environment, 268 (1 3), Koponen, S., Pulliainen, J., Kallio, K., and Hallikainen, M. (2002), ake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment, 79 (1), Ruddick K., Park Y., Nechad B. (2004), MERIS imagery of Belgian coastal waters: mapping of Suspended particulated matter and chlorophyll a. Proceedings of MERIS User workshop, Frascati, Italy (ESA SP 549, May 2004). 6. Pierson, D.C. and Strömbeck, N. (2000), A modelling approach to evaluate preliminary remote sensing algorithms: Use of water quality data from Swedish Great akes. Geophysica, 36, Kallio, K., Pulliainen, J. and Ylöstalo, P. (2005), MERIS, MODIS and ETM channel configurations in the estimation of lake water quality from subsurface reflectance with semi analytical and empirical algorithms. Geophysica (submitted). 8. Schiller, H., and Doerffer, R. (1999), Neural networks for emulation of an inverse model operational derivation of case II water properties from MERIS data. International Journal of Remote Sensing, 20, indfors A., Rasmus K., Strömbeck N. (2005), Point or pointless quality of ground data. International Journal of Remote Sensing, 26(2),

7 10. Pulliainen J., Vepsäläinen J., Kallio K., Koponen S., Pyhälahti T., Härmä P, Hallikainen M. (2000). Monitoring of Water Quality in ake and Coastal Regions Using Simulated ENVISAT MERIS Data. Proceedings of ESA ERS ENVISAT Symposium, Göteborg, October, 2000, pp. 10.

Zu-Tao Ou-Yang Center for Global Change and Earth Observation Michigan State University

Zu-Tao Ou-Yang Center for Global Change and Earth Observation Michigan State University Ocean Color: Spectral Visible Radiometry Color of the ocean contains latent information on the water qualitycdom,