MERIS VALIDATION WITH RESPECT TO OPERATIONAL MONITORING NEEDS IN THE NORTH AND BALTIC SEA

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1 MERIS VALIDATION WITH RESPECT TO OPERATIONAL MONITORING NEEDS IN THE NORTH AND BALTIC SEA Kerstin Stelzer (1), Jasmin Geißler (1), Carsten Brockmann (1), Holger Klein (2), Jeanette Göbel (3), Anu Reinart (4), Kai Sørensen (5) (1) Brockmann Consult, Max-Planck-Str. 2, Geesthacht, Germany, (2) Bundesamt für Seeschifffahrt und Hydrographie, Bernhard-Nocht Str. 78, 20359, Hamburg, Germany, (3) Landesamt für Natur und Umwelt Schleswig-Holstein, Hamburger Chaussee 25, 24220, Flintbek, Germany, (4) Tartu Observatory, Tõravere, Tartu County, Estonia , Estonia, (5) NIVA, Gaustadalléen 21, NO-0349 OSLO, Norway, ABSTRACT In the framework of the GSE project MarCoast and the ESA Case2 Regional project, the concentration of water constituents and SST derived respectively from the MERIS and AATSR sensors, have been validated with in-situ measurements collected by European national monitoring agencies. Alternative processing methods (FUB WEW and Case2Regional processor) have been included in the analysis. Selected results of this work are presented here. They do not permit simple conclusions, but point out the strengths of the different algorithms, as well as problematic areas concerning the case 2 water atmospheric correction, and the retrieval of water constituents close to the shore line. 1. INTRODUCTION The importance of operational monitoring and research for marine environment is reflected in a number of international conventions, directives and initiatives such as Water Framework Directive, European Marine Strategy, TMAP or GMES. In-situ observing systems are one of the main components in the GMES infrastructure. Remote sensing data have the potential to amend field data and to facilitate operational monitoring applications. The objective of the validation analysis undertaken here is testing the suitability of the products of the MERIS and the AATSR sensor onboard the ENVISAT satellite. MERIS provides optical constituents for case 1 and case 2 waters in the standard Level 2 product. AATSR provides SST data. Using the standard algorithm to calculate water constituents from MERIS causes problems in regions close to the coast. This is due to coastal effects, e.g. adjacency effect, and the amount of coloured dissolved organic matter and inorganic suspended particulate matter influencing the atmospheric correction. Another point is that the flag indicating valid pixel values is very strict and the amount of valid pixels is often low which reduces the advantage to survey large areas. The ESA Case2 Regional processor, developed by GKSS/BC [1], is one approach to overcome this problem. Another algorithm, Proc. of the '2nd MERIS / (A)ATSR User Workshop', Frascati, Italy September 2008 (ESA SP-666, November 2008) applied in this context, has been developed by the Freie Universität Berlin (FUB WeW) [2]. The approach to compare the performance between remote sensing methods for the retrieval of water parameters differ from the approach of the monitoring agencies who want to validate Earth Observation data in terms of fitting to the sea truthing data and their purpose. In the framework of the ESA GSE project MarCoast [3] and the ESA Case2Regional project, a comparison between MERIS chlorophyll a, suspended matter and yellow substance values, transparency as well as AATSR-SST with in-situ measurements has been performed. The field data were collected between 2006 and 2008 in the North and the Baltic Sea by four national monitoring agencies, located in Germany (BSH, LANU), Estonia (TO) and Norway (NIVA). The validation was done in cooperation between BC and these authorities. Different techniques (e.g. transects, compositing, time series and scatter plots) have been used to compare Earth Observation products based on the different algorithms with in-situ measurements. The results demonstrate strength and restrictions of the algorithms which are exemplarily illustrated in this paper with chlorophyll-a data. 2. FOCUS OF VALIDATION The validation of MERIS products undertaken in the framework of the MERIS and AATSR Validation Team (MAVT) has the objective to prove that the water leaving reflectance and water constituents are within a certain error level compared to in-situ measurements which correspond directly to the values retrieved from the satellite measurements [4]. The requirements of operational monitoring entities for validation of satellite measurements differ significantly from this MAVT objective. Their interest is to use the data for monitoring purpose, and it has to be proven that MERIS data are appropriate. The operational users expect the satellite data to correspond to their in-situ data, whereas these are not taken for the purpose of validating satellite measurements. Further the

requirements of users on the validation results are different.

patterns as well as imaging of fronts and structures in a synoptic picture. provided by BSH and additional institutions have been taken into account.

Earth Observation Data Water constituents derived from MERIS data applying different processing methods have been investigated within this validation, namely the standard L2 products, the MarCoast

The parameters derived from these methods are chlorophyll-a concentration, concentration of total suspended matter, yellow substance absorption and transparency.

During the growing seasons in 2006 and 2008, satellite data parallel to dedicated measurement days and periods of in-situ measurements have been investigated.

.07.2008) 3.2. In-situ Data Different sources of sea truthing data have been utilized for the validation purposes.

