ON SUITABILITY OF MODIS SATELLITE CHLOROPHYLL PRODUCTS FOR THE BALTIC SEA CONDITIONS

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1 ON SUITABILITY OF MODIS SATELLITE CHLOROPHYLL PRODUCTS FOR THE BALTIC SEA CONDITIONS L. Metsamaa, T. Kutser Estonian Marine Institute, University of Tartu Mäealuse 10A Tallinn Estonia ABSTRACT The amount of phytoplankton, usually expressed as concentration of chlorophyll-a, is one of the most important parameters in describing state of water bodies. The amount of chlorophylla in coastal waters is extremely variable both spatially and temporally. Therefore, the classical monitoring programs, which are based on infrequent measurements in a few fixed stations, or ships of opportunity cannot provide spatial and/or temporal coverage needed to determine the amount of chlorophyll-a. Remote sensing could be the tool to provide necessary information. Chlorophyll retrieval algorithms proposed by different authors are used by space agencies to provide satellite derived chlorophyll products. These products are relatively accurate in Case I (oceanic) waters. However, they often fail in Case II (coastal) waters. We have compared MODIS chlorophyll product with in situ measurements carried out in the frame of Estonian National Monitoring Program. The results indicate that correlation between satellite derived chlorophyll estimates an in situ measurement results is very pure in Estonian coastal waters and there is strong need to develop local chlorophyll retrieval algorithms. This is a task our group is currently working on. 1. INTRODUCTION Reliable, spatially covering and cost-efficient monitoring techniques of coastal waters are generally growing in importance as a consequence of increasing symptoms of the on-going eutrophication process. Phytoplankton blooms, some of them being toxic, are extremely patchy, both temporally and spatially. Consequently, they often remain unobserved using the traditional sampling methods based on temporally sparse sampling at fixed monitoring stations (Härmä et al. 2001). The use of unattended flow-through systems on ship-of-opportunity (Leppänen et al., 1995; Rantajärvi et al., 1998), airborne (Dekker et al., 1992; Jupp et al., 1994) as well as satellite remote sensing (Kahru et al., 1993, 2000; Kahru, 1997; Kutser, 2004) have been recommended to provide more reliable information about the water quality characteristics (e.g. chlorophyll a concentrations) than the conventional monitoring programs can provide. However, standard algorithms developed for MODIS overestimate chlorophyll concentrations in the Baltic Sea by % even in no bloom conditions (Darecki and Stramski, 2004). MODIS standard algorithms were designed primarily for Case 1 waters, where optically significant constituents of seawater are assumed to covary with chlorophyll concentration (Gordon and Morel, 1983; Morel and Prieur, 1977). The Baltic Sea waters do not satisfy this assumption and can be classified as Case 2 waters, so the overall poor performance of Case 1 water algorithms in the Baltic is not surprising (Darecki et al., 2003).

2 It is well recognized that Case 2 waters require new algorithms based on new approaches for dealing with both atmospheric correction and retrievals of ocean bio-optical properties from water-leaving radiance (Sathyendranath, 2000). The prospects of better remote sensing of Case 2 waters is now improving with technological advances in ocean color sensors and scientific efforts underway to gain an in-depth understanding of optics in Case 2 waters. However, before future achievements in these areas are applied to remote sensing, routine processing of global satellite data from sensors such as MODIS will probably continue to be executed indiscriminately for all waters of the world s oceans with standard algorithms designed primarily for Case 1 waters. Therefore, it is useful to develop an understanding of limitations and to quantify errors of current standard algorithms in various Case 2 waters, especially as no specific algorithms exist that would allow masking of regions where Case 1 algorithms may not hold (Darecki and Stramski, 2004). The main purpose of this work is to compare chlorophyll data from monitoring measurements and from flow-through system with MODIS standard product for the Baltic Sea. 2. MATERIAL AND METHODS Two types of chlorophyll-a measurements were used in this study. First of all measurements were provided by a flow-through system which is installed onboard co-operating passenger ferry traveling between Tallinn and Helsinki. This system is measuring automatically chlorophyll-a, salinity, temperature and nutrient concentrations in the surface water. The main features of the system on route Tallinn-Helsinki is shown in table 1. Table1: Main feature of the flow-through system on route Tallinn-Helsinki ( Route: Ship: Ferry company: System type: Frequency: Travel time: Control: Tallinn (Estonia) Helsinki (Finland) M/S Romantika Tallink, Estonia Flow through system. Flow through data twice per day. Automated sampling for phytoplankton, chlorophyll-a and nutrients once per week. 3,5 hours Once per week Features: Brackish water salinity about 5 Measured parameters: Conductivity, water temperature, florescence; automated samples for nutrients, chlorophyll-a and phytoplankton. Spatial resolution: Flow trough system: salinity, temperature and florescence approximately every 200 meters. Water sampling for subsequent laboratory analysis at fixed latitudes. Remote control: Data transfer: Data storage: Depth of water intake: No Diskette Every 20 s outside harbor area 4 meters

