TESTING AVAILABLE MERIS IMAGE PROCESSORS FOR LAKES.

Transcription

1 TESTING AVAILABLE MERIS IMAGE PROCESSORS FOR LAKES. Krista Alikas (1), Ilmar Ansko (1), Anu Reinart (1), Evi Lill (2), Kristi Valdmets (1) (1) Tartu Observatory, Tõravere, Tartu County, 61 62, Estonia; (2) Võrstjärve Limnoogical Centre, Estonian University of Life Science, Rannu, Tartu County, Estonia; ABSTRACT Various processing algorithms for MERIS data have been developed for monitoring optically complex waters. In lakes and coastal zones, optical properties of water are influenced by bloom of various phytoplankton species, total suspended matter originating from the shallow bottom or shores, and high amount of coloured dissolved organic matter carried into water by rivers. In this study, data from large North European lakes (L. Peipsi, L. Vänern, L. Vättern) were compared with MERIS L2 standard products and results by various case-2 water processors: Case-2 Regional Processor, Eutrophic Lakes Processor and Boreal Lakes Processor. Processing was applied over images of optically complex multicomponental inland waters where from MERIS L1b TOA radiances optical properties and concentrations of water constituents were derived. Additionally, ICOL (Improved Contrast between Land and Ocean) processor was used to correct the adjacency effect. 1. INTRODUCTION Remote sensing methods have proved their high value and capacity for monitoring global oceans and seas, which optical properties are determined by phytoplankton and its degradation products [1]. With its specific capacity to obtain information almost simultaneously over large geographic areas, observations from satellites are useful for studying global processes also in coastal waters and large lakes. However, successful application of global remote sensing algorithms for determination optically active substances (OAS) in multicomponental coastal and inland waters on local scale is limited [2]. New methods including full spectral-fit technique are appropriate for retrieval absorption and scattering properties but these parameters are not of interest to water monitoring authorities. Currently there is increasing need for better, quicker and unified monitoring of waters because of: (i) change of the focus of the studies from ecosystem functioning towards how the functioning will change in changing conditions; (ii) new management models based Proc. of the '2nd MERIS / (A)ATSR User Workshop', Frascati, Italy September 28 (ESA SP-666, November 28) more on hydrological borders and watersheds as opposed to traditional administrative borders; (iii) the European Water Framework Directive (WFD) [3] requires the type-specific ecological status of water bodies to be determined by 215. In lakes eutrophication can be considered the most important and most universal reason of water quality degradation [4]. Physically large lakes exhibit several similarities to seas and oceans in their thermal structure and circulation dynamics. From the chemical point of view, lakes are important accumulation sites for substances transported from the watershed or built up in the lake itself but they may contribute positively to global greenhouse gas emission. Large lakes offer a wide range of ecosystem services to society, the multiple use which creates multiple pressures on these water bodies such as nutrient load and toxic pollution, modification of hydrology and shore line structure, and shifting of the food web balance by stocking or harvesting various species. Although large lakes are among the best-studied ecosystems in the world, the application to them of environmental regulations such as the European WFD is a challenging task and requires that several natural and management aspects specific to these water bodies are adequately considered [5]. Consistently implemented long-term programmes for monitoring large lakes are critical for detecting changes in water quality. They are required to assess the effects of past and ongoing management programmes and continuing anthropogenic influences, particularly the effects related to climate change. However, conventional sampling methods cannot produce enough data about spatial and temporal heterogeneity in large lake systems. MERIS (Medium Resolution Imaging Spectrometer) is an instrument on board the ENVISAT satellite put into orbit by the European Space Agency on 1 March 22 ( This instrument specifically addresses the needs of optically complex waters. Coastal and inland waters generally have a higher trophic status, which is associated with

