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Project FRESHMON

Update for Report on FRESHMON data quality and

comparability

Issue/Rev 1.0 2013-10-31 FM_PH3_WP54_D543_Update_PR

2013-10-31 1

Title:

WP54 Calibration

and validation Update for Report on FRESHMON data quality and data comparability

Subtitle: WP 5.4, Deliverable D54.3_2

Related to: WP4

Prepared by: Finnish Environment Institute (SYKE)

Doc: FM_PH3_WP54_D543_Update_PR

Issue/Rev: 1.0

Date: 2013-10-31

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Involved Consortium Partners

Partner Who? Task/Role

SYKE Sampsa Koponen, Kari Kallio, Timo Pyhälahti, Jenni Attila, Hanna Piepponen, Vesa Keto

Coordination, Input

BC Kerstin Stelzer Input

EOMAP Karin Schenk, Thomas Heege, Sebastian Krah

Input

Document Status

Issue Date Who? What?

0.1 2013-05-30 Sampsa Koponen Initial version

0.2 2013-10-02 Kerstin Stelzer Input MV Lakes

0.5 2013-10-23 Sampsa Koponen Kari Kallio, Timo Pyhälahti, Jenni Attila, Hanna Piepponen

Input from Finland (SYKE) and Germany (EOMAP)

0.6 2013-10-25 Karin Schenk, Thomas Heege, Sebastian Krah

Input South German rivers and lakes

0.7 2013-10-28 Sampsa Koponen Compiled version

0.8 2013-10-29 Kari Kallio, Karin Schenk, Kerstin Stelzer

Input from Finland, comments for executive summary and conclusions

1.0 2013-10-31 Sampsa Koponen Final version

Reference Documents

Issue Date What?

1.0 2010-12-13 DOW

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Contents

List of Abbreviations ................................................................................................................................. 4

1 Scope of this document .................................................................................................................... 5

2 Executive Summary .......................................................................................................................... 5

3 Validation in Finland (SYKE) .............................................................................................................. 6

3.1 MERIS Chl-a .............................................................................................................................. 7 3.1.1 MERIS data processing ................................................................................................................... 7 3.1.2 Land and cloud masks .................................................................................................................... 9 3.1.3 In situ data ..................................................................................................................................... 9 3.1.4 Data aggregation and visualization .............................................................................................. 10 3.1.5 Effects of CDOM on Chl-a estimation .......................................................................................... 13 3.1.6 User comments ............................................................................................................................ 15

3.2 High resolution water depth products near Hanko and Kotka ..............................................19 3.2.1 Study area and satellite data ....................................................................................................... 19 3.2.2 Satellite data processing .............................................................................................................. 19 3.2.3 Results .......................................................................................................................................... 20 3.2.4 User comments ............................................................................................................................ 24

4 Validation in Germany (EOMAP and BC) ........................................................................................25

4.1 Lakes in South Germany-MERIS vs. Landsat ...........................................................................25 4.1.1 Satellite data validation ............................................................................................................... 25 4.1.2 Data Comparison ......................................................................................................................... 26 4.1.3 Summary of the results ................................................................................................................ 26

4.2 Bavarian Rivers .......................................................................................................................28 4.2.1 Satellite data validation ............................................................................................................... 28 4.2.2 Data comparison .......................................................................................................................... 28 4.2.3 Summary of the results ................................................................................................................ 28

4.3 River Rhein (Germany) ...........................................................................................................31 4.3.1 Satellite data processing .............................................................................................................. 31 4.3.1 Data comparison .......................................................................................................................... 31 4.3.2 Summary of the results ................................................................................................................ 34 4.3.1 User comments ............................................................................................................................ 34

4.4 Lakes in Mecklenburg-Vorpommern ......................................................................................35 4.4.1 In situ data ................................................................................................................................... 35 4.4.2 MERIS data ................................................................................................................................... 35 4.4.3 Time Series Extraction & statistics ............................................................................................... 35 4.4.4 Results .......................................................................................................................................... 39 4.4.5 Conclusions .................................................................................................................................. 46

5 Conclusions .....................................................................................................................................47

