WP54 Calibration and validation

Transcription

1 Update for Report on data quality and Title: Subtitle: Related to: Prepared by: Doc: WP54 Calibration and validation Update for Report on data quality and data WP 5.4, Deliverable D54.3_2 WP4 Issue/Rev: 1.0 Date: Finnish Environment Institute (SYKE) FM_PH3_WP54_D543_Update_PR

2 Update for Report on data quality and Involved Consortium Partners Partner Who? Task/Role SYKE Sampsa Koponen, Kari Kallio, Timo Pyhälahti, Jenni Attila, Hanna Piepponen, Vesa Keto BC Kerstin Stelzer Input EOMAP Karin Schenk, Thomas Heege, Sebastian Krah Coordination, Input Input Document Status Issue Date Who? What? Sampsa Koponen Initial version Kerstin Stelzer Input MV Lakes Sampsa Koponen Kari Kallio, Timo Pyhälahti, Jenni Attila, Hanna Piepponen Karin Schenk, Thomas Heege, Sebastian Krah Sampsa Koponen Compiled version Kari Kallio, Karin Schenk, Kerstin Stelzer Sampsa Koponen Final version Input from Finland (SYKE) and Germany (EOMAP) Input South German rivers and lakes Input from Finland, comments for executive summary and conclusions Reference Documents Issue Date What? DOW

3 Update for Report on data quality and Contents List of Abbreviations Scope of this document Executive Summary Validation in Finland (SYKE) MERIS Chl-a MERIS data processing Land and cloud masks In situ data Data aggregation and visualization Effects of CDOM on Chl-a estimation User comments High resolution water depth products near Hanko and Kotka Study area and satellite data Satellite data processing Results User comments Validation in Germany (EOMAP and BC) Lakes in South Germany-MERIS vs. Landsat Satellite data validation Data Comparison Summary of the results Bavarian Rivers Satellite data validation Data comparison Summary of the results River Rhein (Germany) Satellite data processing Data comparison Summary of the results User comments Lakes in Mecklenburg-Vorpommern In situ data MERIS data Time Series Extraction & statistics Results Conclusions Conclusions Quality of in situ data Quality of satellite products Quality of data from in situ devices Validation lessons learnt...49 References

4 Update for Report on data quality and List of Abbreviations Abbreviation BfG Chl-a EO ETM ESA DOW LfU LUBW MIP MERIS PR RGB TOA WFD WP Description German Federal Institute of Hydrology Chlorophyll a Earth observation Enhanced Thematic Mapper European Space Agency Description of work (Document DOW Initialled.pdf ) Bavarian Environment Agency State Institute for the Environment, Measurements and Nature Conservation of Baden-Wuerttemberg Modular Inversion and Processing System Medium resolution Imaging Spectrometer Public Report (Document Type, public) Red Green blue Top of Atmosphere Water Framework Directive Work Package

5 Update for Report on data quality and 1 Scope of this document This document is an update to D54.3 Report on data quality and data. The original D54.3 described the results of the validation activities performed in until October This document describes the validation done between November 2012 and October It includes results for three satellite instruments (MERIS, Landsat ETM+, and WorldView-2). The in situ data used in the validation includes measurements done by partners and data provided by user organizations. Related -documents: - D54.1 Radiometric and in situ measurements of the ground truth for assessing the services (delivered 11/2011) describes the measurements made during the 1st phase of the project. - D54.2 Radiometric and in situ measurements of the ground truth for assessing the services (delivered 10/2012) describes the measurements made during the 2nd phase of the project. - D54.3 Report on data quality and data (delivered 10/2012) describes the results of the validation effort until October D52.1 Report on Case Studies for practicability (delivered 10/2012) will describe in detail the in situ measurements made by EAWAG for the Lake Constance Field Campaign. 2 Executive Summary Satellite products from phase 3 have been validated with in situ data in Finland (MERIS Chl-a and WorldView-2 water depth) and Germany (MERIS Chl-a, and MERIS and Landsat 7 ETM+ turbidity and suspended matter). In situ data suitable for EO validation is still lacking in many places since the collection of field samples has not been optimized for EO purposes. Due to this, it is convenient to present the results for Chl-a and turbidity/tsm as time series plots since with those the behavior of EO estimates and in situ measurements can be compared from season to season and year to year. The time series plots make it is possible to analyze where the EO methods work well and where more research (or another instrument) is needed. Problematic areas are small lakes where the resolution of MERIS is not sufficient and in Finland the humic lakes where the absorption by CDOM affects the retrieval of Chl-a. The water depth estimation works well down to a depth of 2 to 3 m. Turbidity in water and atmospheric effects can limit the usability of images. As result of the validation, the EO based products both MERIS based data with a high temporal resolution and Landsat based with high spatial resolution generate very valuable additional information for monitoring aspects and scientific questions. Lessons learnt in the product validation include taking care about time coincidence, location dependence and measurement depth information. Further improvements can be made using pixel wise quality control and data aggregation in the validation process

6 Update for Report on data quality and 3 Validation in Finland (SYKE) In Finland the validation of satellite products took place in the areas shown in Figure 1. In the area Southern Finland, MERIS Chl-a products were compared with in situ observations. In Hanko and Kotka areas water depth products derived from WorldView-2 data were tested. Southern Finland Kotka Hanko Figure 1. Satellite data validation areas in Finland (indicated with red)