Therefore the available in-situ data set consists of measurement stations along transects in the open North Sea (BSH), measurements stations close to the shore (LANU) as well as in the Finnish Bay

Further, regular reports about detections and locations of algal fields observed from ship cruises Figure 2.

2 requirements of users on the validation results are different. Depending on their responsibilities they are interested in comparison between measurement point and pixel value, reproduction of measurements along ship lines, validation of field observations of patterns as well as imaging of fronts and structures in a synoptic picture. provided by BSH and additional institutions have been taken into account. The spatial distribution of the in-situ measurements is shown in Figure DATA 3.1. Earth Observation Data Water constituents derived from MERIS data applying different processing methods have been investigated within this validation, namely the standard L2 products, the MarCoast product provided by ACRI-ST as well as products derived from the Case2Regional and the FUB Water Processors. The parameters derived from these methods are chlorophyll-a concentration, concentration of total suspended matter, yellow substance absorption and transparency. Reduced resolution (RR) data and full resolution (FR) data have been investigated. Further, the standard SST derived from AATSR sensor has been validated as well. During the growing seasons in 2006 and 2008, satellite data parallel to dedicated measurement days and periods of in-situ measurements have been investigated. Figure 1: RGB image (left) and FUB chlorophyll concentration (right) in the Baltic Sea with an algal bloom in the central part ( ) 3.2. In-situ Data Different sources of sea truthing data have been utilized for the validation purposes. The in-situ data vary due to different sampling and analysis methods of the respective institutions. Therefore the available in-situ data set consists of measurement stations along transects in the open North Sea (BSH), measurements stations close to the shore (LANU) as well as in the Finnish Bay (TO) and continuous measurements derived from a ferrybox system in the Baltic Sea / Skagerak (NIVA) [5]. Further, regular reports about detections and locations of algal fields observed from ship cruises Figure 2. Measurement stations of users Parameters available from the different campaigns are amongst others the chlorophyll concentration, secchi depth, SST, suspended matter and humidity, while different sampling and analysis methods have been applied. The chlorophyll concentration has been derived using in-vivo fluorometric methods (BSH, NIVA), spectral photometer (LANU, TO) as well as HPLC analysis (NIVA). 4. METHODS The following methods have been applied to the different data sets: Scatterplots and determination of the regression coefficients of point-to-pixel comparison at regularly monitored stations indicating if a relationship between Earth Observation data and in-situ data exists. Because this method is sensitive to a number of factors such as time differences between image acquisition and in-situ measurement, sampling depths or patchiness of chlorophyll-a concentration, the method is used for a first assessment but has to be regarded carefully. Regression coefficients have been determined for all validated parameters in order to get a comparable statistical parameter. Transect plots along ship routes with dedicated measurement stations have been applied in order to show the evolution of point measurements along a transect and how they fit into the corresponding transect of satellite based parameters. This method shows the tendencies of both measurement techniques and avoid the problems caused within the direct pixel-to-point comparison. Transect plots along measurements from ferrybox systems enable the direct comparison between continuously measured in-situ and satellite data along the ship lines. Time series of measurement stations that are sampled in regular intervals are plotted in comparison with

3 concentrations of water constituents derived from satellite data of all scenes available for the same period. Further, a qualitative comparison of the appearance of high chlorophyll concentration with algal bloom alerts reported from ships on occasion has been performed. These have been compared to reports available in the internet [6], [7], [8], [9]. Exemplary in-situ data sets have been compared to the different processing methods of the EO data. 5. RESULTS The different validation methods give a good understanding of the Earth Observation and in-situ data and their agreement. They provide different levels of information and try to answer the different questions of the users. In the following, the results of the single methods are shown. The scatterplots (Figure 3) and regression coefficients listed in Table 1 show a fairly good agreement between in-situ and EO data for chlorophyll-a concentration of the different algorithms and for transparency, a poor relation for yellow substances, and a very good correlation for SST. Table 1: Summary of correlation results Product / Parameter Year N R² CHL (BSH), RR, MC TRANS (BSH), RR, MC YS (BSH), RR, MC CHL (LANU) RR, MC CHL (LANU) RR, MC CHL (LANU) FR, FUB CHL (LANU) FR, FUB CHL (TO), RR, MC SST (LANU) SST (BSH) Chlorophyll MERIS_FUB [mg/m³] y = 1.055x R 2 = Chlorophyll in-situ [mg/m³] Figure 3: Scatterplot of LANU in-situ chlorophyll-a measurements and MERIS FR data processed with FUB algorithm in 2008 The transect plots show the tendency between continuous EO data and single sampling points along the ship track, even if the water bodies have been slightly shifted between the acquisition times. The satellite data give additional information showing structures of water constituents between the sampling points. In Figure 4 the chlorophyll-a concentration along one of the ship tracks of the BSH ship cruise 2006 has been plotted. The red dots indicate the values at the sampling stations while the blue line shows the chlorophyll concentration derived from averaged MERIS data (MarCoast processing ACRI-ST). Figure 4. Comparison of chlorophyll concentration derived from MERIS sensor (standard Marcoast product) and in-situ measurements along BSH ship track (in-situ: BSH, 2006) Time series plots give the possibility to assess the fit of both data sets although the acquisition has been taken place on different dates. Further, the satellite derived data provide information between the dates of the in-situ measurement. In most cases, the general trend could be seen in both data sets, especially if the station is not too close to the coast or influenced by highly sediment and yellow substance loaded waters or tidal impact. Figure 5 shows the time series of station South of Amrum