3 Surface water samples are collected for water analysis twice a month during ice free period in the frame of Estonian National Monitoring Program. Total number of the monitoring stations is 9. Measurements are carried out in Narva, Tallinn and Pärnu Bays (see figure 1) with three measurement stations in each Bay. Chlorophyll-a is measured from the water samples. Figure 1. Map of Estonian National Monitoring Program sampling stations. Three highlighted stations in Narva (N12, N8, N38), Tallinn (57a, 2, F3) and Pärnu (K5, K21, K2) Bays are under investigation with increased measuring frequency. Highlighted stations with WQ designation are marking a flow-trough system rout between Tallinn (Estonia) and Helsinki (Finland) MODIS has 13 visible and near-infrared bands that could potentially be used in aquatic remote sensing. Bands 1 and 2 are with 250 m spatial resolution, bands 3 and 4 are with 500 m resolution and bands 8-16 are with 1000 m spatial resolution (see table 2). Image data was downloaded via internet from Ocean Color Web. Geo correction and further processing for HDF format satellite images were done with ENVI software. In the present stage of studies we tested suitability of standard chlorophyll product (OC3) in the Baltic Sea condition. There were seven cloud free days in period from April 3 rd to July 10 th 2006 when MODIS images were collected together with the flow-through data. The number of cloud free days matching with water sampling in the frame of the coastal monitoring program was ten during the period. This data was used for the analysis.

4 Table 2: Wavelength ranges of MODIS bands that can be used for marine studies Band wavelengths, nm RESULTS AND DISCUSSION There were 24 chlorophyll-a in situ samples, collected during the coastal monitoring program, which were comparable with MODIS satellite spectrometer observations. MODIS standard chlorophyll- a product values were unrealistic in two cases and left out from the further analysis. Correlation between the chlorophyll-a values from monitoring data and MODIS algorithm is R 2 =0.51 which is quite good. At the same time, as you can see in the figure 2, MODIS algorithm overestimates the chlorophyll-a at least two times. In the figure 2 we can also see that the satisfying correlation is caused by two measurements, which contain biggest chlorophyll-a concentrations. If the chlorophyll-a concentrations vary in case of so called regular boundaries the correlation between in situ measured and satellite assessed chlorophylla is weak. For example if the in situ measured chlorophyll-a is 4-5 mg/m 3, than MODIS estimated chlorophyll-a ranges from 2 mg/m 3 to 24 mg/m 3. This shows that MODIS standard algorithms are not suitable for waters of the Baltic Sea.

5 50 45 MODIS standard product Chlor-a, mg/m R 2 = in situ measured chlorophyll-a, mg/m3 Figure 2.Correlation between the chlorophyll-a values from monitoring data and MODIS algorithm. Blue line is regression line and one-to-one correlation between the chlorophyll-a values from monitoring data and MODIS algorithm is shown with dashed line In addition to water sampling in the frame of the coastal monitoring program we took chlorophyll-a values from flow-trough system data. Correlation between chlorophyll-a measured from the flow-trough system and estimated from MODIS data was low (R 2 =0.21) if all available data was used in the analysis. We analysed also spring (diatoms) bloom and summer (cyanobacteria) bloom data s separately. Correlation between measurements and MODIS estimates was very good (R 2 =0.86, see Fig 3.) during the time of spring bloom. The correlation was even higher if we used only flow-through data R 2 =0.91, but there were only 4 samples under the investigation. It is clearly seen (Fig. 3) that MODIS overestimates the chlorophyll-a by about 2-3 times during spring bloom. In summer 2005 extensive surface accumulations of cyanobacteria took place throughout the Baltic Proper. The most violent bloom occurred in the Central Baltic Proper. The cyanobacterial situation in the Gulf of Finland, where the bloom is usually most dense, was not so critical. Correlation between flow-trough system measured and MODIS assessed chlorophyll-a is R 2 =0.51 (see figure 4) and the deferent s between this data s is 7-10 times. Relatively good correlation between flow-through and satellite data was surprising. The problem is that cyanobacteria can regulate their buoyancy and tend to keep themselves close to water surface in calm conditions. In dense bloom conditions there is hardly any connection between the bloom near or on the water surface satellites are detecting and the concentration of chlorophyll-a at the depth where flow-through systems takes water. As was mentioned above the bloom was not very intensive in the Gulf of Finland in 2005 and most probably the cyanobacteria were mixed in the water column and did not form surface accumulations. This may explain the correlation between satellite and flow-through data.

