UNISEASONAL DYNAMICS OF MAIN WATER QUALITY PARAMETERS IN VISTULA LAGOON EXTRACTED FROM ENVISAT/MERIS GEOPHYSICAL PRODUCT AND IN SITU MEASUREMENTS Marek Kruk (1), Marek Mróz (1), Lidia Nawrocka (2), Katarzyna Parszuto(1), Magdalena Mleczko (1), University of Warmia and Mazury, Oczapowskiego 2, 10-957 Olsztyn, Poland, Email: [email protected], [email protected] (2) Higher School for Vocational Education, Wojska Polskiego 1, 82-300 Elbląg, Email:[email protected] (1)

ABSTRACT The comparison between water quality parameters extracted from MERIS standard geophysical product and in situ measurement is the main topic of the proposed paper. Namely, uniseasonal dynamic of 1. Algal_pigment_index_2 (Chl-a), 2. Total Suspended Matter (TSM) and 3. Yellow substance absorption (CDOM/ODOC) distribution extracted from several MERIS images acquired in the period AprilSeptember 2009 is confronted with field campaign results at the similar dates. The study was conducted on Vistula Lagoon (South Baltic Sea), shallow estuary characterized by intensive algae bloom and sediment mobilization by waves. The comparison of multitemporal values extracted from satellite data itself is provided for one profile along the lagoon and 6 transversal sections. The analysis of the factors responsible for the differences between in situ and satellite measurements is included in the paper. 1. INTRODUCTION The usefulness of satellite remote sensing for monitoring of water quality of aquatic coastal ecosystems is widely accepted as promising technique for sustainable management purposes (Edwards 1999, Phinn et al. 2006). The development of satellite sensor resolution opens possibilities for application of remote sensing for monitoring of coastal water bodies of specific character as shallow estuaries. In this paper, MERIS satellite optical products as chlorophyll a (chl a), Suspended Total Matter (STM) and Colored Dissolved Organic Matter (CDOM) expressed as Dissolved Organic Carbon (DOC) (Mannino et al. 2008) will be compared with in situ measurements of these parameters. The main question of the study is to verify if, and in what extend, MERIS products can be complementary used instead of field results of above parameters in the Vistula Lagoon (N Poland/Russia), characterized by hypertrophic, brackish waters (Pliński 2004). The purpose of the article is also to present dynamics of water quality parameters in profile along the lagoon, its spatial and temporal variability and to analyze the factors responsible for the differences between in situ _____________________________________________________ Proc. ‘Hyperspectral 2010 Workshop’, Frascati, Italy, 17–19 March 2010 (ESA SP-683, May 2010)

measurement results and satellite records. An important issue of the study is to find a range of usefulness and limitations of MERIS measurements. It can also help for improvements and modifications of remote sensing technique and parameter algorithms in the perspective of its applications for monitoring highly eutrophic coastal waters. 2. METHODS 2.1. Study area The study area is located on the Polish part of Vistula Lagoon (828 km2 area, 2.6 m mean depth), the estuary of Baltic Sea (Fig. 1). The hydrological regime of Vistula Lagoon is dependent on the inflow of sea waters. Even 80% of water is coming from Baltic Sea by Pilawa Strait. The major phenomenon causing permanent disturbance of ecosystem balance is an excessive primary productivity of phytoplankton, particularly regular harmful algae blooms (Plinski 2004). The main sources of nutrients are originating from unmanaged properly water treatment in the catchment area and from the resuspension from bottom sediment. The trophic indices are characteristic for the state of strong eutrophy and even hypertrophy. The main consequence of the state described above is a significant impoverishment of biodiversity and a lowering of the production other, except phytoplankton, ecological associations including fishes and macrophytes. More, Cyanophytes and bacteria, as well as trace metals and organic pollutants coming into Lagoon from the sludge, cause severe toxic effects, which practically eliminate this water body from effective fisheries, recreation and water sports activities. 2.2. Field sampling The field sampling and measurement points were located in 5 constant stands (approx. 0.3 km2) along SW–NE profile of the lagoon (Fig. 1) and in 10 additional points which location varied in subsequent dates of sampling. Field campaigns were conducted once a month in the period from April till September 2009 year. Water sampling was done from surface

layer of the water (0 – 0.25 cm) by taking samples from the boat, since about 11:00 AM (point 1) until 5:00–6:00 PM (point 5). Water was collected to three separate HVDP bottles for Chl-a, TSM and DOC determination.

