PERFORMANCE OF MERIS PRODUCTS IN LAKE VICTORIA Sørensen, Kai(1), Are Folkestad(1), Kerstin Stelzer(2), Carsten Brockmann(2), Roland Doerffer(3), Willy Okullo(4) and Leon Schouten (5). (1) Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, N-0349 Oslo, Norway. E-mail:
[email protected] and
[email protected] (2) Brockmann Consult, Max-Planck-Str. 2, 21502 Geesthacht, Germany. E-mail:
[email protected] and
[email protected] (3) GKSS Institute for Coastal Research, Max-Planck-Str. 1, 21502 Geesthacht, Germany. E-mail:
[email protected] (4) Willy Okullo, Makerere University Kampala, Physical Department, Uganda . E-mail:
[email protected] (5) Leon Schouten, Infram BV, Postbus 16, 8316 ZG Marknesse, Netherland. E-mail:
[email protected]
ABSTRACT The newly finished ESA project MERIS Lakes included activity in Lake Victoria to show the potensial for monitoring this eutrophic African Lake. In this study an evaluation of how different processors perform in Lake Victoria. Lack of proper validation data makes it only possible to have a preliminary assessment of the performance of the product algal_2, suspended material and the signal depth, Z90_max. The validation is based on the MERIS Level 2 Reduced Resolution data processed by using the Eutrophication processor developed under the ESA contract and the Case2Regional processor.
this lake, old data was used in an evaluation of the MERIS products performance. Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT was successfully launched on March 1st 2002. Already in 2003 and 2004 a study of optical properties of Murchison Bay, Uganda [2] was performed. A study [3] of the toxic cyanobacteria from the same bay was performed in 2000-2003, and both these dataset has been used in the study. The objective of the present study was to investigate the performance of the MERIS products from new processors developed by the ESA “MERIS Lake” and “Case 2 Regional” projects. The Total suspended material (TSM), Chlorophyll-a (algal_2) and the signal depth, (Z90_max) products were considered. 2 THE TEST AREA
The study showed that the choice of proper processor and their training of them are critical. It was also shown that the product Z90_max showed reasonable good correlation and quantification relative the in situ data. TSM and algal_2 had very few in situ data for validation. However, if the correct training of the processors can be performed with data following the validation protocols and us of correct conversion factor there is potensial to develop of satellite product for Lake Victoria.
1 INTRODUCTION The ESA Lake project “Development of MERIS lake water algorithms” [1] included validation activity in Finland, Spain and Germany. Data for training of a new neural net for Lakes was also collected, and two new processors were developed. An activity in African lakes was also included in the project, but since validation on new data collection was not possible in _________________________________________ Proc. of the '2nd MERIS / (A)ATSR User Workshop', Frascati, Italy 22–26 September 2008 (ESA SP-666, November 2008)
Lake Victoria covers an area of 68.000 km2 in an altitude of 1000 m between Tanzania, Uganda and Kenya (Fig.1). The lake has for a long time had eutrophication problems and severe blooms (Fig.2) of cyanobacteria (e.g. Anabena sp., Microcystis sp.) has been reported [3]. These blooms are reported to cause problems for fishery and drinking water. Another problem of the lake is the floating water plants (Hyacint) (Fig.3). For remote sensing both these factors are important to take into consideration in the validation. Also areas of river inflow or shallow areas where high sediment loads (Fig.4) can occur must be taking into account.
Figure 4. Photo of sediment re-suspended from a shallow area in Murchison Bay. Photo Kai Sørensen. Figure 1. Image of Lake Victoria seen from MERIS on the 28. August 2005. Some of the in situ stations used in the study are shown.
3 METHODS The investigation in 2003/2004 [1] was performed in accordance with validation protocols [4] and was also coordinated with the ESA VAMP project [5]. This investigation in Murchison Bay [2] collected water samples at the surface, at the ½ Secchi Disc Depth (SDD) and at the SDD. The water samples were filtered and prepared at the University in Kampala. Determinations of yellow substance (YS), bleached particle absorption (BPA), difference of total and bleached particle absorption (apig(442)), total suspended material (TSM) and chlorophyll-a (Chl-a_ hplc) were analysed at NIVA in Oslo, Norway [4]. The dataset that was provided by [3] from the study of cyanobacteria in Murchison Bay followed the methods decribed in [4] concerning the Chl-a analysis.