7 Update for Report on data quality and 3.1 MERIS Chl-a Finland has over lakes (size > 1 ha) and the number of lake water bodies defined by the Water Framework Directive (WFD) is almost About 60% of them were not ecologically classified in the last WFD reporting due to lack of monitoring data. E.g. Chlorophyll a (Chl-a), a measure algal biomass and indicates lake s trophic status, is annually measured only in about 1400 lakes. Hence, in Finland, the main objective for the 3 rd phase of was to provide data for inland water body classification performed under the WFD. We took advantage of the work done for coastal areas, where product types suitable for WFD monitoring had already been drafted. In addition to providing water quality (Chl-a, turbidity, SST) maps from the Baltic Sea, SYKE has provided EO and in situ time series plots and histograms of Chl-a for 188 (out of 214) coastal monitoring areas. This chapter describes the steps taken to process the satellite and in situ data, and shows examples of the results for lakes MERIS data processing During the second phase of, MERIS Chl-a products were processed with BEAM and then calibrated with raft data (see D54.3 for details). The results show that there is a good correlation between satellite observations and the values measured by an in situ fluorometer. In D54.3 the equation used to calibrate satellite data was: Chl-a Calib = 0.346* Chl-a Satellite , (1) where Chl-a Satellite is the original estimate from the processing chain. While the calibration leads to good results with the lake where the raft is, it causes some problems when the method is used for other water bodies. First of all, the bias term of 3.76 g/l sets the lower limit of the estimation range. This is too high since there are lakes in Finland that have Chl-a concentrations of about 1 g/l and for some lake types the classification limit between classes High and Good is 2 g/l (for lakes in northern Lapland), 3 g/l (for humus-poor lakes) or 3.3 g/l (for Shallow humus-poor lakes). So, with Equation (1), Chl-a concentrations belonging to the High class would never be obtained in these lake types. The higher end of the estimation range of the FUB processor is 120 g/l for Chl-a so the slope term of (and the bias) set the maximum calibrated value to about 45 g/l. This in turn is too low for many eutrophic lakes where the concentration measured with in situ laboratory samples have been over 100 g/l. Due to this the processing steps of MERIS data were revised for the inland WFD water bodies and are the following (with BEAM ): 1. AMORGOSS for improved geolocation 2. Radiometry correction (Smile, & Equalization, as the MERIS data was from the 3 rd reprocessing the Calibration step was not included) 3. FUB/WeW (water quality processing) 4. Rectification 5. Land and cloud masking (see below) 6. Product generation

8 Update for Report on data quality and The new processing does not include empirical correction. The products were stored as GeoTiff files, which were used in the further processing and published through a WMS. So far the summer months (June-Sep) of years 2006, 2009 and 2011 have been processed from Southern Finland. Figure 2 shows an example Chl-a map. Figure 3 shows the effect of AMORGOS on the products. When AMORGOS is not included in the processing there are more red (non-valid highconcentration) pixels near the shore. Hence, the results are better with AMORGOS included in the processing. MERIS data used in this analysis were provided by ESA. Figure 2. MERIS Chl-a map of Southern Finland on June 16,

9 Update for Report on data quality and Without AMORGOS With AMORGOS Figure 3. The effect of AMORGOS on the Chl-a products. The number of erroneous red (high concentration) pixels is reduced when AMORGOS is used in the processing Land and cloud masks Land (and Baltic Sea) areas are masked from the product images using a mask derived from shore line data. The mask is made with a regular grid (300 m pixels) which is also used in the rectification step and is the same for all products. If a pixel contains even a small amount of land according to the shore line data, it is classified as land. The cloud mask is derived from the processed data. All pixels with Chl-a value of (as generated by FUB) are classified as clouds. This initial cloud mask is then buffered by 4 pixels in each direction in order to reduce the errors caused by cloud shadows and thin clouds that often surround thick clouds and are difficult to detect In situ data The in situ data used in this analysis come from the routine monitoring programme implemented in Finland. Under the programme, water samples are collected by regional environmental authorities and companies (who often outsource the sample collection and analysis to consulting companies) required to monitor water bodies as part of their environmental license to operate e.g. a factory. Chla concentrations, representing a 0-2 m surface layer, were analyzed from the samples in a laboratory with spectrophotometric determination after extraction with hot ethanol (ISO 10260, GF/C filter)