2 optically active substances (dissolved substances and suspended particles) originating from their drainage basins in amounts that exceed their concentrations in the open ocean many times [6]. Four main problems have reduced the utility of satellite monitoring of freshwaters in high latitudes: (1) interference of coloured dissolved organic matter (CDOM) in the estimation of chlorophyll concentration; (2) effect of the neighboring land pixels to the signal originating from water; (3) various and unknown optical properties of coastal aerosols; (4) infrequent satellite measurements and relatively frequent cloud cover. The present paper investigates the possibility of using the MERIS images to monitor water quality in European Union s three largest lakes: Lake Peipsi in Estonia Russia, and Lakes Vänern and Vättern in Sweden. As the results by standard processing have been promising for these lakes [7], we have validated results from new reprocessing but also processed by several user-driven algorithms that are publicly available [8]. We compare the MERIS atmospherically corrected reflectance products and water quality products: chlorophyll a (Chl a), suspended matter (TSM), absorption by dissolved organic matter (a CDOM ) against measurements obtained by long-term monitoring programmes in these lakes. 2. MATERIALS AND METHOD 2.1. Description of lakes The three studied lakes are located in North Europe (59-61 N, Fig. 1). Latitude, N (deg) Latitude, N (deg) Vt2 Vn5 Longitude, E (deg) L.Vätt ern L.Vänern Vt1 Vn Longitude, E (deg) Vn1 Vn3 Vn4 SW EDEN Baltic Sea FIN LAND E STONIA RUSSIA Figure 1. Locations of lakes in Europe. Regular monitoring sampling points in lakes are indicated by circles and labelled. In L. Peipsi additional field campaign in 24 was carried out (crosses). Latitude, N (deg) P2 P38 P16 L. Peipsi P17 P4 P Longitude, E (deg) Despite similar location the lakes, their optical properties vary a lot between the waterbodies. The measured water transparency by Secchi depth is the highest in L. Vättern, m, lower in L. Vänern ( m) and the lowest in L. Peipsi ( m). Lake Peipsi on the border of Estonia and Russia is a large shallow lake (mean depth 7 m and area 3555 km 2 ) [9]. The high nutrient load, input by two large rivers and through resuspension from bottom, has caused a strong eutrophication (mean Chl 2.7±17.5 mg m -3 ). There exists North-South gradient inside the lake, where in the north part has the lowest OAS concentrations and in south the highest. The water colour is strongly affected by high CDOM (mean a CDOM (443), 2.6±1. m -1 ). Lake Vänern (mean depth 27 m and area 5648 km 2 ) in the central Sweden is separated by shallow archipelago area into two basins. Its water quality is classified as moderately nutrient rich and measurements of algal biomass characterize meso-oligotrophy (mean Chl 3.6±1.9 mg m -3 ) [1]. Lake Vättern constitutes only one rather narrow basin (width less than 15 km with area 1856 km 3, but is very deep mean depth 4 m). It has a relatively small watershed and seasonal variations in the tributary watercourses have very little impact on the lake's water quality. Ultra-oligotrophic conditions prevail in this lake therefore Chl vales are low throughout the year (mean Chl 1.1±.3 mg m -3 ) [1] In situ data First source for data are regular state monitoring programmes. In lakes Peipsi (state monitoring database: Vänern and Vättern (SLU database: this includes measurements of Chl a; TSM and absorption by CDOM are available only occasionally. We have used here data only for period May-September, as in October and November sun is too low for correct satellite validation and sky is mainly cloudy Satellite data MERIS Reduced Resolution L1b and L2 images from 2nd reprocessing (MEGS 7.4) were downloaded through MERCI database ( Images were visualised and analysed using software tool BEAM 4.2 (Brockmann Consult/ESA) and processed complementary using three different plug-in processors: Case-2 Regional Processor version 1.3 (, developed with coastal zones data from North Sea), Eutrophic Lakes Processor 1. (, developed over Spanish lakes), and Boreal Lakes Processor 1. (, developed over Finnish lakes).