Figure 2. Photo of a cyanobacteria bloom in Murchison Bay. Photo Kai Sørensen.
Figure 3. Photo of a floating water plant in Murchison Bay. Photo Kai Sørensen.
Comparison of in situ data and satellite data in Murchison Bay was difficult due to the vicinity to land. More data from the open areas of the lake were therefore needed. The Lake Victoria Fisheries Organization (LVFO, www.lvfo.org) do monitoring surveys in the lake several times per year, and they kindly provided the dataset “Source of water quality parameter data on the entire Lake Victoria” for use in this study. The in situ parameters relevant for this study were SDD, and Chl-a fluorescence and turbidity measured by in situ sensors. All MERIS products including the product and scientific quality flags were extracted. The MERIS reflectance data was extracted as average of 3*3 pixels and compared with the in situ data. MERIS reflectance (PCD1_13) or no algal_2 (PCD_17) or TSM/yellow substance (PCD_16) flags were used.
The two processors Case2R (v1.3) and the EUL 1.0 patched with the latest available atmospheric neural network (EUL 1.0 AC NN3) were used in the study. Both included an atmospheric correction for the altitude of the Lake of 1000 m. The training of the NN in the Case2R and EUL processors was based on respectively marine data and Eutrophic Spanish lakes [1]. 4 RESULTS Before the in situ data collected from the Lake could be compared with the MERIS products an assessment was performed on the data to see if the in situ sensor data of Chl-a fluorescence and turbidity could be used as proxies for respectively Chl-a and TSM concentrations.
4.2 Assessment of the turbidity data Turbidity data from the LVFO 2005 dataset was measured with an in situ sensor. We assume that the turbidity expresses the phytoplankton concentration and that these quantities follow a relation to SDD as we normally find in other lakes. We compared (Fig.6) the relationship between turbidity and SDD from the LVFO dataset with data from Norwegian waters [6]. The comparison shows that the relationship from LVFO dataset deviates from the Norwegian dataset at low ( 10 FNU) turbidity values. This indicates that the sensor used in Lake Victoria may not be well calibrated. 100.00
Assuming that changes in SDD are due to changes in the phytoplankton concentration we made an assessment if the Chlorophyll-a fluorescence data could be used as an proxy for the Chl-a concentration. The two dataset from Murchison Bay (SDD and Chl-a HPLC) and the LVFO dataset (SDD and Chloropyll-a fluorescence) were compared. Another dataset from Norwegian Lakes including cyanobacteria lakes [6] were also used. Figure 5 shows the comparison between SDD and Chla from three datasets measured by different methods. The results show that both the Murchison Bay and the LVFO dataset falls within the variation seen from Norwegian lakes. Based on this comparison we will conclude that we can use the LVFO Chl-a fluorescence data as a proxy for Chl-a and phytoplankton concentration, and that we thereby can use these data in the evaluation of the MERIS products. I should be clearly stated that this comparison is not following the validation protocols.
Secchi Disc Depth (m)
4.1 Assessment of the Chl-a fluorescence data
Lake Victoria, LVFO 2005.
SSD, LVFO = 2.456*Turbidity
Norwegian Waters, Sørensen, 1993
SSD, Sørensen = 3.379*Turbidity
-0.4016 -0.6761 ,
2
R = 0.75 2
R = 0.82
10.00
1.00
0.10 0.10
1.00
10.00
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Turbidity (FNU)
Figure 6. Comparison of the relationship between Secchi Disc Depth and turbidity between the LVFO dataset and the Norwegian dataset [6]. However, taking these findings into account and given the fact that there were no other datasets available we decided to use the present in situ data in the assessment of the MERIS performance. The turbidity data was transferred to TSM using a conversion factor from Norwegian Lakes (Eq.1, Sørensen, unpubl.). TSM = 1.45 * Turbidity
(1)
4.3 MERIS data quality control 100 Norwegian Lakes (Sørensen, Unpub., and Rhorlack, unpub.)
An RGB image from Itoma Bay in Lake Victoria (Fig. 7) illustrates the occurrence of cyanobacteria blooms in the area. The patchiness of the bloom is clearly visible, and illustrates the problem with doing comparison between in situ data an MERIS data in this area. The top of atmosphere (TOA) radiance spectra were extracted (Fig.8) at the four pin locations in Fig. 7.