10 Update for Report on data quality and Data aggregation and visualization The monitoring areas of WFD are defined as vectors in shape files. These were used to extract the pixel values found within each monitoring area and from each GeoTiff image. The mean and standard deviation of the pixel values were computed and once the whole year was processed time series and histogram plots were generated for each monitoring area in Southern Finland. Example results are shown in Figure 4 and Figure 5. The number of WFD water bodies within the study area (Figure 1) is Due to the limited resolution of MERIS (300 m) small lakes cannot be monitored with it. For this processing, we set a size limit so that the water body must contain at least 3 MERIS pixels before it is included in the analysis. After this restriction the number of water bodies was 662. For all these the time series and histogram plots were generated. It should be noted the number of lakes might vary slightly, if the coordinate grid of reference for defining the rectified MERIS data was shifted with distance less than half of the nominal resolution, or if different coordinate systems were used in rectification. As the number of small lakes is high, and they are effectively randomly located, this does not have significant impact on the results. In addition, if the number of cloud free pixels extracted from the water body was less than 90% of the maximum number of pixels for that water body (based on the landmask) the values from that day were included in the result plots. This will further reduce the effects of cloud cover on the time series analysis. When this analysis was performed the number WFD of water bodies defined for the whole Finland was Figure 6 shows how many of these can be monitored with a 300 m pixel instrument (e.g. MERIS and Sentinel-3 OLCI) as a function of the minimum number of pixels that fit into the water bodies. At least one 300 m pixel can be found from over 2100 water bodies, however, one pixel is rarely enough for a reliable estimate of water quality. If the minimum number of pixels per water body is set to 10 the number of possible water bodies is less than 700. Furthermore, this computation is theoretical in nature and includes shallow and other areas that may be impossible to monitor with EO. This will slightly reduce the number of water bodies suitable for EO monitoring

11 Update for Report on data quality and (a) (b) Figure 4. Chl-a time series (a) and histogram (b) of Lake Suur-Saimaa for year 2011 with EO and in situ data

12 Update for Report on data quality and (a) (b) Figure 5. Chl-a time series (a) and histogram (b) of Lake Maavesi for year 2011 with EO and in situ data

13 Number of water bodies Update for Report on data quality and Normal landmask Buffered landmask Minimum number of MERIS pixels in the water body Figure 6. The number of Finnish lake WFD water bodies as a function of the minimum number of MERIS pixels that fit inside the water body with two land masks. In the buffered land mask, the mask is extended by one extra pixel from the shore Effects of CDOM on Chl-a estimation Finnish lakes are in WFD divided into 12 lake types, the main criteria being humic level, size and depth (Table 1). The share of those water bodies that can be monitored with MERIS varies considerably by lake type. The impact of humic concentration in the estimation of Chl-a by FUB is demonstrated in Figure 7. In humic-poor lakes FUB is able to estimate Chl-a quite realistically, while in humic-rich lakes FUB systematically underestimates Chl-a and there is no correlation. In humic lakes, with CDOM (a400) typically between 3.3 and 11 m -1, the FUB underestimates Chl-a, but there is linear correlation with the in situ Chl-a. This indicates that Chl-a can be estimated with FUB in humic lakes, but estimates must be empirically corrected or the processor must be modified in order to get correct absolute concentrations. There are two high-cdom lake types (with a400>11 m -1 ) in the Finnish typology: humic-rich lakes and shallow humic-rich lakes (Table 1). These lake types represent 7 and 18% of all water bodies, respectively. The impact of high CDOM on remote sensing based estimation of Chl-a also depends on the concentration of particles in water. The limitations must be further studied particularly in the shallow humic lakes where water quality varies more than in the deep lakes

14 MERIS Chl-a µg/l MERIS Chl-a µg/l MERIS Chl-a µg/l Update for Report on data quality and Table 1. The number and share of WFD lake types in Finland, how many of them can be monitored with the MERIS 300 m pixel size and their average water quality (calculated from in situ monitoring measurements made in in June-September (WFD classification period)). Three (ten) MERIS pixels-column indicates the share of water bodies for which at least three (ten) true water pixels can be obtained. Lake type Number of water bodies Share of water bodies % Three MERIS pixels % Ten MERIS pixels % Chl-a µg/l Turb FNU a400 1/m Secchi Humic lakes, medium-sized Lakes with short water retention Humic lakes, shallow Humic-rich lakes, shallow Humic-poor lakes, shallow Humic lakes, small Lakes in N. Lapland Humus-rich lakes Naturally nutrient rich lakes Humic lakes, large Humus-poor lakes, large Humus-poor lakes, small and medium-sized Total m Humus-poor lakes, small&medium-sized Humic lakes, medium-sized 30 MERIS= MERIS=7.9 In situ= 5.0 In situ= 9.4 N= 35 N= R 2 = R 2 = Humus-rich lakes MERIS=5.8 In situ= 18.2 N= 20 R 2 = In situ Chl-a µg/l In situ Chl-a µg/l In situ Chl-a µg/l Figure 7. Relationships between MERIS estimated (FUB) and in situ Chl-a in three lake types with different humic (CDOM) levels in Finland. Typical CDOM (a(400)) in these lake types (from left to right) are: 2.9, 7.3 and 16.3 m -1. See Table 1 for other average water quality characteristics. Data: August 2006, 2009 and Chl-a was calculated from all MERIS images (3x3 pixels around the monitoring station) and in situ measurements of August each year