5.1 Quality of in situ data .............................................................................................................47

5.2 Quality of satellite products ...................................................................................................47

5.3 Quality of data from in situ devices .......................................................................................48

6 Validation lessons learnt ................................................................................................................49

References ..............................................................................................................................................50

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List of Abbreviations

Abbreviation Description

BfG German Federal Institute of Hydrology

Chl-a Chlorophyll a

EO Earth observation

ETM Enhanced Thematic Mapper

ESA European Space Agency

DOW Description of work (Document „DOW Initialled.pdf“)

LfU Bavarian Environment Agency

LUBW State Institute for the Environment, Measurements and Nature Conservation of Baden-Wuerttemberg

MIP Modular Inversion and Processing System

MERIS Medium resolution Imaging Spectrometer

PR Public Report (Document Type, public)

RGB Red Green blue

TOA Top of Atmosphere

WFD Water Framework Directive

WP Work Package

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1 Scope of this document

This document is an update to D54.3 “Report on FRESHMON data quality and data comparability”.

The original D54.3 described the results of the validation activities performed in FRESHMON until

October 2012. This document describes the validation done between November 2012 and October

2013. 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 FRESHMON partners and data

provided by user organizations.

Related FRESHMON-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 FRESHMON data quality and data comparability (delivered 10/2012)

describes the results of the validation effort until October 2012.

- 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 FRESHMON 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 FRESHMON 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.

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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.

Figure 1. Satellite data validation areas in Finland (indicated with red).

Southern Finland

Hanko

Kotka

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3.1 MERIS Chl-a

Finland has over 56 000 lakes (size > 1 ha) and the number of lake water bodies defined by the Water

Framework Directive (WFD) is almost 4300. 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 3rd phase of FRESHMON 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.

3.1.1 MERIS data processing

During the second phase of FRESHMON, 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-aCalib = 0.346* Chl-aSatellite + 3.76, (1) where Chl-aSatellite 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

0.346 (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 4.10.3):

1. AMORGOSS for improved geolocation

2. Radiometry correction (Smile, & Equalization, as the MERIS data was from the 3rd

reprocessing the Calibration step was not included)

3. FUB/WeW 1.2.8 (water quality processing)

4. Rectification

5. Land and cloud masking (see below)

6. Product generation

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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 high-

concentration) 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, 2006.

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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.

3.1.2 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 100 000 (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.

3.1.3 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. Chl-

a 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).

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3.1.4 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 2271. 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 4276. 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.

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(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.

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(a)

(b)

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

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0 10 20 30 40 50 60 70 80 90 100 1100

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Minimum number of MERIS pixels in the water body

Nu

mb

er

of w

ate

r b

od

ies

2102

391

1296

303

695

201266

83172

54

3

Normal landmask

Buffered landmask

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.

3.1.5 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.

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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 2000-2011 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

m

Humic lakes, medium-sized 154 4 97 84 9.5 2.4 7.3 2.1

Lakes with short water

retention

171 4 23 10 13.5 3.7 11.5 1.7

Humic lakes, shallow 912 21 22 9 19.0 6.8 8.5 1.3

Humic-rich lakes, shallow 772 18 26 11 23.1 5.5 17.0 1.2

Humic-poor lakes, shallow 265 6 15 5 8.1 2.8 3.3 2.5

Humic lakes, small 578 14 20 3 10.8 2.7 8.0 1.8

Lakes in N. Lapland 179 4 21 7 - - - -

Humus-rich lakes 320 7 35 23 16.3 3.0 16.3 1.3

Naturally nutrient rich lakes 185 4 32 14 47.8 20.5 7.0 0.8

Humic lakes, large 44 1 100 100 7.8 2.6 6.3 2.4

Humus-poor lakes, large 68 2 100 99 4.8 1.3 3.0 3.2

Humus-poor lakes, small

and medium-sized

618 14 37 21 5.6 1.4 2.9 3.4

Total 4266 100 30 16 - - -

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 2011. Chl-a was calculated from all MERIS images (3x3 pixels around the

monitoring station) and in situ measurements of August each year.