Lake Victoria, Murchison Bay, Haande 2008 and Okullo 2005
Secchi Disc Depth (m)
Lake Victoria, LVFO 2003-2007
10
1
0.1 0.1
1
10
100 3
Chl-a_HPLC or Chl-a_Fluorescence (mg/m )
Figure 5. Comparison of Secchi Disc Depth and Chla_HPLC or Chl-a fluorescence data for different dataset.
0.01 St.4 12.08.05 St.21 21.08.05 St.26 24.08.05 St.29 25.08.05 St.34 28.08.05
0.009 0.008
St.35 28.08.05 St.44 03.09.05 St.45 03.09.05
Reflectance
0.007 0.006 0.005 0.004 0.003 0.002 0.001 0 400
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Figure 7. RGB image from Itoma Bay with some selected pins. In Fig.8 upper right the strong infrared absorption is seen (PIN 7) caused either by land or floating water plants, while PIN 6 and 8 have some increase in the near infrared probably connected to the cyanobacteria bloom. Only PIN 9 was not influenced by these effects.
Figure 9. MERIS water-leaving reflectance (Rho_w) from selected stations in Lake Victoria 2005. Data are were from the ESA lake project [1] and processed using the Case2R v1.3 processor. 4.4 Comparison of Z90_max and Secchi Disc Depth One of the products from the EUL and Case2R processors is the signal depth (Z90_max) which is related to the Secchi Disc Depth. Investigations in marine areas of the Skagerrak (E. Aas, pers.comm.) have found that the Z90_max correspond in average to the Secchi Disc Depth. In Fig.10 such an comparison are done for the Lake Victoria dataset from 2005, showing a linear and good relation to the Secchi Disc Depth of the selected stations (Fig.9). Secchi Disc Depth (m) 0.00 0
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
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-0.5 -1 Z90_max EUL= -0.981*SSD+0.469 R2 = 0.93
Figure 8. Top of the atmosphere (TOA) radiance spectra extracted from the image Fig.7, for pin 6 (upper left), pin 7 (upper right), pin 8 (lower left) and pin 9 (lower right).
Z90_max (m)
-1.5 -2 -2.5 -3 -3.5 -4 -4.5 -5
After removing suspected spectra using the flagging criteria as specified above and taking into account station close to the coast we could make the comparison with the MERIS data. In Fig.9 some examples of MERIS reflectance are shown from the dataset in 2005 [1].
Figure 10. Comparison of the Secchi Disc Depth and MERIS Z90_max in Lake Victoria based on the Case2R (v1.3) processor. Data are from 2005. A comparison of MERIS Z90_max (EUL 1.0 AC NN3) and in situ SDD was done based on data from 20022007 (Fig. 11). This processor was assumed to better since it was trained on the eutrophic Spanish lakes. The The figure show that the MERIS Z90_max was well
correlated with SDD. However, this comparison showed another relationship between the two parameters than found when using the Case2R processor (Fig.10). Assuming that Z90_max should correspond better to the SDD the Case2R processor has better fit to the in situ data. This illustrates the importance of using the correct training dataset.
Chl2.hplc=21 x apig(442)1.04
(2)
For Murchison Bay in Lake Victoria this relation has been determined on a limited number of data [2] to follow the relation in Eq.3 Chl2.hplc=46.47 x apig(442)1.0558
0
1
Secchi Disc Dept (m) 3 4
2
5
6
7
0
-1
Z9 0_m ax (m )
-2
-3
-4
(3)
This is approximately a factor 2 higher than the one in the processor. If we assume that Eq. 3 is true for the whole lake, the algal_2 values from the processor would become accordingly higher. This would increase the difference between MERIS and in situ Chl-a as seen in Fig. 12. 4.6 Comparison of total suspended material
Z90_Max = -0.42* SDD - 0.81 2 R = 0.70 -5
Previous investigations in Lake Victoria [2] have shown that the conversion from bp(442) to TSM follows the Eq. 4
-6
-7
TSM = 1.02 * bp(442) Figure 11. Comparison of the Secchi Disc Depth and MERIS Z90_max based on the EUL (v1.0+AC NN3) processor. Data from Lake Victoria in 2002-2007.