Fig.1. MERIS FRS Level 1 image acquired on 2009/04/24 (7,5,2, bands as RGB) merged with Landsat5 TM image (3,2,1, bands as RGB components).

2.3. Samples processing and analysis. Samples were transported to the laboratory and after 5-6 hours from leaving the boat were subject of analytical procedures. Chlorophyll-a was determined by acetylene method and calculated using Lorenzen (1967) equation. Total Suspended Matter was defined using weight method preceded by filtration through GF/C Whatman filter and drying in 105°C. Dissolved Organic Carbon was determined using spectrometer TOC 5000 produced by Shimadzu, Japan, according to procedure from Standard Methods...(1996). 2.4. MERIS data acquisitions and water parameters extraction. MERIS data was provided free of charge by the European Space Agency under the Cat.1 Project. Products: MER_FR_1P (Full Resolution Level 1 with spatial resolution of 290x260m, and MER_FR_2P (Full Resolution Geophysical Product Level 2) were made available by ESA on the Internet via Near Real Time processing service. About 30 images acquired since April to October 2009 were downloaded and carefully analyzed. Fourteen of them (tab.1) were classified as almost clouds free and ortho-corrected (level 1) or re-projected (level 2) to UTM 34 cartographic frame. Level 1 datasets have been used for color compositions elaboration and Level 2 standard products have been sources of water quality parameters. The algorithms used in geophysical product preparation, including the atmospheric correction for case 1 waters and its extension to case 2S are based on the MERIS Advanced Theoretical Basis Documents available from ESA Web site.

(http://envisat.esa.int/instruments/meris/pdf/. (Doerffer, Schiller 1997). For waters processing - Inverse modeling technique (IMT) uses an Inverse Radiative Transfer ModelNeural Network (IRTM-NN) to estimate the concentration of algal pigment index 2 (Chl2), yellow substance absorption (ODOC), and suspended particulate matter (SPM) concentration for Case 1 and Case 2 waters from normalized water-leaving reflectance at MERIS bands b442 to b665, b705, #s, #v and +-. In this approach, Case 1 waters are treated as a special case of the Case 2 water algorithm. The IMT considers the complex nature of the water leaving reflectance. The data output from this algorithm are Chl2 concentration, ODOC absorption, TSM concentration and a confidence flag. The second MERIS algal pigment index is a measurement of the concentration in Log10(mg/m3) of phytoplankton (algae) in the water. The MERIS suspended matter products is a measurement of the suspended sediments concentration in Log10(g/m3). The model describes suspended matter as a scattering particle with very little absorption for which a more appropriate name would be "total suspended mineralic matter concentration. The MERIS yellow substance product is a measurement of the Gelbstoff absorption in m-1. The model describes yellow substance as non-scattering absorbing matter. Yellow substance or "Gelbstoff" is decayed organic material that has been dissolved in the marine waters and is usually transported into the sea by rivers. The composition of water constituents defines the scope of the algorithms. For turbid estuarine water it comprises about the following concentration range: phytoplankton pigment (chl. a): 1 - 50 Pg/l gelbstoff absorption at (440): 0.1 - 2 m-1 mineralic suspended matter: 1 - 50 mg/l. The processing of MERIS pixels classified as “water pixels” provides 8 quantitative and 5 qualitative products as well as flags relevant to the quality of all products (MERIS Product handbook, 2006). Three main water parameters have been extracted from MERIS Level 2 standard quantitative products in native measurements units and converted to: Algal Pigment Index 2 (Chl-a) in [mg/m3], TSM – Total Suspended Matter in [g/m3] and Yellow Substance Absorption (or ODOC) coefficient remaining in [1/m] absorption unit. According to MERIS product handbook the accuracy of Chl-a retrieved value is estimated better than 15%, yellow substances better than 30% and suspended matter better than 15%. As Vistula Lagoon is classified as Case 2 waters and observed as turbid water reservoir the masks of “Sediment dominated Case_2 water” have been also extracted from Level 2 Product for each acquisition.

3. RESULTS AND DISCUSSION 3.1. Temporal dynamics of in parameters.

situ

water

140 120 100 80

3 60 mg/m 40 20 0

1 2 3

Fig2. Example of “Sediment dominated Case_2 water” mask. The masks of uncertain pixel values for these 3 quantitative parameters have been also extracted from metadata records.

4 5

month s

6

Serie5 Serie4 Serie3 Serie2 Serie1

stands

Fig 4. Temporal dynamic of chl a in 5 stands (S1-S5) in six month season (April – September 2009) in the Vistula Lagoon central basin.