0 10 20 300

10

20

30

In situ Chl-a µg/l

ME

RIS

Chl-a µ

g/l

Humus-rich lakes

MERIS=5.8In situ= 18.2

N= 20R2= 0.00

0 10 20 300

10

20

30

In situ Chl-a µg/l

ME

RIS

Chl-a µ

g/l

Humic lakes, medium-sized

MERIS=7.9In situ= 9.4N= 49R2= 0.52

0 10 20 300

10

20

30

In situ Chl-a µg/l

ME

RIS

Chl-a µ

g/l

Humus-poor lakes, small&medium-sized

MERIS=5.3

In situ= 5.0N= 35R2= 0.70

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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.

3.1.6 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 FRESHMON to these comments.

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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 2013.

Lake and station number

Date (YYYYMMDD) Local time

Secchi depth (m)

Chl a (ug/l)

Turbidity (FNU)

aCDOM(400) (1/m)

TSM (mg/l)

Vesijärvi 1 20070604 11:20 2,9 3,7 3 1,76 2,6

Vesijärvi 2 20070604 13:10 3,7 2,9 2,3 1,38 2

Vesijärvi 3 20070807 11:10 2,9 4,6 1,9 1,1 2,2

Vesijärvi 4 20070807 12:20 4,6 1,7 1,1 0,9 1,2

Vesijärvi 5 20110609 11:15 2,8 5,4 2,4 1,57 3,4

Vesijärvi 6 20110609 12:30 3,1 5,1 2,3 1,52 3

Vesijärvi 7 20130905 11:30 2,4 11 3,6 1,80 2,5

Päijänne 1 20070604 1130 5 2,1 0,86 2,82 0,8

Päijänne 2 20070807 10:53 4,9 1,9 0,62 2,5 0,7

Säkylän Pyhäjärvi 1 20070823 11:05 3,5 5,3 1,7 1,6 2



Lammin Pääjärvi 1 20110629 10:10 2,3 3,2 1,3 9,63 1,9

Lammin Pääjärvi 2 20130618 09:45 2,2 3 1,7 12,10 1,2

Lammin Pääjärvi 3 20130823 10:40 2,1 1,59 0,76 11,23 0,9

Keravanjärvi 1 20130806 11:05 1,3 8,2 2,1 20,43 2,1

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0.4 0.5 0.6 0.7 0.8 0.9 1 1.10

2

4

6

8

10

12

14

16

R709

/R665

Ch

l-a

( g

/l)

Vesijärvi

Säkylän Pyhäjärvi

Päijänne

Lammin Pääjärvi

Keravanjärvi

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).

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Table 3. Topics for further research from D54.3.

Further step defined in D54.3 Status at the end of FRESHMON

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.

Not yet completed: The effects of lake type

(humic content) on the estimation of Chl-a have

been studied in Chapter 3.1.5 and the effects of

CDOM absorption are clear. Further analysis and

development is still necessary in order to

improve the estimation of Chl-a.

How to handle of large amounts of data together

with frequent cloud cover?

Partially solved: Automated cloud screening

reduces errors caused by clouds but strict rules

may also result in loss of valid data.

What kind of EO products are the most useful

for WFD monitoring?

Solved: Time series and histogram plots are

generated for WFD water bodies. Further

development is expected though.

Include AMORGOS for improved rectification. Solved: AMORGOS has been included in the

processing. The number of pixels affected by

land is reduced.

Further testing with the BOREAL LAKES

processor

Not yet completed: FUB processor was used for

Chl-a.

Table 4. User comments for Chl-a products delivered in phase 3.

User comments Response from the FRESHMON team

Large lakes are well covered with in situ

measurement. Information needs are largest

with small lakes.

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.

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.

The possibilities for this will be investigated

during the GLaSS project, which includes a task

“WFD reporting based on GLaSS products”.

Further info is needed on the validity of the data

in different lake types

Research into the effects of CDOM will be

continued during the GLaSS project

CDOM, turbidity, temperature could be used as

secondary parameters in the classification

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.

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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.

3.2.1 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 2008-2012 and values were corrected with sea level measured

by the Finnish Meteorological Institute.