(4)
The conversion factor in Eq. 4 is lower than the conversion factor used in the present processors (EUL and Case2R) where Eq.5 is used.
4.5 Comparison of Chlorophyll-a TSM = 1.73 x bp(442) A comparison between in situ Chl-a fluorescence data and MERIS Chl-a using the Case2R (v1.3) processor was performed on the data from 2005 (Fig.12). The data are very few and some in situ values were estimated from SDD. However, the few data showed that the algal_2 product was in the same order of magnitude as the in situ Chl-a data. 30 Red dots are Chl-a in situ values Black dots are calulated from Secci Disc Depth
28 26 24
Chl_conc = 1.602* Chl-a_Fl - 3.13 R2 = 0.92
20 18
A small test on the agreement between MERIS TSM and in situ turbidity were done (Fig. 13). The turbidity was transferred to TSM using Eq.1 based on data from Norwegian Lakes. The conversion factor in both Eq.4 and Eq. 5 were applied on the MERIS data. There are too few data to draw any conclusions, but the results show that the MERIS data are in the same order of magnitude as the converted turbidity data, and that using the conversion in Eq. 4 instead of Eq.5 lowered the MERIS TSM to become closer to the in situ data. Some coastal station are included in this test.
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Chl-a (mg/m ), Measured as Chl-a fluorescence
Figure12. Comparison of in situ Chl-a versus algal-2 using the Case2R (v1.3) processor on data from Lake Victoria 2005.
TSM (g/m3) MERIS (EUT 1.0 +AC NN3)
3
C h l_ C o n c E U L m
22
(5)
TSM EUL 1.0 +AC NN3 Conversion factor 1.73 TSM EUT 1.0 +AC NN3 Conversion factor 1.02 (Ref [2])
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TSM (g/m3), converted from turbidity LVFO
In the Case2R processor the conversion factor between the optical quantity apig(442) and chlorophyll-a (Chl2) is based on the Eq.2
Figure 9. Comparison of TSM based on the EUL 1.0 NN3 processors and the TSM data transferred from turbidity.
5 DISCUSSION AND CONCLUSIONS A proper validation of the MERIS Level 2 products in Lake Victoria could not be performed due to lack of in situ data sampled by methods in accordance with the standard MERIS validation protocol. Murchison Bay was the only area for which data sampling followed the validation protocol, but due to the close vicinity to land, the quality of the MERIS data in this bay was insufficient for validation. However, the use of available measurements of Secchi Disc Depth, turbidity and Chl-a fluorescene from the open waters made a preliminary evaluation of the MERIS performance possible. The comparisons of MERIS Chl-a and TSM products with relevant in situ parameters indicated that the MERIS products are, depended of the processor used, within an acceptable range. However, the data for comparison were too few to make strong statements on how the MERIS data work in the area. The investigation also showed that the choice of the correct processor (e.g. Case2Regional or the Eutrophic lake processor) and the version of them or use of different atmospheric net are important and significant changes the results. For the time being one can not recommend any of the tested processor for Lake Victoria. Furthermore, to properly validate the MERIS L2 products and the various existing processors, in situ measurements of optical and geophysical parameters following the MERIS validation protocol are needed. Future investigations should also focus on the inherent optical properties (IOPs) to further evaluate the relationship between apig(442) and Chl-a and between bp(442) and TSM for the entire lake. The present study also showed that the Z90_max product is very well correlated with the Secchi Disc Depth. The Z90_max product is not a standard MERIS product, but the finding is promising with regards to the ability of monitoring water quality in Lake Victoria using the MERIS sensor.
ACKNOWLEDGEMENTS Thanks to Barbro Silde and Merete Grung for the HPLC measurements. Thanks to Sigrid Haande and Thomas Rohrlack at NIVA who provided data and helped in discussion about Lake Victoria. The activity was supported by the ESA MERIS Lakes project “Development of MERIS lake water algorithms. ESA contract No. 20436/06/I-LG, and the Validation project “VAMP” funded by ESA, the Norwegian Space Centre and NIVA (PRODEX contract no. 14849/00/NL/Sfe(IC)). Also the ESA
project “MERIS Case2 Water algorithms Development” and the Norwegian Space Centre project “LakeColour” have supported the project.
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