WP54.3.1Report on FRESHMON data quality and data comparability

Transcription

WP54 Calibration and
validation
Title:
Report on FRESHMON data quality and data comparability
Subtitle:
WP 5.4, Deliverable D54.3
Related to:
WP4
Prepared by:
Finnish Environment Institute (SYKE)
Doc:
FM_PH2_WP54_D543_PR
Issue/Rev:
1.0
Date:
2012-10-31
Project
FRESHMON Report on FRESHMON data quality and comparability
Issue/Rev 1.0
2012-10-31
FM_PH2_WP54_D543_PR
Involved Consortium Partners
Partner
Who?
Task/Role
SYKE
Sampsa Koponen
Coordination, Input
SYKE
Kari Kallio
Input
WI
Annelies Hommersom
Input
EAWAG
Jaime Pitarch
Input
EOMAP
Karin Schenk, Sebastian
Krah, Thomas Heege
Input
Document Status
Issue
Date
Who?
What?
0.1
2012-10-05
Sampsa Koponen
Initial version
0.2
2012-10-22
Sampsa Koponen
Input from WI and EAWAG
0.3
2012-10-26
Sampsa Koponen
Updated version
0.4
2012-10-29
Sampsa Koponen
Input from EOMAP
1.0
2012-10-31
Sampsa Koponen
Final version
Reference Documents
Issue
Date
What?
1.0
2010-12-13
DOW
Contents
List of Abbreviations ................................................................................................................................... 4
1 Scope of this document ....................................................................................................................... 5
2 Executive Summary ............................................................................................................................. 5
3 Objectives of validation WP ................................................................................................................ 5
4 Validation in Finland (SYKE) ................................................................................................................. 7
4.1
MERIS chl-a ................................................................................................................................... 8
4.1.1
4.1.2
4.1.3
4.1.4
4.1.5
4.1.6
4.2
In situ data ......................................................................................................................................................... 8
MERIS data processing ...................................................................................................................................... 9
Data comparison ............................................................................................................................................. 10
Data visualization and analysis ........................................................................................................................ 10
User comments ............................................................................................................................................... 13
Further steps ................................................................................................................................................... 16
Rapid Eye turbidity ..................................................................................................................... 16
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4.2.2
4.2.3
4.2.4
4.2.5
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In situ data ....................................................................................................................................................... 16
Rapid Eye data processing ............................................................................................................................... 16
Data comparison ............................................................................................................................................. 17
User comments ............................................................................................................................................... 21
Further steps ................................................................................................................................................... 22
4.3
Validation of WISP-3 .................................................................................................................. 23
4.4
Validation of Secchi3000 ............................................................................................................ 26
5 Validation in the Netherlands (WI).................................................................................................... 28
5.1
Introduction................................................................................................................................ 28
5.2
Optical measurement campaigns and other in situ data available for validation ..................... 28
5.3
Other in situ data for satellite validation ................................................................................... 29
5.4
Methods ..................................................................................................................................... 29
5.5
In situ versus WISP-3 measurements ......................................................................................... 30
5.6
WISP-3 versus satellite results ................................................................................................... 32
5.7
User comments .......................................................................................................................... 34
5.8
Further steps .............................................................................................................................. 35
6 Validation in Germany (EOMAP and EAWAG)................................................................................... 36
6.1
Lake Constance Field campaign 2012 ........................................................................................ 36
6.1.1
6.1.2
6.1.3
6.1.4
6.1.5
6.2
6.2.1
6.2.2
6.2.3
6.2.4
6.2.5
6.3
6.3.1
6.3.2
6.3.3
6.3.4
6.4
6.4.1
6.4.2
6.4.1
6.4.2
Satellite data validation ................................................................................................................................... 36
WISP-3 validation ............................................................................................................................................ 42
Summary of the results ................................................................................................................................... 44
User comments ............................................................................................................................................... 44
Further steps ................................................................................................................................................... 45
Lake Constance Time series ....................................................................................................... 45
Description of the data sources ....................................................................................................................... 45
Satellite data processing .................................................................................................................................. 46
Data comparison of satellite data with in situ data ......................................................................................... 46
User comment ................................................................................................................................................. 53
Further steps ................................................................................................................................................... 53
Bavarian Lakes ............................................................................................................................ 53
Data comparison ............................................................................................................................................. 55
User comment ................................................................................................................................................. 62
Further steps ................................................................................................................................................... 63
River Elbe .................................................................................................................................... 63
Data comparison ............................................................................................................................................. 63
User comment ................................................................................................................................................. 65
Further steps ................................................................................................................................................... 65
7 Validation in Switzerland (EOMAP) ................................................................................................... 66
7.1
Lake Zurich ................................................................................................................................. 66
7.1.1
7.1.2
7.1.3
Data comparison ............................................................................................................................................. 66
User comment ................................................................................................................................................. 69
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8 Conclusions ........................................................................................................................................ 70
8.1
Quality of in situ data ................................................................................................................. 70
8.2
Quality of satellite products ....................................................................................................... 70
8.3
Quality of data from in situ devices ........................................................................................... 71
References ................................................................................................................................................ 71
Appendix ................................................................................................................................................... 73
List of Abbreviations
Abbreviation
Description
ASTER
Advanced Spaceborne Thermal Emission and Reflection Radiometer
EO
Earth observation
DOW
Description of work (Document „DOW Initialled.pdf“)
FWC
SYKE Fresh Water Centre
GMES
Global Monitoring of Environment and Security
LUBW
State Institute for the Environment, Measurements and Nature Conservation of
Baden-Wuerttemberg
MIP
Modular Inversion and Processing System
MODIS
Moderate resolution Imaging Spectrometer
MERIS
Medium resolution Imaging Spectrometer
PR
Public Report (Document Type, public)
SP
Service Provider
WP
Work Package
WVZ
Zurich Water Supply
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Scope of this document
This document describes the results of the validation activities performed in FRESHMON. This includes
results for satellite instruments (MERIS, MODIS, Rapid Eye, SPOT, ASTER) and in situ devices (WISP-3,
Secchi3000). The in situ data used in the validation includes measurements done by FRESHMON
partners and data provided by user organizations.
Related FRESHMON-documents:
2
-
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.
-
D52.1 Report on Case Studies for practicability (to be delivered 10/2012) will describe in detail
the in situ measurements made by EAWAG for the Lake Constance Field Campaign.
Executive Summary
For FRESHMON, satellite products have been validated in Finland (MERIS and Rapid Eye), Germany and
Switzerland (ASTER, SPOT and MODIS), and the Netherlands (MERIS and MODIS). The results show that
it is still quite difficult to obtain enough good quality in situ data for satellite product validation.
Specific campaigns (see e.g. the Lake Constance campaign in Chapter 6.1) can provide many data
points but if there is cloud cover the data in unusable. Automated stations making continuous
measurements can provide data that is not as sensitive to cloud cover but their spatial coverage is
limited and the data must be used with care (see e.g. Chapter 4.1.1).
In many cases the validation analysis shows that the satellite products have high quality. However,
further development and validation is still necessary in order to satisfy user needs.
The validation of in situ devices (WISP-3 and Secchi3000) show a similar situation: In some cases the
devices work well but further work is needed in order to improve the performance.
3
Objectives of validation WP
The FRESHMON Description of work (DoW) specifies the following objectives for WP54:

To implement several field measurement campaigns using spectrometers and other optical
property measurement devices for assessing FRESHMON and Core service quality

To implement physical calibration network operations for acquiring validation data
Table 1 shows the tasks related to these objectives and comments about their current status.
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Table 1. Tasks related to the objectives of WP54 (from DoW).
Task
Status/comment
Organization and realization of project specific
measurement campaigns, especially for the technical
aspects of EO data validation:
 In situ water sampling and automated, continuous
stationary measurement stations
 Measurement campaigns performed, continuous
data from stationary measurement stations
obtained
 Flow-through and other systems for transect
measurements of water quality
 Transect data obtained from 2011. Data from
earlier years also available
 Spectrometers and other devices for measuring
water optical properties
 Measurements with WISP-3 and ASD
spectrometers
Assessment study of data quality and uncertainties
with respect to different data sources from different
organizations
See Chapter 8
Development of harmonization procedures for ground
truth data
WISP-3 has been used in three countries
Report on final ground truth data evaluation
This report
Perform measurements with Secchi3000 devices
See chapter 4.4
Perform measurements with WISP-3
See chapters 4.3, 5 and 6.1
Secchi3000 and WISP-3 instruments are available to
project partners for the duration of the project to
perform own field measurements
WISP-3 devices were used by WI, SYKE and EAWAG.
Secchi3000 devices were used by SYKE and WI
Secchi3000, WISP-3 and other available instruments
are connected to operate as a sensor network for Lake
IJsselmeer and the Finnish Lake Vesijärvi
A WISP-3 was installed to Lake Greifensee (see D54.1)
in 2011. A test involving over 100 Secchi3000 users in
Finland in 2012.
Validation data will be made available for validation
activities outside the project
See D54.2
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Validation in Finland (SYKE)
In Finland the validation of satellite products took place in the areas shown in Figure 1. MERIS chl-a
products were developed with continuous raft data measured at Lake Säkylän Pyhäjärvi and tested at
Lake Puruvesi. Turbidity products with Rapid Eye were compared against in situ measurements at a
coastal area of Gulf of Finland (Tvärminne).