The WorldView-2 images used in the analysis were taken on 29.4.2011 (Hanko) and 4.9.2012 (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.

3.2.2 Satellite data processing

EOMAP generated water depth product for 29.4.2011 (Hanko) and 4.9.2012 (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).

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3.2.3 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 29.4.2011 (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

4.9.2012 (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).

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Figure 10. Water depth model derived from satellite data for Hanko (29.4.2011) test site (SYKE

processing).

Figure 11. Water depth model derived from satellite data for a small part of Kotka (4.9.2012) test

site (SYKE processing).

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Figure 12. Modelled depth values (SYKE processing) compared to water depth measured in situ

(validation data). Left figure is from Hanko (29.4.2011) and right figure from Kotka (4.9.2012).

Figure 13. Water depth model derived from satellite data for Hanko (29.4.2011) test site (EOMAP

processing).

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Figure 14. Modeled depth values compared to water depth measured in situ from Hanko

(29.4.2011) 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 (4.9.2012) image.

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3.2.4 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 Response from the FRESHMON team

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.

Calibration of the product with in situ

measurements should be performed when

possible.

Water turbidity information or visual

interpretation of the satellite image could be

used to ensure quality of the satellite image

before processing.

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.

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4 Validation in Germany (EOMAP and BC)

4.1 Lakes in South Germany-MERIS vs. Landsat

4.1.1 Satellite data validation

In continuation of the validation conducted for the MERIS data in various lakes in South Germany in

the FRESHMON 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 2004-2012.

Figure 16. Lake Constance station FU, Ammersee and Walchensee

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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 zs [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 zs-0.28), see for further details D54.3 p. 45-62.

4.1.2 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.

4.1.3 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, 2004-2011, with in

situ data, MERIS and Landsat 7 ETM+ (47°37’26.4’’ N, 9°22’31.8’’ E).

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Figure 18. Total Suspended Matter Time series for Lake Ammersee, 2004-2011, with in situ data,

MERIS and Landsat 7 ETM+ (47°58’55.71’’ N, 11°7’20.62’’ E).

Figure 19. Total Suspended Matter Time series for Walchensee 2003-2011, with in situ data, MERIS

and Landsat 7 ETM+ (47°36’5.74’’ N, 11°20’46.66’’ E).

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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.

4.2.1 Satellite data validation

For the validation, EOMAP selected all available Landsat 7 ETM+ imagery with suitable cloud

coverage for the years 2004-2012. 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.

4.2.2 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.


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.

Bavarian Rivers

Years EO 2004-2012 (130 scenes)

Data source EO Landsat 7 ETM+

Years in situ 1984-2013

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)

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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 (47°33’57.32’’N, 10°42’1.06’’E).

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for river Inn at station Passau Ingling (48°33’40.57’’N, 13°26’40.51’’E ).


for river Isar at station München (48°8’43.26’’N, 11°35’48.45’’E).

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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 7.

4.3.1 Satellite data processing

For the validation, EOMAP selected all available Landsat 7 ETM+ imagery with suitable cloud

coverage for the years 2004-2012. 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.

4.3.1 Data comparison

We plotted the in situ measured suspended matter and the satellite derived turbidity products in

Figure 25, Figure 26 and Figure 27.

n

Figure 24. River Rhine validation stations Breisach, Plittersdorf and Maxau together with the

turbidity product from 2012-02-03 (in case of Plittersdorf and Maxau) and 2011-09-28 (in case of

Breisach).

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Table 7: Temporal coverage and data sources of in situ and satellite data for the validation of river

Rhine.

Rhine

Years EO 2004-2012 (72 scenes)

Data source EO Landsat 7 ETM+

Years in situ 1984-2011

Stations in situ 3

Measurement Interval Daily mean until July 2011

Data source in situ German Federal Institute of Hydrology (BfG)


for river Rhine at station Maxau.

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for river Rhine at station Plittersdorf.


for river Rhine at station Breisach.

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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.

4.3.1 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 6th of November 2013.

Figure 28. Satellite derived turbidity vs. In Situ measured Total Suspended Matter for river Rhine

and Bavarian rivers.