140

120

Fig3. Examples of “uncertain pixel values” mask.

100 80 60 mg/l 40 1

CRUISE DATE

20 2

MERIS ACQUISITION DATE

0

3 4

2009_04_23 2009_05_26 2009_06_15 2009_07_21 2009_08_12 2009_09_23

2009_04_24 2009_05_13 2009_05_14 2009_05_26 2009_06_04 2009_06_11 2009_06_15 2009_07_16 2009_07_29 2009_08_07 2009_08_08 2009_08_21 2009_09_01 2009_09_20

Tab.1 Multitemporal MERIS dataset- dates of image acquisitions and cruises.

5

month s

6

Serie5 Serie4 Serie3 stands Serie2 Serie1

Figure 5. Temporal dynamic of TSM in 5 stands (S1S5) in six month season (April – September 2009) in the Vistula Lagoon central basin.

10 9 8 7 6 5 4 3 2 1 0

mg/l 1 2

3

For each acquisition whole lagoon surface was classified as turbid water. This flag was changing for Gulf of Gdansk area showing variable spatial extent of this mask on successive acquisitions. The uncertainty masks of Chl-a, TSM and CDOM were strongly varying on Vistula Lagoon surface for successive acquisitions, ranging from 10 to about 100% of lagoon’s area.

4

mont hs

5

Serie4

6

stands Serie1

Fig 6. Temporal dynamic of DOC in 5 stands (S1-S5) in six month season (April – September 2009) in the Vistula Lagoon central basin.

Chlorophyll-a concentration varied significantly in subsequent months of the study. Generally, the lowest values were recorded in spring months, April and May, but this tendency was continued till July in the most east-northern stands 1 and 2. The maximum chla concentration in June (140 mg/m3) on the stand 3 and high concentration (100 mg/m3), approximately equal on all stands in August, indicates algae blooms (Fig. 4). The algae blooms in summer are characteristic feature of the productivity cycle in the Vistula Lagoon, due to maximum anthropogenic eutrophication and elevated water temperature (Rybicka 2005). The highest concentrations of Suspended Total Matter in Vistula Lagoon were recorded in spring months, May and June and in the September (> 100 mg/l). The lowest values of TSM were noticed in April and August, but only in stands 1 and 2 (20-60 mg/l) (Fig. 5). The seasonal changes in TSM concentration are dependent from two factors : phytoplankton concentration and wind action moving up semi-liquid sediment (Różańska & Więcławski 1978). The concentration of Dissolved Organic Carbon was of approximately equal level on the Vistula Lagoon during period of study. It differs in the range between 6 and 9 mg DOC/l and shows a tendency to achieve higher values in May and, after slight lowering in June, again elevated level in July and August (Fig. 6). It has clear relation with the cycle of phytoplankton biomass seasonal and successional changes (Rybicka 2005). 3.2. Spatial variability of in situ water parameters The five stands of sampling located along NE - SW profile of Vistula Lagoon (Fig.1) do not differ significantly in distribution of parameters measured. There is however visible tendency to divide sampling points for two groups : first consists stands 1-3 which characterizing by lower TSM and DOC values than stands 4 and 5. It can be explain by the composition of sediment, i.e. domination of semi-liquid organic matter in west part of the lagoon (Geochemical Atlas of Vistula Lagoon 1997). The differences in distribution of chl a amongs five sampling points can be probable explain by relation between areas of intensive algae bloom and hydrodynamic and wind forces spatial pattern of the lagoon.

Stand

Chl a

TSM

DOC

mg/m3

mg/l

mg/l

Mean

SD

mean

SD

mean

SD

1

32.9

21.4

76.2

28.0

7.61

0.66

2

43.4

22.1

74.7

31.1

7.44

0.64

3

66.2

34.5

90.7

35.5

7.47

0.82

4

42.2

21.4

97.9

23.8

7.83

0.47

5

41.1

29.3

93.8

25.2

8.41

0.80

F

1.342

0.7934

2.025

p

0.28

0.54

0.12

Table 2. ANOVA of water parameters in 5 stands along Vistula Lagoon NE – SW profile (Fig. 1). SD – standard deviation of mean, F – Fisher test results, p – probability to reject null hipothesis.