6 MODIS standard product Chlor-a,(mg/m3) R 2 = in situ measured chlorophyll-a, (mg/m3) Figure 3. Correlation between the chlorophyll-a values from sampling data and MODIS algorithm during the spring (diatom) bloom (14 th and 17 th of April 2006). Samples are collected from Tallinn Bay monitoring stations and from flow-trough system which is installed onboard co-operating passenger ferry traveling between Tallinn and Helsinki MODIS standard product Chlor-a,(mg/m3) R 2 = Flow-through system measured chl-a, (mg/m3) Figure 4. Correlation between flow-trough system measured and MODIS assessed chlorophyll-a during the summer (cyanobacteria) bloom. The flow-trough system is installed onboard co-operating passenger ferry traveling between Tallinn and Helsinki

7 4. CONCLUSIONS Results of this study show that MODIS standard chlorophyll product (OC3) is not suitable for the Baltic Sea conditions. Analyzing the spring and summer results separately we obtained very good correlation between satellite and in situ data in spring and the correlation was better than expected also in case of summer data. The results show that seasonal chlorophyll retrieval algorithms are needed and the correlation coefficients indicate that such algorithms can be developed. 5. LITERATURE 1. Darecki, M., Weeks, A., Sagan, S., Kowalczuk, P., Kaczmarek, S. Optical characteristics of two contrasting Case 2 waters and their influence on remote sensing algorithms. Continental Shelf Research. 23 (2003): Darecki, M. and Stramski, D. An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea. Remote Sensing of Environment. 89 (2004): Dekker, A.G., Malthus, T.J., Goddijn, L.M. Monitoring cyanobacteria in eutrophic waters using airborne imaging spectroscopy and multispectral remote sensing systems. In: Proceedings of Sixth Australasian Remote Sensing Conference vol. 1, Gordon, H. R., and Morel, A. Remote assessment of ocean color for interpretation of satellite visible imagery A review. In R. T. Barber, M. J. Bowman, C. N. K. Mooers, & B. Zetzschel (Eds.), Lecture notes on coastal and estuarine studies. New York: Springer-Verlag, Härmä, P., Vepsäläinen, J., Hannonen, T., Pyhälahti, T., Kämäri, J., Kallio, K., Eloheimo, K., Koponen, S. Detecton of water quality using simulated satellite data and semiempirical algorithms in Finland. The Science of the Total Environment. 268 (2001): Jupp, D.L.B., Kirk, J.T.O., Harris, G.P. Detection, identification and mapping of cyanobacteriaeusing remote sensing to measure the optical quality of turbid inland waters. Australian Journal of Marine Freshwater Research. 45 (1994): Kahru, M., Leppaänen, J.-M., Rud, O. Cyanobacterial blooms cause heating of the sea surface. Marine Ecology Progress Series. 101 (1993): Kahru, M. Using satellites to monitor large-scale environmental change in the Baltic Sea. In: Kahru, M., Brown, C.W. (Eds.), Monitoring Algal Blooms: New Techniques for Detecting Large-Scale Environmental Change. Springer-Verlag, Berlin, Kahru, M., Leppaänen, J.-M., Rud, O., Savchuk, O.P. Cyanobacteria blooms in the Gulf of Finland triggered by saltwater inflow into the Baltic Sea. Marine Ecology Progress Series. 207 (2000): Kutser, T. Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing. Limnology and Oceanography. 49 (2004): Leppänen, J.-M., Rantajärvi, E., Hällförs, S., Kruskopf, M., Laine, V. Unattended monitoring of potentially toxic phytoplankton species in the Baltic Sea in Journal of Plankton Research. 17 (1995): Morel, A., and Prieur, L. Analysis of variations in ocean colour. Limnology and Oceanography. 22 (1977):

8 13. Rantajärvi, E., Olsonen, R., Hällförs, S., Leppänen, J.-M., Raateoja, M. Effect of sampling frequency on detection of natural variability in phytoplankton: unattended highfrequency measurements on board ferries in the Baltic Sea. ICES Journal of Marine Science 55 (1998): Sathyendranath, S. (Ed.) Remote sensing of ocean colour in coastal, and other optically-complex, waters. IOCCG Report, vol. 3. Dartmouth, Nova Scotia: IOCCG Project Office, Report on the Functionality of the Ferrybox Systems Onboard of the Ferries. Description of the FerryBox Systems. In internet: _2_1 FerryBox_System_Description Revision_1-4_WWW.pdf ( ) 16. Ocean Color Web In internet: ( )