15 Update for Report on data quality and Figure 8 shows the in situ remote sensing reflectance spectra (Rrs) measured from five lakes in Finland with a portable spectrometer (ASD Pro Jr.), while Table 2 shows the corresponding results of the lab measurements. In lakes Säkylän Pyhäjärvi and Vesijärvi the CDOM absorption is low and reflectance values high. In lakes Lammin Pääjärvi and Keravanjärvi the situation is the opposite. One important feature of the spectra is the peak near 700 nm and the dip near 670 nm. The size of the peak in relation to the dip has been observed to grow with increasing Chl-a concentrations and several Chl-a estimation algorithms that take advantage of this have been developed (starting with Dekker (1993) and Gitelson et al. (1993)). Figure 9 shows a comparison of a reflectance band ratio vs. Chl-a concentration for the five lakes. While the number of data points is small and the results are preliminary, the correlation between the band ratio and Chl-a concentration appears to be high if the data points are grouped according to CDOM class. Same behavior has been noted with reflectance data simulated with a large in situ concentration dataset measured from Finnish lakes (Kallio 2006). This also indicates that the Chl-a estimation can be improved, if information about the CDOM absorption is available. The situation becomes more complex when algorithms based on neural networks trained with simulated data (such as the FUB processor) are used in the estimation. They contain nodes which convert the input values (reflectances and other parameters) into output values (water quality parameters) using weights defined during the training. Once the network has been trained it is not possible to modify it and if the measured water type is not included in the training data the results can be unreliable. In addition, atmospheric correction, which is the most critical part of the water quality processing, is typically also based on neural networks. Strong CDOM absorption is usually not included in the models and the FUB processor regularly processes negative reflectances for humic lakes User comments Based on the user comments in D54.3 several topics for further research were identified. These are shown in Table 3 together with the current status of the research. Table 4 in turn shows the user comments for the data provided in Phase 3 and the response of to these comments

16 Update for Report on data quality and Figure 8. In situ remote sensing reflectance spectra from Finland measured with ASD spectrometer. Table 2. In situ measurements from Finland in 2007, 2011 and Lake and station number Date (YYYYMMDD) Local time Secchi depth (m) Chl a (ug/l) Turbidity (FNU) a CDOM (400) (1/m) TSM (mg/l) Vesijärvi :20 2,9 3,7 3 1,76 2,6 Vesijärvi :10 3,7 2,9 2,3 1,38 2 Vesijärvi :10 2,9 4,6 1,9 1,1 2,2 Vesijärvi :20 4,6 1,7 1,1 0,9 1,2 Vesijärvi :15 2,8 5,4 2,4 1,57 3,4 Vesijärvi :30 3,1 5,1 2,3 1,52 3 Vesijärvi :30 2,4 11 3,6 1,80 2,5 Päijänne ,1 0,86 2,82 0,8 Päijänne :53 4,9 1,9 0,62 2,5 0,7 Säkylän Pyhäjärvi :05 3,5 5,3 1,7 1,6 2 Säkylän Pyhäjärvi :40 3,6 5,4 1,5 1,5 2 Säkylän Pyhäjärvi :15 3,6 7,2 2,3 1,5 2 Lammin Pääjärvi :10 2,3 3,2 1,3 9,63 1,9 Lammin Pääjärvi :45 2,2 3 1,7 12,10 1,2 Lammin Pääjärvi :40 2,1 1,59 0,76 11,23 0,9 Keravanjärvi :05 1,3 8,2 2,1 20,43 2,

17 Chl-a ( g/l) Update for Report on data quality and Vesijärvi Säkylän Pyhäjärvi Päijänne Lammin Pääjärvi Keravanjärvi R 709 /R 665 Figure 9. In situ reflectance band ratio (709nm/665nm) vs. Chl-a concentration (laboratory). Vesijärvi and Säkylän Pyhäjärvi have low CDOM values, Päijänne has moderate CDOM values and Lammin Pääjärvi and Keravanjärvi have high CDOM values (Table 2). In the Lake Säkylän Pyhäjärvi station indicated with the solid line arrow the water column is likely to be stratified with higher Chl-a values in the surface layer (the spectra in Figure 8 shows elevated values in the near infrared, which supports this assumption). In a nearby station where reflectance was not measured, a water sample was taken also from the surface layer (top 3 cm) and it had clearly higher Chl-a concentration (indicated with dash line arrow)

18 Update for Report on data quality and Table 3. Topics for further research from D54.3. Further step defined in D54.3 The Lakes Säkylän Pyhäjärvi and Puruvesi both belong to the group called 'large low humic lakes'. Many Finnish lakes have high CDOM concentration which affects the estimation of Chl-a. This must be accounted for in the processing in order to avoid erroneous estimates. How to handle of large amounts of data together with frequent cloud cover? What kind of EO products are the most useful for WFD monitoring? Include AMORGOS for improved rectification. Further testing with the BOREAL LAKES processor Status at the end of Not yet completed: The effects of lake type (humic content) on the estimation of Chl-a have been studied in Chapter and the effects of CDOM absorption are clear. Further analysis and development is still necessary in order to improve the estimation of Chl-a. Partially solved: Automated cloud screening reduces errors caused by clouds but strict rules may also result in loss of valid data. Solved: Time series and histogram plots are generated for WFD water bodies. Further development is expected though. Solved: AMORGOS has been included in the processing. The number of pixels affected by land is reduced. Not yet completed: FUB processor was used for Chl-a. Table 4. User comments for Chl-a products delivered in phase 3. User comments Large lakes are well covered with in situ measurement. Information needs are largest with small lakes. Histograms are useful but further integration of the data into the water quality databases would make it easier to use the data. E.g. the yearly mean and median values would be useful. Further info is needed on the validity of the data in different lake types CDOM, turbidity, temperature could be used as secondary parameters in the classification Response from the team The resolution of MERIS/OLCI is a limitation and Sentinel 2 will be important in the future if it is able to provide reliable Chl-a products. The possibilities for this will be investigated during the GLaSS project, which includes a task WFD reporting based on GLaSS products. Research into the effects of CDOM will be continued during the GLaSS project Turbidity is possible as demonstrated in Germany (Chapter 4). For temperature the resolution of suitable satellite instruments is a limitation (AVHRR which is used for Sea Surface Temperature in 12 large lakes in Finland has a resolution of about 1 km). Reliable estimation of CDOM still required research