The testing and validation of WISP-3 took place in Southern Finland (see D54.2 for details on the
locations of the measurements).
Puruvesi
Säkylän
Pyhäjärvi
Tvärminne
Figure 1. Satellite data validation areas in Finland (indicated with red circles).
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4.1 MERIS chl-a
The quality of water at Lake Säkylän Pyhäjärvi (in South Western Finland) has been extensively studied
for many years with a variety of measurement methods. The testing of the use of remote sensing
started in 2006. Since 2008 a raft with continuously operating measurement equipment has been
installed on the lake every summer. Due to its large size, roundish shape and availability of daily in situ
chl-a and turbidity measurements the lake is an excellent development and validation site for remote
sensing methods.
MERIS data used in this analysis were provided by ESA.
This chapter describes the steps taken to process the satellite and in situ data and shows how well the
results match.
4.1.1
In situ data
Raft
The instruments installed on the raft make continuous measurements with one hour interval at the
depth of 1.5 m. The measurements are transmitted once per day for further processing through a GSM
modem.
Parameters:
-
Chl-a (TRIOS fluorometer)
-
Turbidity (S::can Nitro::lyser)
-
Temperature (S::can Nitro::lyser)
During 2009 the raft was in the northern part of the lake (depth 20 m) near the lake deep. In 2011 it
was moved to the southern part of the lake. In this area the effects of river inflow on water quality are
stronger. The water depth at this site is 5 m.
The apparent daily variation of chl-a concentration visible in the original raft data (Figure 2) is at least
partly caused by changes in the fluorescence efficiency of phytoplankton cells (known as quenching,
and is caused by photo-inhibition due to an excess of light). This causes the measured day time
concentrations to be too low, particularly in sunny conditions. In the modified data this effect is
reduced by interpolating the day time values from night time data. These modified values are used in
the comparison with satellite products.
Laboratory
Water samples were collected from the raft at regular (about every 2 weeks) intervals at the depth of
1.5 m. The samples were analysed in a laboratory with the following determination methods:
-
Chl-a: spectrophotometric determination after extraction with hot ethanol, ISO 10260, GF/C
filter
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Turbidity: nephelometric method, based on the measurement of light (860 nm) scattered
within a 90° angle from a beam directed at the water sample, with formazine used as a
standard matching solution (EN 27027)
All these data were provided by the user SYKE FWC.
4.1.2
MERIS data processing
The processing steps for MERIS data (with BEAM 4.10.3) were the following:
1. Radiometry correction (Calibration, Smile, & Equalization)
2. ICOL 2.10 Snapshot (adjacency correction)
3. FUB/WeW 1.2.8 (water quality processing)
4. Rectification
The postprocessing included:
-
Extraction of the pixel and flag values from the location of the raft (3 by 3 pixel window).
12
Original data
Modified data
10
Chl-a (g/l)
8
6
4
2
0
145
150
155
160
Julian day
Figure 2. Variation of chl-a values estimated with a fluorometer at the raft of Lake Säkylän Pyhäjärvi in 2011
and the modified chl-a values used in the comparison with satellite data.
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Data comparison
Figure 3 shows a scatter plot of the chl-a value estimated with the FUB processor and the chl-a value
measured by the raft instruments. The equation for the trend line shows that the EO result is
overestimated (this has also been observed in previous data comparisons). However, the correlation is
still reasonably high which indicates that by calibrating the FUB result with the raft data, a good
satellite estimate can be obtained. The scatter plot shows that there is a difference between the years
2009 and 2011. The satellite estimates in 2011 are clearly lower than in 2009 for the same in situ value.
The reason for this is not clear at the moment. As explained above the raft was moved to a different
place and this can be part of the explanation. If data only from year 2009 is used the R2 increases to
0.92 and the trend line equation changes to Y = 0.44 X + 0.99. However, all results presented below
have been made with the trend line correction computed with data including year 2011.
Figure 4 shows a time series of chl-a measured with the raft fluorometer, from laboratory samples and
with MERIS (after applying the calibration equation shown in Figure 3). As can be seen, the MERIS
values follow the seasonal changes visible in the raft and laboratory chl-a concentrations quite well.
MERIS is able to estimate correctly the higher concentrations during the spring blooms (early June) and
the bloom in mid-June and the lower concentrations after the spring bloom and mid-June bloom.
Increased concentrations are also detected during the cyanobacteria season (August-September 2009
and 2011).
4.1.4
Data visualization and analysis
Figure 5 shows the MERIS results as chl-a maps. The maps show how the concentrations within the
lake can vary considerably especially during blooms. Pixels influenced by land have been masked by
using a mask computed from shore line data.
Often a user is interested in knowing what the mean chl-a value of the lake is instead of looking at
maps. Table 2 shows the mean value of all MERIS pixels for year 2009 for each day and the mean of all
days. Table 3 shows the median chl-a values with EO and different in situ methods.
If the user is interested in seeing e.g. the variation of chl-a within the lake it is possible to compute
histograms from the satellite images. The example in Figure 6 shows how the bloom events are visible
in the histogram.
The method applied for Lake Säkylän Pyhäjärvi was also tested at Lake Puruvesi (located in Eastern
Finland). This lake has very clear water (chl-a during the summer typically close to 1 g/l). In some
areas (e.g. a bay caller Ristilahti) cyanobacteria blooms have occurred in the recent years. Figure 7
shows the MERIS chl estimate of the lake in 4 occasions. In situ data were not available on the dates of
the MERIS overpasses so only qualitative validation was possible. The MERIS algorithm correctly
estimates low values in most parts of the lake. In the bay where cyanobacteria have occurred, the
estimated concentrations are higher (just as they should be). Thus, the method appears to work well
also for this lake.
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R2 = 0.658
2009
2011
N = 15
16
RMSE = 2.56
14
Raft Chl-a (g/l)
y = 0.346*x + 3.76
12
10
8
6
4
2
0
0
5
10
15
20
25
30
35
40
45
MERIS FUB & ICOL Chl-a (g/l)
Figure 3. Comparison of MERIS chl-a estimate vs. raft chl-a measurement at Lake Säkylän Pyhäjärvi. The
vertical lines over each data point show the minimum and maximum MERIS value within a 3 by 3 pixel area
surrounding the raft. The vertical lines show the minimum and maximum raft measurement during the day of
the satellite overpass (before correcting for the effects of quenching).
20
14
Raft
Laboratory
MERIS (FUB&ICOL)
MERIS Min-Max
18
16
Raft
Laboratory
MERIS (FUB&ICOL)
MERIS Min-Max
12
14
Chl-a (g/l)
Chl-a (g/l)
10
12
10
8
8
6
6
4
4
2
0
1.5.
1.6.
1.7.
1.8.
1.9.
1.10.
Date
2
1.6.
1.7.
1.8.
1.9.
1.10.
Date
(a) 2009
(b) 2011
Figure 4. Time series of chl-a estimated with raft instruments, from laboratory samples and with MERIS at
Lake Säkylän Pyhäjärvi.
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Figure 5. Chl-a maps of Lake Säkylän Pyhäjärvi estimated with MERIS. The black line indicates the shore line of
the lake. The grey land mask has been computed from the shoreline with a criterion that non-masked pixels
cannot contain any land areas. The locations of the raft in 2009 and 2011 are indicated with a black and white
arrow, respectively, in the 1st image. The length of the lake is 25 km.
Table 2. Mean daily chl-a estimated from MERIS images at Lake Säkylän Pyhäjärvi in 2009.
Date
(MM.DD)
06.01
06.08
06.18
06.21
06.26
07.06
08.22
09.14
All days
Chl-a (g/l)
4.9
7.6
16.9
14.5
5.9
3.8
9
9.3
9.7
Table 3. Yearly median chl-a values (g/l) with EO and in situ methods. For MERIS, N is the total number of
pixels for all days used in the analysis.
Year
MERIS
Raft
Routine =
Control at raft
Note
lake deep
2009
8.1 (N=11744)
6.3 (N=2927)
4.5 (N=9)
6.3 (N=8)
Raft at lake deep
2011
5.2 (N=10276)
6.8 (N=2927)
5.5 (N=5)
7.2 (N=7)
Raft in SE part of the lake
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2500
20090601
20090608
20090618
20090621
20090626
20090706
20090822
20090914
All days
Number of pixels
2000
1500
1000
500
0
0
5
10
15
20
25
Chl a (g/l)
Figure 6. Chl-a histograms of Lake Säkylän Pyhäjärvi on eight days during summer 2009 using MERIS data.
Figure 8 shows the chl-a maps of Lake Pyhäjärvi for two days without the land mask. By comparing the
pixel values with the shore line it is possible to estimate the accuracy of the rectification. Typically, the
rectification error is less than one pixel and the land mask will remove the pixels affected by land. The
use of the AMORGOS software for improving the rectification accuracy is being investigated.
4.1.5
User comments
The comments from the user SYKE Fresh Water Centre are in document:
FRESHMON_User_feedback_and_validation_Report_SYKE_FWC_20121026.doc
Here are the main points raised by the user:
-
User needs:
o The service meets the demand when planning new approaches for the lake and coastal
water quality monitoring, in large spatial scales. These new approaches still need a
considerable amount of work (standardization, verification, comparability etc.) before
operational use.