4 (LANU AlgFES measurement net) for April August Chlorophyll Concentration [mg/cm³] Station: south of Amrum Date The qualitative approach for comparing remotely derived chlorophyll-a concentrations with algal bloom reports show that the field observations of algal agglomerations have a very good agreement with the structures observed with Earth Observation products (Figure 7). Sometimes, movements of structures could be observed when satellite data and observation have a time difference. Chloropyhll in-situ Chlorophyll Satellite in-situ: LANU 2008 Figure 5.Time series of in-situ measurements and MERIS derived chlorophyll concentration at a dedicated measurement station (in-situ: LANU, 2008) Transect plots with ferrybox data give the possibility to compare two continuously measured data sets. Figure 6 shows the chlorophyll concentration along the ferrybox transect from calibrated in-vivo fluorometric measurements, HPLC point measurements and satellite derived chlorophyll-a concentrations from different calculated by different processors. Structures such as fronts and algal blooms are detected in both data sets even if they are shifted due to time difference of the acquisitions. Depending on the measurement method of in-situ data, a systematic difference of values may occur. This is for instance the case when in-situ data are measured with in-vivo fluorometric methods because the algorithm for deriving MERIS chlorophyll-a concentration is based on in-situ HPLC chlorophyll data. Figure 6: Comparison of different in-situ and remote sensing measurements for chlorophyll a values along a transect of ferrybox measurements. (in-situ: NIVA). A general improvement of validation results could be achieved by excluding non-reliable pixels from the satellite data sets. Only pixels that fulfil certain quality criteria have been taken into account. The selection has i.e. excluded pixels influenced by clouds or cloud shadow or by neighbouring land pixels. Figure 7: Thematic Map of algal field observations in Mai2008 published by BSH, compared with MERIS FR, FUB processing (left: overview, right: focus on area of interest). 6. CHALLENGES & DISCUSSION When comparing in-situ and satellite data, several aspects have to be taken into account: - different areas covered or sampled (pixel vs. point) - patchiness of chlorophyll concentration - accuracy of co-location of pixel and measurements station - temporal coincidence with respect to tides and other currents - different analysis and processing methods - different depth of sampling / observation - measurement stations close to coastline or within intertidal flats (see Figure 3) - challenging to atmospheric correction algorithms above case-2 waters and yellow substance retrieval Generally, some care has to taken with so called matchups, i.e. comparison of single measurements with pixel values of the satellite in coastal waters, which are often highly variable in time and space (patchiness). Even after small time differences the correlation can break.

5 7. CONCLUSIONS The results of the different validation methods provide different information contents and have to be assessed with respect to the requirements of the users and the availability of in-situ data. The acceptance of remote sensing data for monitoring purposes is strongly depending on the validation results whereas users judge the results in a quite different way depending on their requirements. The validation process will go on in close cooperation with the users with the goal to achieve a reliable monitoring system combining in-situ and remote sensing methods by making best use of the strengths of each of them. Further, different processing algorithms fit for different purposes. The better reproduction of structures and fronts can be reached with FUB WeW or C2R algorithms, while the agreement between single pixels and measurement points is higher with ESA IPF or Marcoast processing. 8. REFERENCES 1. Doerffer, R., Schiller, H. (2008 MERIS Lake Water Algorithm for BEAM. Algorithm Theoretical Basis Document (ATBD). ESRIN Contract No /06/I-LG 2. Schroeder, T., Schaale, M. and Fischer, J. (2007): retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case-2 water processor for BEAM, Int Journal of remote Sensing, Vol 28, Doerffer R. (2002) Protocols for the validation of MERIS water products. European Space Agency Doc. No. PO-TN-MEL-GS-0043, Sørensen, K., AAS, E., HØKEDAL, J. (2007): Validation of MERIS water products and biooptical relationships in the Skagerrak. International Journal of Remote Sensing, Vol. 28, Nos. 3 4, /MURSYS-Umweltreportsystem reports/alg/algsit08_2.pdf 9. IOW (2008): Cruise- No. 07/PE/08/17, 7. Kratzer, S., Brockmann, C., Moore, G. (2007): Using MERIS full resolution data to monitor coastal waters - a case study from Himmerfjarden, a fjord-like bay in the northwestern Baltic Sea, RSE, 112 (5),

Validation of MERIS products for Skagerrak and Norwegian coastal waters

Validation of MERIS products for Skagerrak and Norwegian coastal waters Kai Sørensen (NIVA), Are Folkestad (NIVA) Kerstin Stelzer (BC), Carsten Brockmann (BC) Roland Doerffer (GKSS) Outline of the presentation