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4.4 Lakes in Mecklenburg-Vorpommern

4.4.1 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 2003 – 2011 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.

4.4.2 MERIS data

MERIS FR data have been processed with the FRESHMON processing chain at BC, including an

advanced pre-processing, water constituents retrieval with the FUB algorithm and a post-

processing/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.

4.4.3 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 4.4.4.3 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.

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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 26.03.2007.

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Table 8. In situ stations covering 4 lakes within Mecklenburg-Vorpommern.

Station Years with in situ Latitude Longitude

Kölpinsee Tiefste Stelle 2003, 2007 53.503366 12.582082

Kölpinsee Westteil 2003 53.507532 12.523838

Malchiner See Südbecken 2003, 2007 53.674672 12.599639

Malchiner See Tiefste Stelle 2003, 2007 53.698372 12.628530

Müritz Außenmüritz 2003 - 2011 53.415814 12.698690

Müritz Binnenmüritz 2003 - 2011 53.511010 12.669476

Müritz Kleine Kuhle 2003 - 2011 53.373696 12.707307

Müritz Kleine Müritz 2003 - 2011 53.330402 12.702991

Müritz Klink 2003 - 2011 53.476465 12.639179

Müritz Ostufer 2003 - 2011 53.431278 12.718314

Müritz Röbeler Bucht 2003 - 2011 53.393362 12.630110

Müritz Sietow 2003 - 2011 53.449233 12.630644

Plauer See Leister Lank 2003 - 2011 53.498514 12.272821

Plauer See Nordbecken 2003 - 2011 53.510922 12.317700

Plauer See Seemitte 2003 - 2011 53.449992 12.298436

Plauer See Tief Suckower Keller 2003 - 2011 53.416120 12.291269

Plauer See Werdertief 2003 - 2011 53.492475 12.343200

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(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 2003 – 2011; compiled from MERIS data (blue line) and in situ data (red dots).

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4.4.4 Results

4.4.4.1 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).

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4.4.4.2 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.

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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).

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Figure 38. Comparison of the yearly averages of chlorophyll concentration at the different stations

within the Plauer See, in situ: red; MERIS: blue.

4.4.4.3 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.

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Figure 39. Time series plot for station Mueritz Klink showing chlorophyll values from 2003 – 2011

derived from MERIS data (blue line) and in situ measurements (red dots).

Figure 40. Time series plot for station Aussenmueritz showing chlorophyll values from 2003 – 2011


Figure 41. Time series plot for station Mueritz Kleine Kuhle showing chlorophyll values from 2003 –

2011 derived from MERIS data (blue line) and in situ measurements (red dots).

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Figure 42. Time series plot for station Mueritz Ostufer showing chlorophyll values from 2003 –

2011 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 2003 – 2011


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.

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Figure 44. Time series plot for station Plauer See Seemitte Keller showing chlorophyll values from

2003 – 2011 derived from MERIS data (blue line) and in situ measurements (red dots).

Figure 46. Time series plot for station Plauer See Suckower Keller showing chlorophyll values from


Figure 45. Time series plot for station Plauer See Nordbecken showing chlorophyll values from


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Figure 47. Time series plot for station Plauer See Werdertief showing chlorophyll values from 2003

– 2011 derived from MERIS data (blue dots) and in situ measurements (red dots).

4.4.5 Conclusions

The above demonstrated comparisons between in situ measurements and satellite based retrieval of

chlorophyll concentration shows the overall good agreement of both methods – where applicable -

and the complementarity of the techniques. The opportunities that can be retrieved from a

combined usage and analysis would provide a very valuable data set for lake monitoring.

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5 Conclusions

5.1 Quality of in situ data

SYKE:

In Finland, the Chl-a in situ data came from the national monitoring programme. Since the analyses

are done in certified laboratories, we assume the quality of in situ data to be high. However, the

locations of the stations are not always optimal for EO validation and simultaneous match-ups are in

practice only possible at automatic stations.