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Figure 7. Chl-a maps of Lake Puruvesi (in Eastern Finland) estimated with MERIS. The sub-basin known as
Ristilahti is indicated with a black arrow in the last image.
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Figure 8. Rectification accuracy of MERIS data. The black line indicates the shoreline.
-
Important improvements
o Further efforts to reduce the so called ‘coastal errors’, and further efforts to test the
product in varying types of lakes (see also general comments)
-
Spatial/temporal coverage
o In large lakes it is sufficient, but in smaller ones the spatial resolution could be higher if
possible.
o The lakes with a high number of islands provide an extra challenge.
o Product is not ready for humic lakes, because chlorophyll a is not estimated well
enough.
o Temporal coverage may not be sufficient enough if there is too much cloudiness in
summer period.
-
Product quality and validity:
o Yes, it is ok for us. If we think of citizens as users of the products, the colour scales of
the water quality maps should be thought carefully: e.g. to separate somehow between
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‘normal’ springtime algae blooms and harmful blue-green algae blooms typically
occurring in late summer.
-
General comments
o Use of real-time lake float data sets seems to be an efficient tool in validation of the
results. These activities should be expanded to variable types of lakes (with varying
eutrophy, humus content etc.)
o Water quality data banks of SYKE include large amounts of spatial and temporal
independent data which can be used for product validation. There are numerous small
lakes in Finland that are not sampled at all. By grouping similar lakes and using sample
data to calibrate the satellite products the coverage of monitoring can be improved.
4.1.6
Further steps
In the next phase, solutions to the following problems/questions will be investigated:
1. 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.
2. How to handle of large amounts of data together with frequent cloud cover?
3. What kind of EO products are the most useful for WFD monitoring?
4. Include AMORGOS for improved rectification.
5. Further testing with the BOREAL LAKES processor
4.2 Rapid Eye turbidity
The objective here was to see if satellite data could provide useful information to a user interested in
mapping coastal fish reproduction habitats.
4.2.1
In situ data
The measurements performed by the user RKTL are described in FM_PH2_WP54_D542_PR
(Radiometric and in situ measurements of the ground truth for assessing the services).
4.2.2
Rapid Eye data processing
Two high-resolution sensors were used for processing the Finnish Bay Area:
1. RapidEye: 5m resolution (5 channels used: 440nm - 850nm)
2. SPOT 4: 20m resolution (4 channels used: 500nm - 1750nm)
For processing of the optical multispectral satellite data, the Modular Inversion and Processing System
(MIP) was used. MIP is designed for the physically based recovery of hydro-biological parameters from
multi- and hyperspectral remote sensing data and used for the environmental mapping of aquatic
shallow and deep waters of inland waters, coastal zones and wetlands. The architecture of the
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program binds a set of general and transferable computational schemes in a chain, connecting biophysical parameters with the measured sensor radiances.
For the processing, a Water-Land mask was necessary. This mask defines both a transition zone
between water and land pixel and is necessary for removing adjacency effects of land surface on water
areas. Aerosol Optical Thickness hat to be restricted which lead to an error in calculation of Yellow
Substances both for RapidEye and SPOT. Because of atmospheric conditions and sun glitter, retrieved
values shall be treated with care.
The first versions of Rapid Eye turbidity and Secchi depth estimates were delivered on Aug 30, 2012. A
comparison of RapidEye Total Suspended Matter results with in-situ measurement showed an
underestimation of RapidEye. After receiving comments from the user (see chapter 4.2.4) the data
were reprocessed. By reducing the scattering coefficient to 50 %, the TSM results could be adjusted to
in-situ data. The results shown below are based on the data from the second processing. Due to a
wrong retrieval of water constituents, a recalibration of SPOT was necessary. SPOT's TSM results were
not compared with in-situ data.
As a quality measure, the quality of each pixel was calculated taken into account several factors like
sunglint, solar angle, spacecraft angle, AOT influence, shallow water, transition zones, residuum of the
retrieval, processor warnings and concentrations on upper limit. Threshold values define distinct values
when a parameter is assumed to influence the quality. If the quality falls below a certain threshold
value, the pixels are flagged with a specific greyvalue (253) to indicate that these pixels are not reliable.
Pixels in the RapidEye scene were flagged due to shallow water risk and retrieval / processor warning.
4.2.3
Data comparison
Figure 9 shows a comparison of Rapid Eye turbidity vs. in situ turbidity while Figure 10 shows turbidity
maps. As can be seen flags are raised in most areas where turbidity is high. R2 is not very high but the
RMSE remains relatively low. The largest errors are found with the in situ data collected two day
before the image acquisition (June 18th) so it is likely that time difference affects the results. Figure 11
shows the values of in situ and satellite data at the times of the in situ data measurements. The
differences between in situ and satellite data values tend to increase also here, when the time
difference increases.
Figure 12 shows comparison of Rapid Eye Z90 vs. in situ Secchi depth. The Z90 value has been
multiplied with 2 in order to have a better match with the in situ data (this factor has had similar values
also in other cases). The correlation is quite good, the slope is close to 1 and bias term is small. Thus,
Rapid eye is able to estimate Secchi depth well. Also the effect of time difference appears to be
smaller. Figure 13 shows a Secchi depth map computed from Rapid Eye Z90 data.
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In situ turbidity (NTU)
10
9
N = 56
8
R2 = 0.61
7
RMSE = 1.5
6
Y = 0.69*X +0.5
5
4
June 18
June 19
June 20
June 21
(1:1)
Trendline
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Rapid Eye turbidity (NTU)
(a) All data
In situ turbidity (NTU)
10
9
N = 42
8
R2 = 0.22
7
RMSE = 0.96
6
Y = 0.49*X +0.96
5
4
June 18
June 19
June 20
June 21
(1:1)
Trendline
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Rapid Eye turbidity (NTU)
(b) Flagged data points removed
Figure 9. Rapid Eye turbidity vs. in situ turbidity in Tvärminne area in June 2012 (second version data). In (a)
all data points are shown. In (b) the data points that have been flagged by the processor have been removed.
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(a) Without flags
(b) With flags
Figure 10. Rapid Eye turbidity map on June 20, 2012 of the Tvärminne area (second version data).
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Figure 11. Rapid Eye turbidity vs. in situ turbidity on June 20, 2012 in the Tvärminne area (second version
data).
In situ Secchi depth (m)
9
8
N = 56
7
R2 = 0.71
6
RMSE = 0.9
5
Y = 1.1*X -0.04
4
June 18
June 19
June 20
June 21
(1:1)
Trendline
3
2
1
0
0
1
2
3
4
5
6
7
8
Rapid Eye Z90 (m) * 2
Figure 12. Rapid Eye Z90 vs. in situ Secchi depth in the Tvärminne area in June 2012 (second version data).
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Figure 13. Secchi depth (m) map of Tvärminne area with Rapid Eye on June 20, 2012 (computed with Secchi
depth = 2* Z90).
4.2.4
User comments
The following comment about the satellite products were received from the RKTL:
About the 1st version products (delivered on Aug 30, 2012)
-
I think it is possible that the inner bay area (with the 3 “outlier” data points; 9,83 NTU, 9,77
NTU, 9,72 NTU ) is really that turbid as the calibration points show, since it goes in line with our
experience from other bay areas. We also visited one of the points (ID 8) also on 7.6.2012 and
got an NTU value of 7,38 (18.6. 9,77 NTU). The increase in the turbidity from early to late June
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could be due to high nutrient load, increased amount of blue algae (typical for mid-summer)
and humus substances, maybe even pollen blended in the water column? Could be that the
time difference of 2 days doesn’t have that big effect and, thus, the points aren’t real “outliers”.
-
The reason why I’m not that keen on removing the “outlier” points is that I feel the interpreted
satellite image is now lacking contrast, especially the real turbid innermost bay areas are
underestimated.
-
Maybe removing the “shadows” in shallow water near coastline/islands would help adjusting
the calibration? In those “shadows” turbidity values probably are quite high, and adding those
areas to the analysis might help adjust the calibration.
-
Validation of Z90 (Secchi depth) turned out to be surprisingly good! 20-30 cm difference in
Secchi values in the same measuring point may exist already between 2 (not that experienced)
people conducting the measurements in the field.
About the 2nd version products (delivered on Oct 15, 2012):
-
The values in the inner bays have improved. E.g., in the area of Dragsvikfjärd the values are now
higher and more realistic. However, there still are many places in the (outer) archipelago where
a "shadow" of high (and unrealistic) turbidity values can be seen extending 40-50 meters on the
western sides of islands. Better masking of land areas, islands, bridges etc. would improve the
situation and make the data more useful.
-
In the Finnish Game and Fisheries Research Institute we are modelling the distribution of
newly-hatched pikeperch larvae in the archipelago area surrounding Hanko and Tammisaari. As
a predictor variable we use turbidity. We have in situ turbidity measurements which we
interpolate into map surfaces. These we use for building a model that predicts the distribution
of pikeperch larvae. If the satellite turbidity maps are accurate enough, we would be happy to
use them also as predictors.