3 2.4. ICOL processor Due to complex interactions between land, atmosphere and water during lake remote sensing, pixels near shore may be affected by the reflectance from neighbouring land pixels so called adjacency effect. Improved Contrast between Land and Ocean (ICOL) processor was used to correct for this effect. The input for ICOL processor was a MERIS L1b product and output BEAM- DIMAP file with corrected radiances Case-2 water processors After ICOL pre-processing, BEAM plug-in processors were applied to produce adjacency corrected L2 data from MERIS L1b data over inland waters. These products are later marked as ICOL+ the name of the processor, in tables and figures. Also standard MERIS L1b (without ICOL pre-processing) was used as input products for Case-2 Water processors. In Case-2 water processors a novel algorithm for atmospheric correction and a new set of algorithms to derive optical properties is present [11] Atmospheric correction (AC) module The atmospheric correction neural network for the BEAM and lake processors includes: a) a new aerosol optical model b) a modification in the calculation of the transmittance of the upward directed radiance, c) an improved surface reflectance model, d) a new interpolation procedure for the results of the Monte Carlo photon tracing calculations, e) an extended set of more than 2 simulated spectra 2.7. Bio-optical models Atmospherically corrected reflectances are converted into inherent optical properties (IOP) and concentrations (Chl a and TSM) with a new set of different neural networks (ranges shown in Tab. 1). The neural networks were developed through simulations performed with bio-optical models using specific inherent optical properties of different water types [8]: a) Boreal lakes - the absorption by gelbstoff can be high. Most of the lakes are oligotrophic, but meso- and eutrophic lakes were used as well; b) Eutrophic lakes - the optical properties are dominated by phytoplankton where Chl a concentrations can be high; c) - trained with mean optical properties for coastal areas. Table 1. Range of optically active substances that are used for training of three different Neural Networks. Chl a (mg m -3 ) Tsm (g m -3 ) a CDOM (m -1 ) 2.8. Confidence flags Standard L In and Lake Processors, pixels marked as L2 invalid product were removed from analysis. Pixel is flagged as L2 invalid if one of the seven flags (Rad_err top of atmopshere radiance out of valid range; Land land pixel; Cloud_ice cloud or ice; Tosa_oor radiance reflectance at top of standard atmosphere out of range; OOTR - water leaving radiance reflectanc out of training range; Whitecaps - Whitecaps pixels; Wlr_oor WLR out of scope) were raised. In L2 standard product, the effect of removing high aersosol load (ice_hace), medium glint (med_glint) and TSM and Chl invalid product (PCD_16, PCD_17 respectively) pixels were examined. Flag case2_s (Turbid - sediment dominated Case2) was raised in most of the pixles, especially in L. Peipsi, therefore sediment dominated pixels were not removed from analyses. 3. RESULTS 3.1. Chlorophyll a Chl a estimation with standard L2 product is in a good agreement with data over all waterbodies (R 2 =.66, Fig. 2). Case-2 water processors overestimate small Chl a concentrations (up to 4 mg m -3 ) in L.Vänern and L. Vättern. Generally the results are systematically shifted towards higher values, which extent depends on the processor - overestimates concentrations by a factor of two, then and results are even higher. There is no good correlation between satellite and data. Applying adjacency effect corrector ICOL and removing invalid pixels marked by confidence flags does not improve the result significantly as seen in Tab. 2.

4 MERIS algal_2: Cchl (mg m -3 ) y =.85x R 2 =.66 Chlorophyll a 1:1 L. Peipsi L. Vänern L. Vättern : C chl (mg m -3 ) Figure 2. Comparison of MERIS estimated Chl a (product algal_2) and measured Chl a in three lakes. 2. Total suspended matter The frequency distribution of TSM concentrations is quite similar between and all Case-2 processors data (Fig. 3). Direct comparison of satellite and measured data showed an overestimation of small TSM values (~1 g m -3 ) and low correlation in case of every processor in L. Peipsi. Removing invalid pixels, indicated by flags, did not improve the result, whereas the ICOL preprocessed images were in better correlation with data (Tab. 3). 