3.3. Temporal dynamics and spatial variability of water parameters extracted from standard MERIS data. Spatio-temporal characteristics of water parameters: Chl-a, TSM and CDOM extracted from MERIS standard products were analyzed on one longitudinal and six transversal sections (profiles) going from NW to SE direction (see fig.1.). The profile no.6 has been drown through Pilawa Strait permitting observations of inflow and outflow of water from and to Gulf of Gdansk. The profiles permitted to extract of above mentioned parameters values both for Vistula Lagoon and for Gulf of Gdansk as a reference. In the paper only three transect profiles are placed as the examples. One of major observations is that the Chl-a values are never higher than 40 mg/m3. It seems that any saturation appears in these satellite “measurements”. For spring acquisition (2009-04-24) chlorophyll-a values are in range of 20-40 mg/m3 and for later acquisitions are in range of 30-40 mg/m3. It is also visible that a very slight gradient of values appears from NW to SE direction on the Polish side, resulting probably from predominant wind directions. It is not clearly observable on the Russian side because, it seems, of Pregola river input. The values of TSM and CDOM are not characterized by any well-defined spatial pattern. They are ranging from 10 to 25 g/m3 for spring observations and from 10 to 20 g/m3 for later observations. Fig.10 shows a very large span between the lowest and the highest values of CDOM. Extremely high values for this parameter are

observed comparing with those managed by MERIS algorithms in standard products processing.

Fig.8. Multitemporal (3 days) set of transversal profiles shoving chl-a spatial variability.

Fig.7.Multitemporal set of longitudinal sections showing spatio-temporal variability of Chl-a, TSM and CDOM values.

Fig.10. Multitemporal (3 days) set of transversal profiles shoving CDOM spatial variability. Fig.9. Multitemporal (3 days) set of transversal profiles shoving TSM spatial variability.

3.4. In situ data versus MERIS extracted data 3.4.1. Chlorophyll a Chlorophyll-a measured from field sampling in 5 constant points and in all six months of the study does not correlate significantly with MERIS chl a values extracted from the same areas (0.3 km2) (Fig. 11A). More detailed analysis showed, however, that there are periods as April, when optical sattellite records are significantly (p < 0.01) correlated with in situ measurements (Fig. 11B). This suggests that in situ and MERIS data are close each other in lower ranges of chl a concentrations, not exceeded 40 mg/m3, the maximum value of extraction. This low level is observed in Vistula Lagoon only during early spring

chl a MERIS (mg/m³)

A

y = 0.0572x + 26.095 R² = 0.0218 p > 0.05 n = 30

50.00 40.00 30.00 20.00 10.00 0.00 0.00

50.00

100.00

150.00

chl a in situ (mg/m³)

y = 0.4019x + 18.827 R² = 0.3389 p < 0.01 n = 15

50.00 40.00 30.00 20.00 10.00 0.00 0.00

20.00

40.00

chl a in situ (mg/m³)

Fig.11. Regression equations and determination coefficients showing relationship between chl a measured in situ with data extracted from MERIS. A – data from all campaigns (5 stands), B- data from April campaign (5 stands + 10 additional points) A

A

60.00

chl a MERIS (mg/m³)

chl a MERIS (mg/m³)

B

months, as April. For other months elevated values of chl a, related to algae bloom were observed. Total Suspended Matter analyzed from field sampling in 5 constant stands and in all six months of the study, as in the case of chl a, does not correlate significantly with MERIS TSM values extracted from the same stands (Fig. 12A). Also, parallelly to chl a, analysis limited to observation in one date (April), show significant (p < 0.01) relationship between field TSM data and these extracted from satellite (Fig. 12B). The explanation of this phenomena is probably the same, as in the case of chl a. MERIS extraction seems to be more relevant for substitution of field TSM measurements in its lower ranges observed on Vistula Lagoon in early spring with low biological production.

40.00 30.00 20.00 10.00 0.00 0.00

50.00

B

60.0 40.0 20.0 0.0 0.0

50.0

100.0

150.0

TSM MERIS (mg/l)

TSM in situ (mg/l)

25.0

y = 0.1685x + 14.85

20.0

2

R = 0.3708 p > 0.05 n = 30

15.0 10.0 5.0 0.0 0.0

20.0

40.0

60.0

150.00

y = 0.4019x + 18.827 R² = 0.3389 p < 0.01 n = 15

50.00 40.00 30.00 20.00 10.00 0.00 0.00

20.00

40.00

60.00

chl a in situ (mg/m³)

B

30.0

chl a MERIS (mg/m³)

TSM MERIS (mg/l)