19 Update for Report on data quality and 3.2 High resolution water depth products near Hanko and Kotka The objective of this chapter was to find out how well satellite based (WorldView-2) water depth maps match with in situ observations Study area and satellite data Two different test sites were selected for this study. One site is located near Hanko, in the Western Gulf of Finland where the bottom is sandy and sandbanks are common. Another test site located at the Easter part of Gulf of Finland characterized by steep rock shores and esker islands. Field data of water depth was collected in VELMU-program (Finnish Marine Underwater Nature Inventory Programme) during sampling seasons and values were corrected with sea level measured by the Finnish Meteorological Institute. The WorldView-2 images used in the analysis were taken on (Hanko) and (Kotka). WorldView-2 is multispectral satellite sensor with eight optical bands and 2m spatial resolution. Compared to other very high resolution satellites it has three new interesting bands: violet (coastal), yellow and red edge. Satellite images were georectified by using shoreline-layer Satellite data processing EOMAP generated water depth product for (Hanko) and (Kotka) and images with the MIP processor, comprising a coupled retrieval of atmospheric and in-water optical properties (Ohlendorf et al. 2011). As the result of atmospheric correction, the subsurface reflectance is retrieved. The transformation of the subsurface reflectance to bottom reflectance is based on the equations published by Albert and Mobley (2003). The water depth, which is originally an input value for these equations, is iteratively calculated in combination with the spectral unmixing of the corresponding bottom reflectance. By minimizing the residual error the final water depth is determined. This processing step results in two output images, one image containing water depth and another image containing bottom reflectances. The production of water depth products was tested also at SYKE (by the Marine Research Centre) for the two images. Based on the idea that light is attenuating exponentially when the depth is increasing, Lyzenga (1978) showed the equation between the remote sensing reflectance and the water depth. Lyzenga s model shows that light will penetrate water depending on its wavelength and this information can be used to determine depth from satellite images. The model however assumes that water quality is homogeneous throughout the image and it requires information about the reflection properties of different bottom types. Stumpf et al. (2003) proposed a ratio method which reduces the effect of bottom substrate. The method is based on the Lyzenga s algorithm but it utilizes two bands to derive the depth. By using this method the change in attenuation of different colored light is much greater than the change affected by bottom reflectance in different bands. This method can then be used to derive depth over varying bottom substrates. At SYKE the Stumpf et al. (2003) model was applied to both satellite images. Changes in water quality were taken into account by dividing satellite images into several smaller processing areas based on their turbidity. The best band combinations were tested and some local changes in the equation were done with a set of field measurements (N=249 in Hanko and N=88 in Kotka). Half of the field measurements were used for calibration and half for validation (see below)

20 Update for Report on data quality and Results The results of the model used by SYKE are shown in Figure 10 (Hanko) and Figure 11 (Kotka). The accuracy of the model was tested by comparing the estimated values with field samples (Figure 12). In Hanko the SYKE model was shown to be quite accurate at least in shallow areas but the error was increasing with the depth. In Kotka the model was less accurate than in Hanko due to turbid water and unclear atmosphere. The shallowest areas have too high depth values which may be due the steep shores and the reflection from the land areas. The model is still capable of finding ecologically important shallow areas and gives accurate estimates of water depth in those areas. The result of the processing done at EOMAP is shown in Figure 13 for the (Hanko) image. The results of the comparison of EO values vs. in situ values are shown in Figure 14 (with all available stations). Due to high turbidity in water, the processing was successful only for a small portion of the (Kotka) image and the results did not cover the study area well. Thus, a comparison with in situ data was not possible. Instead, the results by EOMAP were compared with the results from SYKE (Figure 15). According to the results, the EOMAP processing works well for shallow water areas (<2 m). As evident in the plots, the EO values are slightly underestimated but this can be corrected by calibrating the result with in situ observations when those are available. Since water depth does not change quickly, the in situ measurements do not have to be concurrent with the satellite overpass if information about the sea level is available (i.e. data from different years can be used). For deeper water the estimation errors are larger. Fortunately, the users were more interested in shallow water areas. The cut-off depth (the largest estimated depth) appears to be about 2.5 to 3.5 m depending on the processing and the area (differences in water quality)

21 Update for Report on data quality and Figure 10. Water depth model derived from satellite data for Hanko ( ) test site (SYKE processing). Figure 11. Water depth model derived from satellite data for a small part of Kotka ( ) test site (SYKE processing)

22 Update for Report on data quality and Figure 12. Modelled depth values (SYKE processing) compared to water depth measured in situ (validation data). Left figure is from Hanko ( ) and right figure from Kotka ( ). Figure 13. Water depth model derived from satellite data for Hanko ( ) test site (EOMAP processing)

23 Update for Report on data quality and Figure 14. Modeled depth values compared to water depth measured in situ from Hanko ( ) with EOMAP processing (validation data). Figure 15. Water depth values estimated from satellite data by EOMAP compared to water depth values estimated from satellite data by SYKE from the Kotka ( ) image