-
In the future we would be interested in using high-quality high-resolution turbidity maps from
satellites also for other coastal areas. They would be useful in planning field surveys (where to
perform in situ measurements) and in predicting of distribution of fish reproduction habitats in
new coastal areas.
4.2.5
Further steps
Based on the comments from the user the following steps are outlined for the next phase:
-
An improved land mask is needed to remove the areas near the shore line that have
unrealistically high turbidity values. Some of these may be caused by bottom effect so a water
depth product could be useful.
-
Extend the coverage to other areas.
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4.3 Validation of WISP-3
In Finland WISP-3 was used in campaigns conducted during 2011 and 2012 (see
FM_PH1_WP54_D541_PR and FM_PH2_WP54_D542_PR (Radiometric and in situ measurements of the
ground truth for assessing the services) for details). Unfortunately, none of the measurements
coincided with a satellite overpass. Thus, comparison with satellite products was not possible. Instead,
the analysis concentrated on the performance of WISP-3 in Finnish lakes and coastal areas.
Figure 14 shows a comparison of chl-a values estimated with WISP-3 and measured at a laboratory.
WISP-3 appears to estimate quite accurately the chl-a values for the following campaigns:
-
Inkoo 20120904: Coastal sites from a boat
-
Helsinki 20110812: Coastal sites from a research vessel
-
Helsinki 20110818: Coastal sites from piers
At Lammi (20110629, Humic lake from a boat) the estimation fails. The likely reason for this is the high
concentration of humic matter in the lake (a_CDOM (400nm) about 9.5 1/m).
The WISP-3 estimate appears not to be reliable at River Vantaa. The water in the river is very turbid
(TSM values measured at laboratory varied between 7.9 and 200 mg/l) and this is the likely reason for
the large errors in the WISP-3 chl-a estimates.
Figure 15 shows reflectances measured with WISP-3 (see Table 4 for the corresponding water quality
values). The stations Rajasaari and Taivallahti have high chl-a concentrations (10-15 g/l) and also high
reflectances. The reflectance values appear to be too high in the blue and infrared. The reason for this
is not known at the moment.
The strong absorption by CDOM at the lake at Lammi is also clearly visible. The shape of the spectra at
River Vantaa differs clearly from the rest due to the large amount of inorganic particles.
At the humic lake at Lammi WISP-3 shows higher values of the spectra in the blue wavelength which
are not estimated correctly. Figure 16 shows a comparison of the WISP-3 result with spectra measured
by an ASD spectrometer. After normalization the shapes of the spectra have a good match between
wavelengths 500 and 720 nm. In the blue wavelengths (<500 nm) WISP-3 overestimates the
reflectance.
As a conclusion, it can be stated that the current algorithms of WISP-3 appear to work quite well in
Finnish waters with low or moderate inorganic particle and humic concentrations. For the other cases
it is necessary refine the algorithms.
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25
Inkoo 20120904
Lammi 20110629
Helsinki 20110812
Helsinki 20110818
River Vantaa
WISP-3 Min-Max
Laboratory Chl a (g/l)
20
15
10
5
0
0
5
10
15
20
25
WISP-3 Chl a (g/l)
Figure 14. WISP-3 chl-a vs. laboratory chl-a with Finnish data. In some sites several WISP-3 measurements
were made. The round symbols indicate the mean values of the measurements. The horizontal lines labeled
as ‘WISP-3 Min-Max’ show the minimum and maximum values of the measurements.
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Figure 15. Reflectances of various water types in Finland measured with WISP-3. The values of water quality
parameters are shown in Table 4.
Table 4. Values of water quality parameters for stations shown in Figure 15 (measured in a laboratory).
Station name
Chl-a (µg/l)
Turbidity (FNU) / TSM (mg/l)
CDOM (1/m)
Lammi
3.2
1.3 / 1.9
9.5
Rajasaari
15
6.6 / 6.8
1.43
Taivallahti
11
6.6 / 6
1.43
Inkoo 3
3.6
1.1 / -
-
Inkoo 4
3.1
1.1 / -
-
Inkoo 6
6.3
3.1 / -
-
River Vantaa
2.75
- / 11.76
-
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-3
1.4
x 10
ASD
WISP
1.2
Reflectance
1
0.8
0.6
0.4
0.2
0
400
450
500
550
600
650
700
750
800
850
900
Wavelength (nm)
Figure 16. Reflectance measured at a humic lake at Lammi with WISP-3 and ASD spectrometers. The ASD
estimate is for above water and the WISP-3 result has been divided by 8 in order to make the comparison of
the shapes of the spectra easier.
4.4 Validation of Secchi3000
The development of the Secchi3000 device took place through funding from other projects. The device
was tested during FRESHMON validation campaigns in Finland in order to take advantage of the
laboratory and other in situ data that were collected. The development and calibration of the device is
still ongoing and the results presented here are preliminary.
The Secchi3000 measurements can be performed using mobile phones and a software that sends the
measurement pictures from the phone to a server. The estimation of Secchi depth with Secchi3000 is
done with the following steps:
1. Extract the brightness values (digital numbers) of white, black and grey measurement areas at
two depths (an automatic image analysis software is not yet ready and the Secchi3000 pictures
were processed manually).
2. Estimate the attenuation of water with Red and Green bands from differences between the
extracted digital numbers.
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3. Convert the attenuation into an estimate of Secchi depth using calibration coefficients (for this
step an equation developed with data collected during 2011 was used).
Figure 17 shows a comparison of Secchi depth estimated with Secchi3000 (using two mobile phones)
vs. Secchi depth measured using the traditional method (with the white lid of the sample collecting
device). The current Secchi3000 algorithm appears to work very well until the Secchi depth reaches
about 4 meters. After that the device starts to overestimate. The likely reason for this is that the sensor
elements of the cameras are not sensitive enough to detect the small attenuation in clear waters with
the current 10 cm path length. The results with the two phones are very similar indicating that the
differences caused by different sensor elements are not large. The measurements were also performed
without the image transfer software but taking the pictures directly from the camera. The estimates
were again very close to the values shown in Figure 17.
Over a hundred Secchi3000 devices were distributed to users in Finland for testing during summer
2012. The analysis of this data is still ongoing.
6
5
Reference (m)
Nokia E5
Nokia 500
4
3
2
1
0
0
2
4
6
8
10
12
14
16
18
Secchi 3000 (m)
Figure 17. Secchi depth estimated with the Secchi3000 device vs. Secchi depth measured with the reference
method (from the lid of the sample collector). The data used in the analysis was collected during the Inkoo
campaign on Sep. 4, 2012.
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Validation in the Netherlands (WI)
5.1 Introduction
Lakes IIsselmeer and Markermeer (Figure 18) are the largest lakes of the Netherlands. Lake
Markermeer is located close to the city of Amsterdam, and both lakes are intensively used as
freshwater reserve, recreational area, fishing ground and for transport. Due to the large nutrient load
high concentrations of phytoplankton and sometimes cyanobacteria occur in Lake IJsselmeer. The
shallower Lake Markermeer is influenced by resuspension and is relatively turbid.
Figure 18. Map showing Lake Ijsselmeer and Lake Markermeer in the middle of the Netherlands.
5.2 Optical measurement campaigns and other in situ data available for validation
Campaigns to obtain optical measurement for MERIS validation took place in 2011. In 2012, due to the
loss of MERIS, the focus was shifted to MODIS. WISP-3 measurements and Secchi depths were taken
next to automated measurement poles of the Dutch agency which is responsible for monitoring of
water quality (Rijkswaterstaat).
In 2011, six days of field campaigns were performed (July 6th, July 21, September 23, 25 and 28 and
October 28) on Lake Markermeer and Lake IJsselmeer. The measurements were taken simultaneously
with MERIS overpasses, but due to cloud cover a match up was obtained only for 28 September. A
Master's thesis has been published about the work in September (Chiwara, 2012).
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In 2012, two in situ validation cruises took place during summer 2012, a third one is planned in
autumn/winter (either week 44 or week 50, depending on the weather). The first cruise was in Lake
IJsselmeer, 6 June 2012, when the Netherlands Institute of Ecological Research (NIOO) allowed us to
join their in situ campaign. During this campaign it was fully overcast, sometimes with rain. There was a
strong wind and as the boat was small, it was hard to obtain valid WISP-3 measurements.
The second cruise was on Lake Markermeer, 26 June 2012, when a monitoring campaign of the Dutch
agency responsible for monitoring (Rijkswaterstaat) was joined. The weather was very calm, in the
morning partly cloud covered, in the afternoon with a clear blue sky.
During the 2012 in situ campaigns, WISP-3 and Secchi depth measurements were taken next to
automatic measurement poles. In Lake Ijsselmeer three poles (FL46, FL9 and FL47) were visited, plus
one extra station. In Lake Markermeer, two poles were visited (FL42 and FL52), one of these was
visited twice (beginning and end of the cruise) and considered as two different stations. At the day of
the campaign in Lake Markermeer a MODIS image of the area was acquired.
5.3 Other in situ data for satellite validation
In addition to the optical measurement campaigns, data from measurement poles, monitoring
campaigns and some project-related in situ campaigns (Table 5) will made available by Rijkswaterstaat
and the FRESHMON user (Deltares) in the Netherlands. This data will be used for satellite validation.