'Table 2. Correlation analyses between measured and MERIS L2 Chl a products. N means the number of data pairs used for the calculation. Percentage in brackets shows the amount of pixels left after removing invalid pixels. Chl a Regression equation R 2 N Regression equation R 2 N (after removing invalid pixels) MERIS L2 y =.84x y =.85x (4%) y =.79x y =.7x (58%) y =.44x y =.38x (86%) ICOL+EUT y =.79x y =.32x (95%) y =.77x y =.77x (54%) y =.42x y =.37x (82%) EUT y =.35x y =.34x (98%) Table 3. Correlation analyses between measured and MERIS L2 TSM products. N means the number of data pairs used for the calculation. Percentage in brackets shows the amount of pixels left after removing invalid pixels. TSM Regression equation R 2 N Regression equation R 2 N (after removing invalid pixels) MERIS L2 y =.29x y =.15x (24%) y =.39x y =.38x (97%) ICOL+ y =.74x y =.73x (95%) ICOL+EUT y =.97x y =.94x (92%) y =.31x y =.3x (88%) y =.52x y =.5x (94%) EUT y =.69x y =.65x (91%) Table 4. Correlation analyses between measured and MERIS L2 CDOM products. N means the number of data pairs used for the calculation. Percentage in brackets shows the amount of pixels left after removing invalid pixels. CDOM Regression equation R 2 N Regression equation R 2 N (after removing invalid pixels) MERIS L2 y =.18x y =.55x (25%) y =.72x y =.75x (94%) ICOL+ y =.21x y =.21x (94%) ICOL+EUT y =.25x y =.25x (94%) y =.69x y =.72x (94%) y =.19x y =.18x (94%) EUT y =.3x y =.3x (87%)

5 Normalized frequency standrad L2 ICOL+ (a) Water leaving reflectance L. Peipsi ICOL+EUT C TSM (g m -3 ) 25 3 More Figure. 3 Histograms of TSM as estimated by MERIS standard L2 and Case-2 water processors products and measurements in studied lakes. : a_gelbstoff (m -1 ) y =.75x -.14 R 2 =.61 1:1 L.Peipsi L.Vänern L.Vättern : a CDOM (m -1 ) Figure 4. Comparison of processor: estimated CDOM products and measured CDOM absorption in three lakes Dissolved organic matter The retrieval of CDOM absorption by processor had a quite good correlation with data, especially in the case of ICOL pre-processed images (Fig. 4, R 2 =.61,). In the case of L. Vättern pixels all processors delivered too low values. Additional processing with ICOL and removal of flagged pixels didn t improve the result. Eutrophic, and standard L2 products showed underestimation of CDOM values in every lake (results of the correlation analyses Tab. 4). (b).8 Water leaving reflectance (c).8 Water leaving reflectance Wavelength (nm) L. Vänern ICOL Wavelength (nm) L. Vättern ICOL Wavelength (nm) Figure 5. MERIS derived reflectance spectra in three studied lakes: L. Peipsi (a), L. Vänern (b), L. Vättern (c)

6 3.4. Water leaving radiance reflectance Opposite to the MERIS standard products, the processors with new AC models did not return negative reflectance values after atmospheric correction. For all Case-2 water processors, the reflectance spectrum was rather similar to those that have been measured earlier in the field. In L.Vättern processor retrieved good results, whereas EUT and overestimated shorter wavelengths of the spectrum (Fig. 5). Using the specific correction for adjacency effect improved really the shape of the reflectance spectra in all lakes, but in L. Peipsi, results were overestimated (Fig. 5a). 3.5 Confidence flags Removing pixels flagged as L2 invalid products has little influence to the result. There are two fags which are not taken into account when L2 invalid flags is being raised: 1) indication that atmospheric correction is out of range, and 2) training range has not been sufficient for concentrations. Although they both were raised commonly pixel by pixel and should be considered while validating data. Including additionally both of these confidence flags to L2 invalid product, it would eliminate ~9% of pixels on average in the case of processor and therefore making it impossible to use the data on further analyses. In case of every parameter (Chl, TSM, CDOM) most frequently the flag indicating the concentration is out of training range was raised in every lake. For processor almost ~5% and for ~1% of pixels were flagged as atmospheric correction was out of range. Other flags were not raised. ICOL preprocessing did not affect the results and overall behavior stayed similar. 