24 Update for Report on data quality and User comments The user comments for the EOMAP water depth product are shown in Table 5. Table 5. User comments for EOMAP water depth product. User comments The quality of the product was sufficient for our purposes even though the absolute depth values were not as accurate as was expected. Validation based on field measurements could improve model to give better estimations of the absolute depth. Water turbidity information or visual interpretation of the satellite image could be used to ensure quality of the satellite image before processing. Response from the team Calibration of the product with in situ measurements should be performed when possible. Screening of the potential images is possible. E.g., the poor quality of the Kotka image was known before the processing, but since alternative products were not available it was processed anyway

25 Update for Report on data quality and 4 Validation in Germany (EOMAP and BC) 4.1 Lakes in South Germany-MERIS vs. Landsat Satellite data validation In continuation of the validation conducted for the MERIS data in various lakes in South Germany in the deliverable D54.3, we compared the results of the Landsat 7 ETM+ processing products with the results of a set of selected lakes (Lake Constance station FU, Ammersee and Walchensee, see Figure 16). For the validation, we selected the available Landsat 7 ETM+ imagery with suitable cloud coverage for the years Figure 16. Lake Constance station FU, Ammersee and Walchensee

26 Update for Report on data quality and The in situ data set provided by the users of the State Institute for the Environment, Measurements and Nature Conservation of Baden-Wuerttemberg (LUBW) and the Bavarian Environment Agency (LfU) consists of monitoring measurements of Chl-a and Secchi depth. For the validation, the Secchi depth z s [m] was converted to TSM using the formula from Heege et al. (1998, p.25), which was adjusted with the results to TSM= 2.65*(1/ln z s -0.28), see for further details D54.3 p Data Comparison The derived Total Suspended Matter of MERIS and Landsat 7 ETM+ processing are visualized in Figure 17 for Lake Constance, in Figure 18 for Ammersee and in Figure 19 for Walchensee Summary of the results In Figure 17 and Figure 18 a reasonable coincidence of the MERIS (green points) and Landsat 7 ETM+ (red points) is demonstrated. The number of suitable Landsat 7 ETM+ images was limited, but with the launch of the new sensor Landsat 8 in February 2013 and the upcoming launch of the Sentinel 2, the number and hence the temporal resolution of suitable sensors in this spatial and spectral resolution class increases significantly. Figure 17. Total Suspended Matter Time series for Lake Constance, station FU, , with in situ data, MERIS and Landsat 7 ETM+ ( N, E)

27 Update for Report on data quality and Figure 18. Total Suspended Matter Time series for Lake Ammersee, , with in situ data, MERIS and Landsat 7 ETM+ ( N, E). Figure 19. Total Suspended Matter Time series for Walchensee , with in situ data, MERIS and Landsat 7 ETM+ ( N, E)

28 Update for Report on data quality and 4.2 Bavarian Rivers For the validation of the satellite based turbidity product in Bavarian Rivers, we analyzed the in situ data of suspended matter provided by the Bavarian Environment Agency LfU (production series S_cMess) in mg/m³ in comparison to the turbidity product derived from the MIP (EOMAP) processing of Landsat 7 ETM+ data, see Table 6 for the overview of the data set. The in situ data until end of 2008 are considered as tested/checked values, after this date the data have to be considered as raw data (comment by LfU). To collect a reasonable data set with coincident dates, we chose the stations Füssen for river Lech, Passau Ingling for river Inn and München for river Isar, see Figure 20 all stations and the selected ones in red Satellite data validation For the validation, EOMAP selected all available Landsat 7 ETM+ imagery with suitable cloud coverage for the years The satellite data have been processed with MIP-EWS for turbidity, sum of organic absorption, yellow substances and Z90 (signal penetration depth) together with quality and extracted metadata. As unit for the satellite turbidity product we introduce here the Earth Observation Turbidity unit ETU, based on backscattering properties Data comparison For several rivers and stations in Bavaria we compared the same day matches of in situ data with satellite data, see Figure 21 for river Lech, Figure 22 for river Inn and Figure 23 for river Isar. Most of the data also matches in the sampling time plus/minus half an hour. If the time differs too much, we considered the median in situ value of the day. As an exception, highly variable in situ values within a few hours have been sorted out Summary of the results The summary of the results will be analyzed together with the Rhine results in the next chapter (4.3.2.). Table 6: Temporal coverage and data sources of in situ and satellite data for the validation of Bavarian rivers. Years EO Data source EO Bavarian Rivers (130 scenes) Landsat 7 ETM+ Years in situ Stations in situ 17 Measurement Interval Daily single measurements (with exceptions), several per day, e.g. every 15 minutes (not continuous through the data set) Data source in situ Bavarian Environment Agency (LfU)

29 Update for Report on data quality and Figure 20. Overview of the all stations for Bavarian Rivers, stations selected for validation are marked in dark red/ bold. Figure 21. Comparison of Satellite derived turbidity and in situ measured Total Suspended Matter for river Lech at station Füssen ( N, E)

30 Update for Report on data quality and Figure 22. Comparison of Satellite derived turbidity and in situ measured Total Suspended Matter for river Inn at station Passau Ingling ( N, E ). Figure 23. Comparison of Satellite derived turbidity and in situ measured Total Suspended Matter for river Isar at station München ( N, E)