However, due to delays in signing of the SLA caused by the loss of MERIS and consequent shift in
possible products, this data has not been processed and analysed yet.
Table 5. In situ data measured by Deltares and Rijkswaterstaat in the Netherlands during summer 2012.
Type
Measurements
Frequency
In situ monitoring data
Chl, SPM, DOC, Transparency, Kd,
Once or twice a month
Autonomous pole measurements
Chl, Turbidity, Phycocyanin
fluorescence
Continuously
In situ data of project campaigns
Chl, SPM
Some campaigns
5.4 Methods
The WISP-3 was deployed at the front of the ship, facing 135 degrees away from the sun (Hommersom
et al., submitted). Secchi depths were taken at the shadow side of the ship. The ship was kept
stationary during the measurements.
Chl, SPM and phycocyanin concentrations were derived from the WISP-3 spectrum using band-ratio
algorithms. The algorithm of Gons et al. (2005) is used for chlorophyll-a, the algorithm of Simis (2006)
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is used for phycocyanin and an algorithm of Rijkeboer (2000) is used for suspended matter retrieval. All
are tuned for or supposed to be valid for Lake Ijsselmeer and Markermeer.
MERIS L1 data was processed in BEAM software with the ICOL processor for adjacency correction
(Santer and Zagolski 2009) and C2R processor (Doerffer and Schiller 2006a, 2006b). With some
comparison exercises, it was found that it is necessary to use the ICOL processor in Lake IJsselmeer,
and that the C2R processer provided better results than, for example, the WeW processor (Chiwara,
2012). Phycocyanin concentrations were derived from MERIS according to Simis (2006).
For MODIS, a special ocean surface calibration was applied in SeaDAS 6.3 software, which results in
L1b. Atmospheric correction was done with the MUMM algorithm in SeaDAS (Dagliottiet al., 2011,
Ruddick et al, 2000). Level 2 processing was done with the WISP-algorithm (Peters, in preparation).
5.5 In situ versus WISP-3 measurements
With the 2012 data, comparisons could be made between the concentrations derived from the optical
WISP-3 measurements and the automatically obtained data from the measurement poles. Figure 19,
Figure 20 and Figure 21 show good correlations between the data. For chlorophyll, there seems to be
an offset difference between the WISP-derived Chl data and the pole data (derived from fluorescence).
However, after the offset correction the correlation is good.
For the MODIS derived data, the chlorophyll concentration does not relate well with the pole data. This
is explained further in section 5.6.
Figure 19. WISP-3 versus measurement pole Chl-a. For the regression, the measurement at (65.1, 3.1), station
FL46, has been left out. This station had a large influence of waves and therefore the WISP-3 reflectance
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spectra were rather noisy. Also the MODIS matchup derived concentration has been plotted vs. the pole data
(not used in the regression).
Figure 20. WISP-3 derived SPM concentrations versus measurement pole turbidity data. Also the MODIS
matchup derived concentration has been plotted vs. the pole data (not used in the regression).
Figure 21. WISP-3 derived Phycocyanin concentrations versus pole Phycocyanin fluorescence data.
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5.6 WISP-3 versus satellite results
In 2011, good correlations between MERIS and WISP-3 reflectance spectra were found for the stations
sampled at the day of the matchup with MERIS (September 28th). An example is shown in Figure 22.
From the MERIS image of September 28, phycocyanin concentrations were derived and compared with
WISP-3 obtained phycocyanin concentrations of the stations of that day (Table 6, from Chiwara, 2012).
The chlorophyll map derived at that day is shown in Figure 23. More results from the 2011 campaigns
are presented in Chiwara (2012).
Figure 22. WISP-3 versus MERIS obtained reflectance spectrum derived at Lake IJsselmeer, 28 September 2011
(Figure: Chiwara, 2012).
Table 6. Phycocyanin concentrations derived from WISP-3 and MERIS (Chiwara, 2012)
Station
WISP-3 derived
phycocyanin (µg/l)
MERIS derived phycocyanin
(µg/l)
1
65.56
70.1
2
71.88
71.52
3
70.36
71.52
4
69.17
72.31
5
62.37
70.96
6
57.81
68.09
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Figure 23. MERIS chlorophyll map of September 28 (ranges between 10 and 20 g/l).
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Figure 24. WISP-3 versus MODIS obtained normalised reflectance spectra derived at Lake Markermeer, 26
June 2012.
In 2012, one MODIS almost-matchup was obtained. The in situ sampling took place under partly cloudy
sky and MODIS recorded only one pixel north of the in situ station. The reflectance spectra of WISP-3
and MODIS showed a similar shape (Figure 24), but the intensity of the reflection showed a large
difference. Probably the clouds have had a large influence on the atmospheric correction of MODIS in
this pixel. For further comparison, the reflectance spectra were normalised. Most striking is the missing
chlorophyll peak in the MODIS data. This explains the why the derived chlorophyll concentration did
not correlate well with the pole-derived chlorophyll concentration (Figure 19). However, based on this
single pixel no conclusions can be drawn.
5.7 User comments
User response on a first version of the scatter plots showing WISP-3 vs. pole measurements (which
were more noisy than the quality checked version included here): [translated from Dutch]: SPM and PC
look nice, for Chl there is something going on. I think you should distrust the data of the pole in its
accuracy. We have sampled next to the some of the poles and analyzed those in the lab. I hope to
obtain some more clarity on the usefulness of the Chl-sensor data from this. A first analysis of the polelab measurements also showed an offset for the fluorometers on the poles.
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5.8 Further steps
WISP and MERIS-ICOL-C2R processed products show reflectance spectra that are similar in shape.
Therefore, it is expected that the radiometric measurements with the WISP-3 and the EO products
fulfill the quality criteria.
To improve the EO derived water quality products, MERIS-ICOL-C2R radiometric products will be
processed with the WISP-algorithm. The algorithm will be tuned with in situ laboratory data provided
by the user and with in situ trend series derived from the measurement poles (Table 5). Tuning of the
algorithm based on this in situ data is planned before the end of 2012.
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Validation in Germany (EOMAP and EAWAG)
The assessing of the quality of the FRESHMON data products in Germany is based on in situ data
provided by the users as well as on measurements from the FRESHMON Lake Constance field campaign
2012. The satellite data for the validation consist of low to medium resolution (MODIS, MERIS) and
high resolution data (SPOT-4, SPOT-5, ASTER, RapidEye). The MODIS data were free of charge acquired
from the NASA; MERIS data for the Alpine region were provided by ESA and acquired with the help of
Brockmann Consult. The high resolution data were tasked especially for the field campaign days (see
D54.2 for an overview).The WISP-3 has been validated with in situ sampling methods in course of the
Lake Constance field campaign.
6.1 Lake Constance Field campaign 2012
The validation of satellite products and the WISP-3 instrument took place in Lake Constance using data
collected during a campaign in spring/summer 2012. The validation of satellite data with the in situ
measurements is described in chapter 6.1.1 and the validation of WISP-3 against other in situ data is
presented in chapter 6.1.2.
Lake Constance is affected each spring by input of suspended mineral particles due to massive snow
melting from the Alpine Rhine at its eastern shore. The particles spread into the lake and create strong
spatial variability. The objective was to measure this with satellite and in-situ methods. The satellite
products will be used as input and validation data for the models developed in WP52 and WP53.
The in situ measurements performed by EAWAG are described in FM_PH2_WP54_D542_PR
(Radiometric and in situ measurements of the ground truth for assessing the services) and D52.1
(Report on Case Studies for practicability). The latter also contains additional results of the field
campaign. In this document D54.3 only the best examples were chosen to underline the main results
regarding the assessment of the FRESHMON data quality.
6.1.1 Satellite data validation
The validation of satellite products is done with data measured with the WISP-3 instrument and with
filtered TSM measurements.
6.1.1.1 Satellite data processing
Four high-resolution sensors were used for processing Lake Constance satellite images:
1. RapidEye: 5m resolution (5 channels used: 440nm - 850nm)
4. ASTER: 15m resolution (3 channels used: 520nm - 860nm)
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MODIS Aqua and MODIS Terra data were processed using the channels available at 500m resolution
and 250m resolution, ranging from 459-2155nm.
The optical multispectral satellite data were processed with the Modular Inversion and Processing
System (MIP), see Heege (2003, 2009) and Kisselev (2004) for further details. MIP is designed for the
physically based recovery of hydro-biological parameters from multi- and hyperspectral remote
sensing data and used for the environmental mapping of aquatic shallow and deep waters of inland
waters, coastal zones and wetlands. The architecture of the program binds a set of general and
transferable computational schemes in a chain, connecting bio-physical parameters with the measured
sensor radiances. Important processing steps comprise of land-water-cloud-detection, adjacency,
sunglitter and atmospheric correction and the application of radiative transfer database. The products
retrieved from the processing of the Lake Constance field campaign are Total Suspended Matter and
Chl-a concentrations, yellow substance was set to a fixed value.
6.1.1.2 Comparison of satellite data and in situ measurements
For the comparison of in situ data and satellite data, we chose the 25 th of May. On this date
measurements with WISP-3, filtering method have been conducted at the stations (Figure 25a) and
satellite data from of SPOT-5 20m resolution, ASTER 15m resolution and two MODIS (Aqua/Terra) with
250 m resolution were acquired (Figure 25b).