4. DISCUSSION AND CONCLUSIONS The MERIS products are still under validation and preliminary promising results have been demonstrated. It is also important to mention that there has been real improvement when more case specific and local algorithms have been developed. The shape of the spectra has improved significantly with new Case-2 Water processors s atmospheric correction module. The comparison shows very good results in case of estimating CDOM absorption using boreal lake processor. The reason for the overestimation of Chl in most of the cases is not yet clear and need careful check. At the moment there are no specifically developed algorithms for the lakes that we studied, even these lakes are mostly within the range for which the MERIS algorithms were developed. There is enormous variation of optical properties caused by varying concentrations of optically active substances, but also by inherent optical properties in coastal and inland waters. These variations can be quantified using rather simple classification methods as shown in [12]. Spectra presented in Fig. 5 correspond very well to the classes of water developed for Estonian and Finnish lakes [12]: L. Peipsi being Moderate, Turbid or Very turbid, L. Vänern being Clear or sometime Moderate. L. Vättern would belong by this classification to clear class, however so clear water was not presented in classification database and its reflectance spectra were more similar to clearest (transparent water) described elsewhere. L. Vänern spectra (Clear) have the maximum reflectance at the wavelengths between nm and it decreases sharply at the wavelengths on both sides of the maximum, so that its value at 5 nm is higher than at 65 nm. Type Moderate spectra also have maxima at nm, but reflectance at 5 nm is less than at 65 nm. Type Turbid (L. Peipsi) has a reflectance maximum between 58 and 6 nm, and its shape is rather irregular. The maximum reflectance is the highest among all measured spectra. Remarkable is the low reflectance at nm that corresponds to the Chl a absorption peak. Very turbid case has an additional reflectance maximum notable at nm. Further analyses of the large lakes data will include improved classification based on the parameters that belong into state monitoring program, but also using satellite specific band selection for reflectance spectra. Classification provides complementary information for proper regional optical model and variation of optical properties over location and seasons, which should in next step also improve the applicability of algorithms to satellite images. Acknowledgements: For Estonian Science Foundation Grant s 6814 and 73 for funding the work and for ESA/ENVISAT, EOHelp Desk, Cat 1 ID 318 and Brockmann Consult for MERIS images and help in processing. 6. REFERENCES 1. Sathyendranath, S. (ed.) (2). Remote sensing of ocean colour in coastal and other optically complex waters. Report of the International Ocean Colour Coordinating Group, (3). IOCCG, Dartmouth, Canada

7 2. Dekker, A. G., Vos, R. J. &. Peters, S. W. M., (22). Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. International Journal of Remote Sensing (239: Directive 2/69/EC. Official Journal of European Commission L327: Nõges, P., Kangur, K., Nõges, T., Reinart, A., Simola, H. & Viljanen, M. (28). Highlights of large lake research and management in Europe. Hydrobiologia (599): Nõges, P. & Nõges, T. (26). Indicators and criteria to assess ecological status on the large shallow temperate polymictic lakes Peipsi (Estonia/Russia) and Võrtsjärv (Estonia). Boreal Environmental Research (11): Arst, H., (23). Optical properties and remote sensing of multicomponental water bodies. Praxis Publishing, Springer, Chichester, UK. 7. Alikas K. & A. Reinart Validation of the MERIS products on large European lakes: Peipsi, Va nern and Va ttern Hydrobiologia (28) 599: Koponen Sampsa, A. Ruiz-Verdu, T. Heege, J. Heblinski, K. Sorensen, K. Kallio, T. Pyhälahti, R. Doerffer, C. Brockmann and M. Peters. Validation Report. Development of MERIS lake water algorithms June 26, attachments/ /MERIS_LAKES_Validation_report.pdf 9. Nõges, T. (eds), (21). Lake Peipsi. Meteorology, Hydrology, Hydrochemistry. Sulemees Publishers, Tartu, 163 pp. 1. Willén E., (21. Phytoplankton and water quality characterization: Experiences from the Swedish large lakes Mälaren, Hjälmaren, Vättern and Vänern. Ambio (3): Doerffer, R (28). Atmospheric Correction Neural Network for BEAM Processor April 17, 28 ( eric_correction_rd28417.pdf?version=1). 12. Reinart, A, Herlevi A, Arst H, Sipelgas L., (23) Preliminary optical classification of lakes and coastal waters in Estonia and South Finland. Journal of Sea Research (49):