31 n Update for Report on data quality and 4.3 River Rhein (Germany) The German Federal Institute of Hydrology (BfG) has selected three stations of the river Rhine with continuous suspended matter measurement data to validate it against the satellite derived products. The in situ data has been provided in the form of suspended matter concentrations in mg/l for three stations Breisach, Plittersdorf and Maxau (see Figure 24) as daily measurement values and Table Satellite data processing For the validation, EOMAP selected all available Landsat 7 ETM+ imagery with suitable cloud coverage for the years The satellite data have been processed with MIP-EWS for turbidity, sum of organic absorption, yellow substances and Z90 (signal penetration depth) together with quality and extracted metadata Data comparison We plotted the in situ measured suspended matter and the satellite derived turbidity products in Figure 25, Figure 26 and Figure 27. Figure 24. River Rhine validation stations Breisach, Plittersdorf and Maxau together with the turbidity product from (in case of Plittersdorf and Maxau) and (in case of Breisach)

32 Update for Report on data quality and Table 7: Temporal coverage and data sources of in situ and satellite data for the validation of river Rhine. Years EO Data source EO Rhine (72 scenes) Landsat 7 ETM+ Years in situ Stations in situ 3 Measurement Interval Daily mean until July 2011 Data source in situ German Federal Institute of Hydrology (BfG) Figure 25. Comparison of Satellite derived turbidity and in situ measured Total Suspended Matter for river Rhine at station Maxau

33 Update for Report on data quality and Figure 26. Comparison of Satellite derived turbidity and in situ measured Total Suspended Matter for river Rhine at station Plittersdorf. Figure 27. Comparison of Satellite derived turbidity and in situ measured Total Suspended Matter for river Rhine at station Breisach

34 Update for Report on data quality and Summary of the results In situ data and satellite derived turbidity indicate the same temporal trends and dynamics for all three stations in the different Bavarian rivers and for the river Rhine stations. Due to different methodologies of the measures - satellite turbidity, in situ derived suspended matter (using again different methodologies) we observe quantitative differences, so each in situ methodology should be related to always the same satellite derived ETU. Figure 28 shows this when comparing different in situ stations with matching satellite derived turbidity: The calibration to in situ measured suspended matter vary for some stations. This may be due to different in situ methodologies, or also due to different optical backscattering properties (e.g. varying particle size distributions) specific to the different locations. We furthermore expect that the location of the in situ sampling point has a dominant impact for unsystematic difference: Locations close to the shore (which is the case in most of the analyze in situ stations) or closer to the seafloor are highly impacted by resuspension and localized hydrodynamic effects, and may not always represent the values of the main river volume. Another methodological difference is the sampling depth: The satellite based turbidity reflects the values close to the water surface (typically the first 30cm to 100cm in rivers), while the in situ sampling points are frequently deeper located User comments The results have not been presented to the users so far, but will be discussed at the upcoming user workshop in Munich on the 6 th of November Figure 28. Satellite derived turbidity vs. In Situ measured Total Suspended Matter for river Rhine and Bavarian rivers

35 Update for Report on data quality and 4.4 Lakes in Mecklenburg-Vorpommern In situ data The validation in Mecklenburg-Vorpommern (North-East of Germany) has been performed for MERIS water quality products and in situ measurements provided by the Ministerium für Landwirtschaft, Umwelt und Verbraucherschutz Mecklenburg-Vorpommern, Abteilung 4. The in situ measurements cover the years from and are taken from different lakes. Figure 29 shows the area of interest, while Figure 30 is zooming to the positions of the stations. Table 8 lists the stations we received data from and the years they are covering. The data included Secchi depths and chlorophyll concentration MERIS data MERIS FR data have been processed with the processing chain at BC, including an advanced pre-processing, water constituents retrieval with the FUB algorithm and a postprocessing/flagging for erasing invalid pixels. The processing has been performed on the calvalus cluster for the archived MERIS data, only processing the pixels around the measurement stations Time Series Extraction & statistics The chlorophyll concentration and KD values have been extracted around the stations using a 3x3 macro-pixel around the respective positions. The values within the macro pixels have been averaged after the removal of invalid pixels and outliers. Subsequently, the time series have been plotted for each station, sorted by lakes. Figure 32 demonstrates the resulting plots, which are presented in chapter for the different stations. Furthermore, lakes statistics have been calculated. Here, averages of the data at the different stations were calculated for the single years. Furthermore, the stations within one lake have been compiled for the retrieval of a yearly average. For the in situ data, these were 5 to 6 measurements per year, while the averages from the MERIS data have been received from up to 87 values, most of them between 30 and 50 per year. Match-up extraction of the exact measurement points provides the possibility to directly compare the pixel and point values. The derived scatter plots supply a first guess of the agreement of data, but should not be overestimated due to the constraints of comparing point and pixel data. Time differences of in situ and MERIS where up to 4 hours

36 Update for Report on data quality and Figure 29. Area of Interest covering the largest lakes in Mecklenburg-Vorpommern, North-East Germany. Figure 30. Position of in situ measurement stations of 4 lakes in Mecklenburg-Vorpommern; Background map: NatGeo_World_Map - National Geographic, Esri, DeLorme, NAVTEQ, UNEP- WCMC, USGS, NASA, ESA, METI, NRCAN, GEBCO, NOAA, ipc; image within the lakes: MERIS RGB Image from