(a)
(b)
Figure 25 Overview of (a) the sampling stations on 25th May in Lake Constance and TSM concentration
retrieved from processing MODIS Aqua 12:15 with 250m resolution and (b) all the satellite images acquired on
25th May
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SPOT-5 over flight time was at 09:25, ASTER at 10:33, MODIS Terra at 10:30 and MODIS Aqua at 12:15.
The sampling time started at 07:19 and ended at 14:58 (all times in UTC). The time difference between
satellite acquisitions and in situ sampling due to the temporal and spatial dynamics in the lake is a
relevant factor in the course of the comparison of the data. Doerffer (2002, p.7 ) suggest for MERIS a
coincidence of ± one hour for Case I waters and even ± half an hour for Case II waters. To estimate the
accuracy we therefore use only measurements that are taken within max. 0.5 hour. This is also
extremely dependent on the gradient of the sampling station and could be reduced to minutes in some
cases. Also, computer simulations suggest that the temporal variability can be high during one day, so
this temporal mismatch can affect the correlation.
In Figure 26 the comparison of WISP-3 vs. TSM filtering vs. satellite estimated TSM is shown with an
overview of all measurements for the in situ sampling stations. Matches of the sampling with the
satellite over flights plus/minus (pm) half an hour are highlighted with circles.
Figure 26. Overview of the measurements from 25.05.2012 with the different methods and highlighted time
coincidences
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The smaller the time differences are, the closer the values fit together. For SPOT data, a time
difference with the in situ measurements at station 8 is only 5 minutes and the values of filtering
method and satellite data are only 0,1 mg/l separated (see Table 7).
Table 7. Example for time equivalence of in situ measurements and satellite data on 25.05
Station Time
TSM filtering
TSM WISP
8
09:18
5,9
5,3
8
09:25
17
12:15
17
12:22
SPOT-5
MODIS 12:15
5,8
3,1
2
2,7
6.1.1.3 Intercomparability of different satellite sensors
For the intercomparability between the different satellite sensors we chose dates where difference of
the over flights was not larger than +- 2 hours.
On May 4th we have satellite data from RapidEye with 5m resolution at 11:13 and two MODIS data
sets, one from Terra on 10:15 and the other from Aqua on 12:00. In Figure 27 an overview of the in situ
sampling stations is given, together with the retrieved TSM values of the satellite data processing.
Figure 27. Overview of the sampling stations on May 4th, 2012 with retrieved TSM concentrations from
RapidEye and MODIS data.
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Figure 28 demonstrate that the matching of the different satellite sensors is good at the stations with
low variability (13-17, 21-28), whereas at the stations with high concentrations and influence of the
river Rhein the difference between the medium resolution sensors and the high resolution sensors
become obvious (18-20). Here the importance of the time difference described already in chapter
6.1.1.2 gives an explanation of the divergent results. With higher resolution the spatial variability can
be detected in regions with high gradients.
Figure 28. Comparison of the TSM processed with MODIS and RapidEye satellite data on May 4th, validated at
the coordinates of the in situ sampling stations 13-28.
Other examples are the 26th and 27th of June, with in situ measurements on stations 1-7 (29).
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Figure 29. Overview of the in situ sampling stations on 26.06 and 27.06 1-7 and the TSM concentrations from
26th and 27th June derived from the satellite data
On 26th June 2012 we have data form MODIS and ASTER, both on the terra satellite, to compare the
sensors see Figure 30a and 30b. For six points we have a SD of 0.52 [mg/l] when comparing MODIS
versus ASTER.
(a)
(b)
3,0
SD = 0,52
r = -0,02
N =6
2,8
TSM ASTER 15m
2,6
2,4
2,2
2,0
1,8
1,6
1,4
1,6
1,7
1,8
1,9
2,0
2,1
2,2
2,3
TSM MODIS 250m
Figure 30. Comparison of the processed satellite data on 26.06 from MODIS Aqua, MODIS Terra and ASTER (a)
measured TSM concentration and (b) TSM MODIS versus TSM ASTER
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In Figure 31 the trend at the stations on 27th June in the satellite data seems to stable, except for
station 6, where MODIS 250m resolution data converge to the TUR [FTU] values sampled with on the
surface with a Sea Bird Profiler turbidity meter, which was not detected by using 500m resolution data.
Figure 31. Comparison of the processed satellite data on 27.06 from MODIS Aqua, MODIS Terra and in situ
measurements TSM filtering and surface Turbidity.
6.1.2 WISP-3 validation
Figure 32 shows a comparison of WISP-3 TSM estimate vs. TSM estimates from bottle samples and
beam attenuation. For both cases the correlation is high. For the WISP-TSM vs. Lab-TSM the
comparison shows a need to calibration of the WISP-3 result.
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(a)
(b)
Figure 32. WISP-3 TSM vs. (a) laboratory TSM concentration and (b) beam attenuation with data from Lake
Constance.
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6.1.3 Summary of the results
Comparability of the different methods
As shown in the examples above, a direct comparison of satellite data with in situ measurements need
a lot of special conditions and is therefore prone to a large variety of influences which can lead to
errors. When taking this into account, the matching of the TSM results, which have the fitting
conditions, is quite good between the different methods.
For CHL, difficulties arise with both in situ and high resolution satellite data measurements. The
measured chlorophyll values did not have a lot of meaning from the validation point of view, since Lake
Constance is oligotrophic and therefore the values always remain low. At that order of magnitude, the
chlorophyll sensors had a lot of noise due to the low values, and the WISP values had errors. The
retrieval of CHL-a from the high resolution satellite images has to be improved and could not serve as a
validation base here. Only the medium resolution results of CHL-a are analysed further together with
the user from LUBW-ISF in the upcoming months.
Intercomparability of the satellite sensors
The comparison between the different satellite sensors indicates a reliable intercomparability of the
sensors. Therefore, the products of the satellite data processing represent a harmonized measurement
method. An important advantage of the satellite data is the ability to identify spatial variability which
cannot be covered with in situ measurements at the same time. Obvious is also that the higher the
resolution the better the correlation of satellite data (and also with in situ measurements) in areas with
high variability.
Comparability of WISP and other in situ methods
The algorithms for remote sensing are site-dependent. Since WISP-3 was calibrated for Dutch lakes, we
cannot expect a perfect match to other in-situ methods. TSM by water filtering or optical transmissivity
have ambiguity on their definition, since the larger particles that can be considered as suspended
change with the amount of turbulence.
Results for Lake Constance on May 25 show excellent correlation even though high waves, which could
distort the measurements, were present. For this reason, WISP-3 is suitable to measure TSM in Lake
Constance after recalibration.
6.1.4 User comments
The users from the ISF (LUBW) were impressed by the spatial resolution, number of images that could
be captured, and the reliability of the FRESHMON turbidity and TSM products. The satellite based
method turned to be more reliable and independent on technical problems than the in situ sampling
during the campaigns. Also the area wide harmonized measure of FRESHMON products in comparison
to quite different in situ sampling measures was positively evaluated. For chlorophyll the users
recommended further analysis for the organic absorption product.
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Spatially resolved remote sensing products in combination with transects of probes during campaigns
allowed the user to analyze the impact natural spatial scales. This allowed a new insight how to
optimize in future appropriate monitoring approaches. The user applied the FRESHMON services and
campaigns for publication and exchanged the experience with other water authorities and research
organizations, e.g. at the DGL Symposium (German Community for Limnology, 24.09.2012) or at the
European Large Lakes Symposium ELLS, 09.10.2012).
6.1.5 Further steps
Further analyzing steps emerged out of the results:
-
Improvement of the Chl-a algorithms for high resolution data
-
Further analysis of the vertical stratification as a possible source for differences
6.2 Lake Constance Time series
6.2.1 Description of the data sources
In addition to the field campaign results, MERIS satellite processing results for Chl-a and TSM have
been validated with in situ data for the years 2003-2011, provided by the State Institute for the
Environment, Measurements and Nature Conservation of Baden-Wuerttemberg (LUBW) taken from
the Lake Constance water information system BOWIS of the International Commission for the
Protection of Lake Constance (IGKB). The in situ data consist of 5 stations, but only one should be here
discussed. Fischbach-Uttwil (FU) is located in the middle of the lake with stable conditions (see 33).
Figure 33. Lake Constance validation station Fischbach-Uttwil 47° 37.44'N 9° 22.53' E
The data set provided by the user consists of approx. two-weekly monitoring measurements of Chl-a
and the corresponding Secchi depth. The Chl-a concentration is given as an average of 0-20m. For the
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comparison, 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).
6.2.2 Satellite data processing
MERIS Full resolution data were processed for the years 2003-2011 using the MIP processing
algorithms (see 6.1.1.1 and D33.1 for further details). As a quality measure, the total quality of each
pixel was calculated taken in to account several factors like sunglint, solar angle, spacecraft angle, AOT
influence, shallow water, transition zones, residuum of the retrieval, processor warnings and
concentrations on upper limit. Each of the influence of the possible technical or environmental factor
to the pixels' quality is scaled from 0 - 100. 0 equals = very low quality and 100 equals = no quality
concerns. Threshold values define distinct values when a parameter is assumed to influence the
quality. For the validation, the values have been calculated taken a matrix of 3x3 pixels around the
coordinate taken into account.