EOMAP:

The situ data differs a lot in terms of temporal resolution, e.g. daily measurements vs. two-weekly or

monthly measurements. Furthermore, we receive either daily mean values or exact measurement

times as input, which have to be compared. For one example we only received non-calibrated data

from a specific date onwards, therefore the validation has to be considered with caution. In a lot of

cases, we have not found enough matching dates to validate the products in an effective way. As we

have demonstrated in D54.3, the time difference can play an important role in the comparability of

the measurements.

BC:

The in situ data and the procedure of sampling are dedicated for the monitoring requirements of

Mecklenburg-Vorpommern. The sampling was not dedicated to be suitable for the validation of

satellite data. While using these data for validation, we perform a validation fit of purpose showing

the users how the satellite derived chlorophyll values agree with their monitoring measurements.

5.2 Quality of satellite products

SYKE:

According to the validation results the MERIS Chl-a product works well in non-humic lakes. In humic

lakes the EO method underestimates the concentrations. The magnitude of the effect is still unclear

and this caused some concerns with the users. The effects of CDOM on Chl-a estimation will be

studied further in the GLaSS project.

The limited resolution of MERIS (300 m) is a problem as it cannot be used with small lakes where the

information needs are the largest.

The water depth products performed well for depths less than 2 to 3 m.

EOMAP:

For the high resolution products, the results are comparable with the moderate resolution products.

Therefore, the satellite product can –again- be considered as a harmonized product, independent

from the sensor resolution.

The number of validated Landsat 7 ETM+ images was limited. But the satellite products reflect the

temporal trends and dynamics of the in situ measured suspended matter quite well. With the launch

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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.

BC:

The processing of the MERIS products has been performed with the FRESHMON processing chain

which is based on the FUB processor without any local adjustment or calibration. Thus, the good

agreement to the in situ data is very promising and the processing seems to be very suitable for

these kind of lakes. The spatial resolution of the data is the limiting factor and only stations with a

certain distance to land were suitable for the comparison. On the other hand, the high temporal

resolution of the MERIS data provides very valuable additional information for monitoring aspects

and / or scientific questions.

5.3 Quality of data from in situ devices

The quality of data from in situ devices was not included in this update.

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6 Validation lessons learnt

Important issues to take into account when comparing satellite and in situ measurements are:

Qualitative temporal trends and dynamics are more in focus than quantitative comparisons

of values when generated with different methodologies. This is mainly due to the lack of

simultaneous match-ups.

Possible time coincidence of the measurements in areas with highly variable conditions, e.g.

rivers or river inflows, is highly recommended when comparing same day measurements.

Location of the sampling stations, e.g. near the shore, can influence the results.

Different sampling depths occur in most cases when comparing satellite with in situ

measurements.

Feasibility check of suitable images (turbidity, atmospheric conditions) for water depth

processing.

Pixel wise quality control (the quality levels of data points in result images are indicated with

e.g. different symbols) of the satellite product plays an important role for the validation.

Temporal vs. spatial resolution needs to be demonstrated to the users and the advantages/

disadvantages needs to be discussed especially with respect to their requirements.

Further aggregation of products may bring the products closer to the requirements of the

users and the validation results also are less sensitive to single pixel to point comparisons.

In situ data which would follow the recommendations for validation purposes are not always

available. Routine in situ data is often acquired with different focus. Nevertheless, our

validation activities show that it is possible to compare the two data sources in useful way.

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References

Albert, A. and Mobley, C.D., “An analytical model for subsurface irradiance and remote sensing

reflectance in deep and shallow case-2 waters,” Optics Express 11(22), 2873-2890 (2003)

Dekker, A.G. and Peters, S.W.M., The use of the Thematic Mapper for the analysis of eutrophic lakes:

A case study in The Netherlands. International Journal of Remote Sensing, vol. 14, no. 5, pp. 799-821,

1993.

Gitelson, A., Garbuzov, G., Szilagyi, F., Mittenzwey, K-H., Karnieli, K., and Kaiser, A., Quantitative

remote sensing methods for real-time monitoring of inland waters quality. International Journal of

Remote Sensing, vol. 14, no. 7, pp. 1269-1295, 1993.

Kallio, K. 2006. Optical properties of Finnish lakes estimated with simple bio-optical models and

water quality monitoring data. Nordic Hydrology 37: 183-204.

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