37 Update for Report on data quality and Table 8. In situ stations covering 4 lakes within Mecklenburg-Vorpommern. Station Years with in situ Latitude Longitude Kölpinsee Tiefste Stelle 2003, Kölpinsee Westteil Malchiner See Südbecken 2003, Malchiner See Tiefste Stelle 2003, Müritz Außenmüritz Müritz Binnenmüritz Müritz Kleine Kuhle Müritz Kleine Müritz Müritz Klink Müritz Ostufer Müritz Röbeler Bucht Müritz Sietow Plauer See Leister Lank Plauer See Nordbecken Plauer See Seemitte Plauer See Tief Suckower Keller Plauer See Werdertief

38 Update for Report on data quality and (1) RGB TOA radiances (2) aerosol optical depth within the atmosphere (3) RGB water leaving reflectances (4) Chlorophyll concentration (5) excluding invalid pixels Figure 31. Results of the processing steps needed to retrieve water constituents from the signal measured at the satellite sensor. Figure 32. Time series plot of the chlorophyll concentration at the station Aussenmueritz for the years ; compiled from MERIS data (blue line) and in situ data (red dots)

39 Update for Report on data quality and Results Match-Up statistics The two following scatterplots (Figure 33 and Figure 34) show the relationship between in situ and satellite match-ups. There is a slightly better correlation if only the central pixel is used instead the average (and standard deviation) of a 3x3 macro-pixel. In general the satellite data show lower values than the in situ data, however, some cases occur where the satellite data have significant higher values than the in situ data (up to twice as high). No clear pattern could be found under which conditions this occurs. Figure 33. Scatterplot for match-ups between satellite and in-situ measurements (3x3 macropixels averages). Figure 34. Scatterplot for match-ups between satellite and in situ measurements (central pixel only)

40 Update for Report on data quality and Temporal Lake Statistics The following figures show the averages of the chlorophyll concentration, whereas Figure 35 shows the average from all stations within the Mueritz and Figure 36 shows the averages for the single stations. The stations Roebeler Bucht and Kleine Mueritz are not included in the overall average as they show clearly that the method is not suitable for those stations. They are located in very narrow areas of the lake, close to the shoreline. The averaged chlorophyll concentrations for the different years of the stations in the Plauer See (Figure 37) show slightly higher values compared to the Mueritz. The averages show good agreement between in situ and MERIS data, but for 2009 and 2011 the satellite data are higher. This pattern is mainly caused by the stations Nordbecken und Seemitte (Figure 38). Figure 35. Yearly average of the chlorophyll concentration retrieved from the values of the measurement stations within the Mueritz from in situ (red) and MERIS (blue); excluding the stations Roebeler Bucht and Kleine Mueritz

41 Update for Report on data quality and Figure 36. Comparison of the yearly averages of chlorophyll concentration at the different stations within the Mueritz, in situ: red; MERIS: blue. Figure 37. Yearly average of the chlorophyll concentration retrieved from the values of the measurement stations within the Plauer See from in situ (red) and MERIS (blue)

42 Update for Report on data quality and Figure 38. Comparison of the yearly averages of chlorophyll concentration at the different stations within the Plauer See, in situ: red; MERIS: blue Time Series Plots Mueritz In total, there are eight measurement stations within the Mueritz. T time series plots of four stations are shown below. The map on the left is showing the position of the respective stations. Best agreement between in situ and satellite chlorophyll data can be seen for the stations Kleine Kuhle, Außenmueritz, Mueritz Klink. An overall good agreement can be seen in the yearly development as well as the absolute levels of the chlorophyll concentration. The satellite data provide a larger number of measurements, but also showing a higher scatter of the data. The stations with good agreement are located in the central area of the lake. For stations located closer to the shoreline or within narrow areas of the lake, both measurements do not agree. One example is the station Roebeler Bucht where only few chlorophyll retrievals from MERIS are valid and show much too low values compared to the in situ measurements. For the Station Mueritz Ostufer many data could be extracted from the satellite data but showing a high scatter. This could be due to the fact that the station is located in an area where bottom reflection might occur, due to water conditions. Therefore, the identification and excluding of pixels with bottom reflection will be in the focus for future work

43 Update for Report on data quality and Figure 39. Time series plot for station Mueritz Klink showing chlorophyll values from derived from MERIS data (blue line) and in situ measurements (red dots). Figure 40. Time series plot for station Aussenmueritz showing chlorophyll values from derived from MERIS data (blue line) and in situ measurements (red dots). Figure 41. Time series plot for station Mueritz Kleine Kuhle showing chlorophyll values from derived from MERIS data (blue line) and in situ measurements (red dots)

44 Update for Report on data quality and Figure 42. Time series plot for station Mueritz Ostufer showing chlorophyll values from derived from MERIS data (blue line) and in situ measurements (red dots). Figure 43. Time series plot for station Roebeler Bucht showing chlorophyll values from derived from MERIS data (blue line) and in situ measurements (red dots). Plauer See There are five monitoring stations within the Plauer See, while suitable data could be extracted for 4 of them and only a few valid measurements are available for station Werdertief