6.2.3 Data comparison of satellite data with in situ data
Total Suspended Matter
In Figure 34 the results of the satellite processing and the provided in situ data for Total Suspended
Matter are visualized. If the standard deviation exceeded 1, also the total quality value was included in
the calculation.
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Figure 34. Time series 2003-2011 of Total Suspended Matter at the FU station Lake Constance.
The satellite data is shown with the corresponding total quality generated out of the EOMAP
processing chain, ranging from poor quality (orange = total quality < 33%), over middle (olive=total
quality 33-66%) to good quality (green = total quality > 66%). Also, the spatial standard deviation is
shown as a matrix of 3x3 pixels around the exact coordinate has been taken into account when
generating the comparison. To strengthen the thesis of a high variability due to different measurement
times (see 6.1), the figure also contains the comparison of exact matching of in situ measurement days
with satellite over flights, plus/minus one day and plus/minus two days. Due to the low variability at
station FU in the middle of Lake Constance and as we had no other way to compare the measurements
otherwise, the comparison is made with this large time difference.
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The seasonal trend in the single years indicated by both in situ and satellite data can be easily followed
and the values have a SD=0,21 mg l-1 when comparing the data from the same day (see Figure 35).
Figure 35. Total Suspended Matter derived from satellite data versus in situ data provided by the user
LUBW/IGKB for same day matches at the FU station Lake Constance
To emphasize the importance of time coincidence to compare the data, an example of the MERIS
processing 2006 is shown in Figure 36. In situ measurements on the same day fits together with the
satellite retrieved concentrations (blue circles). Also, the seasonal trend can be followed, with the peak
in summer.
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Figure 36. Example for Total Suspended Matter at the FU station Lake Constance for the year 2006
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Chlorophyll-a
In Figure 37 the results of the satellite processing and the provided in situ data for Chl-a are visualized,
also in connection with the quality, the spatial standard deviation and the special focus on day
matches.
Figure 37. Time series of Chl-a concentration from satellite data and in situ data provided by the user LUBW at
the FU station Lake Constance
In general, the seasonal trend in the single years indicated by both in situ and satellite data can be
followed and the values have a SD=2,24 µg l-1 when comparing the data from the same day for the
years 2003-2009 (see Figure 38) and SD=4,93 µg l-1 for the years 2010 and 2011.
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(a)
(b)
Figure 38. Chl-a derived from satellite data versus in situ data provided by the user LUBW for same day
matches at the FU station Lake Constance (a) for the years 2003-2009 and (b) for the years 2010-2011
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While analyzing the data a trend can be seen that there is an increase of the Chl-a concentrations
especially in the years 2010 and 2011 in comparison to the other years and to the in situ data. As an
example the yearly measurements from 2003 and 2010 are displayed in Figure 39.
(a)
(b)
Figure 39. Chl-a derived from satellite data and in situ data provided by the user LUBW for same day matches
at the FU station Lake Constance (a) for the years 2003 and (b) for the years 2010 with an increase of the
concentration in summer
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An explanation could be that the rising concentrations of Yellow Substances in the course of the year
influence the satellite data processing results. The data was processed with a fixed value for Yellow
Substances constant over the whole year. This has to further evaluated, also in close collaboration with
the user.
6.2.4 User comment
The user highlighted the value of long-term, well resolved harmonized measures that come with the
new FRESMON products.
Users advised the necessity of a general analysis how to evaluate the different in situ and space based
measures as proxy values for the ecological status of aquatic systems, as requested by the European
Water Framework Directive. Several authority members and several authorities stated that they intend
to include satellite based products in future operational monitoring programs and appreciate the
opportunity to develop related strategies within the FRESHMON collaboration. A combination of
standard in situ sampling with automated in situ probes and satellite based measures was proposed to
be more cost effective, better harmonized and better adapted to the spatial and temporal scales of
aquatic systems than pure in situ approaches.
The necessity to exchange this with European agencies and the EEA was highlighted.
6.2.5 Further steps
Following further steps will be made resulting from the results of the Lake Constance time series:
-
Further analysis of the Chl-a retrieval, especially for the years 2010 and 2011, in collaboration
with the user and with focus also on the sum of absorbers product (ABS) which might be helpful
to understand and to trace the natural processes
6.3 Bavarian Lakes
In situ data provided this summer from the Bavarian Environment Agency serve as the base for the
validation of the Chl-a and TSM products generated from MERIS satellite imagery for the time from
2003-2011. The in situ data consist of Chl-a measurements for different depths, Secchi-depth,
temperature, dissolved organic carbon and total bio volume PP in mm³ l-1. In this first run of the
analysis TSM calculated out of the Secchi depth (same formula as described in 6.2.1) and Chl-a are
compared. Due to the low number of matching days, no attention was paid to time coincidence.
The sampling stations cover the larger Lakes in Bavaria, for the exact station names and coordinates
see Table 8.
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Table 8. Example for time equivalence of in situ measurements and satellite data on 25.05
Station-No. Lake
Sampling station
2246
Ammersee (1662400000)
Deepest point
2138
Chiemsee (1846600000)
Deepest point, Weitsee
2018
Kochelsee (1645126000)
Deepest point Ostwanne
2084
Simssee (1819624000)
Deepest point
2094
Staffelsee (1661424000)
Deepest point Nordwanne
2097
Starnberger See (1666280000)
Deepest point
2110
Tegernsee (1821280000)
Deepest point
704776
5289636
2124
Waginger See (1868180100)
Deepest point
782236
5316499
2125
Walchensee (1632240000)
Deepest point
676362
5274688
UTM East UTM North
658372
5316495
758365
5308822
676444
5279462
742764
5307862
662796
5285212
673604
5310951
For the validation only the results for Ammersee, Chiemsee, Starnberger See and Walchensee are
described. For an overview of the location of the sampling stations see Figure 40. Descriptions of the
lakes in the following chapters refer all to Nixdorf et al. (2004).
Figure 40. Sampling stations in southern Bavaria
For the Bavarian Lakes the same processing steps were done as for the Lake Constance, see 6.1.1.1.
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6.3.2 Data comparison
For the comparison of Total Suspended Matter (TSM) and Chlorophyll-a (Chl-a), we used the depth
integrated mean value from 0 to 20m for the comparison (same as with the time series at Lake
Constance 6.2.1).
Ammersee
The Ammersee is the third largest lake in Bavaria and has an area of 47 km² with a maximal depth of
81m. It is a glacial formed finger lake with the typical elongated shape from South to North, three
times longer than wide. The satellite retrieved TSM in mg l-1 (Figure 41) and Chl-a in µg l-1 (Figure 42)
are shown.
Figure 41. Ammersee TSM time series 2003-2011 MERIS and in situ data
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Figure 42. Ammersee Chl-a time series 2003-2011 MERIS and in situ data
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Chiemsee
The Chiemsee has an area of approx. 80 km² and is the largest lake in Bavaria, third largest in Germany.
Chiemsee is formed due to glacial erosion in the last ice age as a kettle lake. The mean depth is
25.63m, maximal 73.4m. The satellite retrieved TSM in mg l-1 (Figure 43) and Chl-a in µg l-1 (Figure 44)
are shown.
Figure 43. Chiemsee TSM time series 2003-2011 MERIS and in situ data
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Figure 44. Chiemsee Chl-a time series 2003-2011 MERIS and in situ data
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Starnberger See
The Starnberger See is located 30 km south west of Munich, has an area of 56 km², 19km long and is
4.7km wide. The Starnberger See is the longest lake in Bavaria and has a maximal depth of 123.8m. The
satellite retrieved TSM in mg l-1 (Figure 45) and Chl-a in µg l-1 (Figure 46) are shown.
Figure 45. Starnberger See TSM time series 2003-2011 MERIS and in situ data
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Figure 46. Starnberger See Chl-a time series 2003-2011 MERIS and in situ data
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Walchensee
Walchensee is an alpine lake with an area of 16.27 km², located 75km south of Munich and of tectonic
origin with glacial reshape. The maximal depth is 189.5m. The satellite retrieved TSM in mg l-1 (Figure
47) and Chl-a in µg l-1 (Figure 48) are shown.
Figure 47. Walchensee TSM time series 2003-2011 MERIS and in situ data
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Figure 48. Walchensee Chl-a time series 2003-2011 MERIS and in situ data
Suspended matter fits quite well to the in situ measurements in all lakes (Figures 41, 43, 45, 47) and
the trends throughout the year can be followed. For Chlorophyll-a, we find a significant overestimation
of satellite retrieved chlorophyll for all lakes (Figures 42, 44, 46, 48). We expect that this is due to a
general increased organic absorption in these lakes and may be corrected for this impact after further
ongoing analysis and lake specific calibration.
Detection of Chl-a may be also be strongly sensitive to the low sensor resolution in relation to the scale
of the aquatic system and influences like the adjacency effect. The adjacency effect could lead to the
visible underestimating TSM in some of the small, narrow pre-alpine lakes (see Figure 45). Analysis of
the effect has been made by Odermatt et al. (2008) for Lake Constance.
6.3.3 User comment
The validation results needs still to be presented to the end-user of the Bavarian Environment Agency.