2

R = 0.0034 p < 0.01 n = 15

100.00

chl a in situ (mg/m³)

y = -0.0278x + 16.819

80.0

y = 0.0572x + 26.095 R² = 0.0218 p > 0.05 n = 30

50.00

80.0

100.0

TSM in situ (mg/l)

Fig.12. Regression equations and determination coefficients showing relationship between TSM measured in situ with data extracted from MERIS. A – data from all campaigns (5 stands), B- data from April campaign (5 stands + 10 additional points)

Fig.13. Regression equations and determination coefficients showing relationship between DOC measured in situ with CDOM data extracted from MERIS. A – data from all campaigns (5 stands), Bdata from September campaign (5 stands + 10 additional points) Colored Dissolved Organic Matter extracted from MERIS satellite does not correlate significantly with DOC in situ data (Fig. 13A). However, as in the case of previous water parameters, detailed analysis of correlation between satellite CDOM and field DOC showed, that in particular date i. e. September, the correlation can be very high and significant (p < 0.01) (Fig. 13B).

4. CONCLUSIONS 1. Temporal changes of in situ measured chl a, TSM and DOC in shallow, brackish waters of the Vistula Lagoon can be characterized by combined effect of seasonal phytoplankton dynamic, including algae blooms, and irregular water mixing by wind and hydrodynamic factors. 2. There are no significant differences in the transect spatial variability of in situ measured chl a, TSM and DOC parameters. 3. The correlations between in situ and MERIS chl a, TSM and DOC/CDOM data recorded from the whole season were weak and not significant. Particularly MERIS chl a measurement is mostly under the range of variability of chl a in situ data. 4. Significant correlations between in situ and MERIS data can be find only in the selected months with lower level of parameters measured, as it was showed for April chl a and TSM data and for September DOC/CDOM data. 5. Due to lower range of MERIS recording in case of chl a and mostly weak correlations between field and satellite data of TSM and DOC/CDOM, it could be concluded that usefulness of MERIS data for monitoring of shallow estuary with highly eutrophic brackish waters, as Vistula Lagoon, is limited and can consider only for selected periods of the season. 6. General analysis of the MERIS data shows that reliable determination of these parameters for Vistula Lagoon encounter major problems resulting from their extremely high values and mutual dependencies. 5. REFERENCES 1.

Doerffer, R., Schiller H. (1997). Pigment index, sediment and gelbstoff retrieval from directional water leaving radiance reflectances using inverse modelling technique. Doc. No. PO-TN-MEL-GS-0005 Name: ATBD: 2.12. 2. Edwards, A.J. (Editor) (1999). Application of satellite and airborne image data to coastal management. Coastal Regions and Small Islands Papers, 4, UNESCO, Paris, 185 pp. 3. European Space Agency – MERIS Product Handbook, Issue 2.1, 24th October 2006. 4. Geochemical Atlas of Vistula Lagoon, (1996), State Geological Institute, Warsaw (in Polish) 5. Lorenzen, C.J. (1967). Determination of chlorophyll and pheopigments: spectrophotometric equations. Limnology and Oceanography, 12: 343–346. 6. Mannino, A., Russ, M.E., Hooker, S.B. (2008).Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight. Journal of Geophysical Research 113 ,C07051, doi:10.1029/2007JC004493

7. Phinn, S., Joyce, K., Scarth, P. & Roelfsema, C. (2006). The role of integretated information aquisition and management in the analysis of coastal ecosystem change. In : Remote Sensing of Aquatic Coastal Ecosystem Processes. Science and Management Applications (Eds. L.L. Richardson & E.F. LeDrew), Springer, Netherlands, 326 pp. 8. Pliński, M. (2004). The Hydrobiological characteristic of the Polish part of Vistula Lagoon : a review. Oceanological and Hydrobiological Studies 34, 287-294. 9. Różańska, Z., Więcławski, F. (1978). Badania czynników środowiskowych Zalewu Wiślanego w warunkach antropopresji (Investigations of the environmental factors of the Vistula Lagoon under anthropogenic pressure). Studia i Materiały Oceanologiczne, Biologia Morza, KBM PAN Sopot, 21(4):9-36 10. Rybicka, D. (2005). Potentially toxic blue-green algae (Cyanoprokaryota) in the Vistula Lagoon. Oceanological and Hydrobiological Studies 34, 161-176. 11. Standard Methods for Examination of Water and Wastewater, (1996), APHA, Washington.

ACKNOWLEDGMENTS This work has been carried out in the framework of the VISLA project financed by the Polish-Norwegian Research Fund. Satellite data was provided by the European Space Agency.