A special workshop to discuss the results is foreseen in the upcoming months.
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6.3.4 Further steps
The overestimation of the Chl-a have to be further analysed, together with an ongoing validation of the
in situ data set provided by the user. Analysis of possible relation between temperature, dissolved
organic carbon, organic absorbers and Chl-a have to be made.
6.4 River Elbe
For river Elbe, satellite sensors MODIS Aqua, MODIS Terra, RapidEye and SPOT were used to derive
turbidity and total suspended matter. For MODIS, more than 50 scenes were processed from 2009 to
2012. Processing is continued ongoing with the operational near real time pilot processor installed at
the ground segment of the German Aerospace Center. 5 RapidEye scenes and 1-2 SPOT scenes were
requested and processed furthermore in collaboration with the users BAW and BfG.
Standard data processing was applied for MODIS Aqua, Terra (250m and 500m fusion processor),
RapidEye and SPOT. SPOT showed significantly underestimated TSM concentration for a hazy scene
even after manual radiometric recalibration of channel 4 for SPOT. Former experience with SPOT
showed also that the radiometric calibration is not as stable as required for automated data
processing.
Three sources of in situ measurements for validation were available:
1) Aanderra turbidity sensors operating continuously at different locations (LZ1,2,3 and D2,3,4)
in river Elbe. The output values of the different turbidity sensors were not yet intercalibrated as
provided by the user. Measures closest to the water surface and matching in time with satellite
measurements were however compared in the Figure 49 for satellite records MODIS and
RapidEye.
2) Gravimetrically estimated Total Suspended Matter taken at a field campaign of user BfG on
June 30 2010. None of the measurements matched in time with the satellite record MODIS, but
were still used for comparison (see Figure 49 blue symbol).
3) Gravimetrically estimated Total Suspended Matter taken at a field campaign of user BAW on
June 21 and 23. Time matching measurements with MODIS and RapidEye are displayed (red
dots in Figure 49).
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Figure 49. Elbe in situ measurements in relation to TSM recalibrated satellite data
The strong impact of the tide and changing currents cause extreme fast temporal and spatial changes
of turbidity in river Elbe. This is demonstrated both by the in situ measurements of the authorities, but
also by many remote sensing records where we had multiple satellite records per day available (see
Figure 50).
Figure 50. Time series of suspended matter monitoring of the River Elbe using MODIS Terra and Aqua 250m
satellite data
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Therefore only the time matching comparisons such are useful for a reliable validation. We used the in
situ measurements of BAW from June 21 and 23, 2010 to establish a new relation between satellite
derived scattering and total suspended matter:
Total Suspended Matter value [mg/l] = TURsat+ 0.00285 * ((TURsat /10)^3.9)
6.4.1 User comment
The User from Federal waterways engineering and research institute BAW made following statement:
“Understanding the transport processes of suspended sediments in waterways is essential to reduce
river engineering expenses such as dredging. As the federal authority BAW is responsible for the
German waterways, we optimize waterway engineering for example in the estuaries of the Elbe, Weser
and Ems rivers. Measurements in combination with 3D hydrodynamic modelling are a key to
understanding the processes and optimizing engineering works. The area-wide remote sensing products
of suspended matter provided by the FRESHMON consortium give very valuable synoptic measurements
of suspended matter surface concentrations. We expect that the anticipated temporal and spatial
resolution of observations with the GMES satellite fleet will significantly improve our daily work.”
6.4.2 Further steps
The provision of further in situ data sets has been requested from the user.
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Validation in Switzerland (EOMAP)
7.1 Lake Zurich
In situ data for three stations in Lake Zurich has been provided from the user Water Supply Zurich,
containing Chl-a measurements in [µg/l] at different depths. In this report only the results for station
Thalwil (SZHTH) are described, which is located in the middle of Lake Zurich (E 8°35’43.11’’ N
47°17’11.9’’).
Figure 51. In situ measurement stations Lake Zurich SZHTH, SZHMO, SZHLE
For Lake Zurich the same processing steps were done as for the Lake Constance time series, using
MERIS data from the years 2003-2011, see 6.2.
In Figure 52 the time series of satellite Chl-a and in situ data is shown. The standard deviation of the
satellite derived Chl-a concentrations was calculated with also taken into account the quality values.
The quality is visualized with colors (see also 6.2.3). The quality of the pixels was lower than in other
lakes (maximal 60%) and for visualization reasons the figure was adjusted to this value (green means
>66% of 60% total quality).
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Figure 52. Chl-a concentration derived from MERIS satellite data and in situ measurements from the WVZ for
the year 2006-2011
For further comparison of the data, the years 2010 and 2011 are shown. In Figures 53 and 54 one can
see that the trend of the in situ data can be follow with the satellite data and the data generally fits
together. The different sampling depths have partly a large influence on the Chlorophyll-a
concentrations. For further comparisons, the Z90 values of the satellite should be taken in to account.
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the year 2010
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the year 2011
7.1.3 User comment
The validation results have been provided to the end-user of the Zurich Water Supply. Feedback is
promised until end of November. A special workshop to further discuss the results is foreseen in the
upcoming months.
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Conclusions
8.1 Quality of in situ data
The results show that it is still quite difficult to obtain enough good quality in situ data for satellite
product validation. Especially time coincidence is a source for errors. Specific campaigns (see e.g. the
Lake Constance campaign in Chapter 6.1) can overcome this problem and provide many data points
but if there is cloud cover the data in unusable. The campaigns are also time- and cost- intensive.
Automated stations making continuous measurements can provide data that is not as sensitive to
cloud cover but their spatial coverage is limited and the data must often be used with care (see e.g.
Chapter 4.1.1). Also the measurement depths of in situ data need to be considered more.
8.2 Quality of satellite products
Despite the problems with in situ data, in many cases the validation analysis shows that the satellite
products have high quality. However, further development and validation are still necessary in order to
satisfy user needs.
The following gives a short overview of validation results from each service provider that participated
in the validation WP:
SYKE:
In Finland the estimation of chl-a with MERIS works well for low humic concentration lakes. Algorithm
development is still needed for lakes with higher humic content. The high resolution turbidity products
from Rapid Eye images show reasonable accuracy (the estimation accuracy of Secchi depth was
better). More work is needed especially on masking the areas influenced by bottom reflectance.
EAWAG:
The results from Lake Constance show that the inter-calibration between in-situ methods is highly
satisfactory. Regarding satellites, our computer simulations suggest that there is a lot of time
variability, so correlations with satellite improve if we restrict the comparison in a time interval around
the "shot".
EOMAP:
The outputs from the EOMAP MIP processor were validated for various inland waters with hundreds of
satellite records over a period of 10 years, at river Elbe and many Alpine Lakes, running in a
harmonized configuration for various satellite sensors.
The satellite product for TSM is showing overall very good validation results. Chl-a product seems
largely processor and lake depended, however the processor configuration was not yet optimized to
the different Lake types when applied for a wide range of Alpine Lakes. In the current configuration
Chlorophyll is physically direct related to the organic absorption. As Chlorophyll product this works well
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for Lake Zurich and – up to 2010 – for Lake Constance. For several Bavarian Lakes obviously other
organic absorbers then phytoplankton pigments play a dominant role. We still have to perform
detailed analysis and hope to improve the product in terms of Chlorophyll also here. In general, as a
result of the validation of the time series the usage of satellite products can be regarded as a
harmonized measurement method, which may be used as proxy for the ecological status of the inland
waters (proposal of the user water authorities).
WI:
In the Netherlands, with data from year 2011 a good correlation between MERIS-ICOL-C2R images and
WISP-3 measurements was found. From the data of 2012 we can conclude that optical measurements
with the WISP-3 can be used to derive concentrations of water quality parameters, such as Chl, SPM
and Phycocyanin from Lake Ijsselmeer and Markermeer. Currently we are working on the tuning of the
algorithm to derive these concentrations also from EO data, so we can deliver the quality-controlled
EO based maps by the end of 2012 to our user.
8.3 Quality of data from in situ devices
The validation of in situ devices (WISP-3 and Secchi3000) show a similar situation as for the satellite
products: In some cases the devices work well but further work is needed in order to improve the
performance. Nevertheless, the ability of WISP-3 to quickly measure in situ reflectance is an advantage
when in situ measurements are performed.
References
Doerffer R. 2002. Protocols for the validation of MERIS water products. GKSS Doc. No. PO-TNMELGS-0043. 46 pp. http://envisat.esa.int/workshops/mavt_2003/MAVT-2003_801_MERISprotocols_
issue1.3.5.pdf
Doerffer R, Schiller H (2006a) Algorithm Theoretical Basis Document MERIS Case II ATBD-ATMO MERIS
Regional Case 2 water, BEAM extension atmospheric correction ATBD. Version 1.1, 24. p. 25
Doerffer R, Schiller H (2006b) Algorithm Theoretical Basis Document MERIS Regional Case 2 water
BEAM extension performance of the atmospheric correction part: sun glint correction. MERIS Case 2
ATMO-Test. p. 14
Chiwara, 2012. Monitoring blue-green algae in the IJsselmeer, using remote sensing and in-situ
measurements. Thesis, ITC Enschede, the Netherlands, 71 pp. Available from:
www.itc.nl/library/papers_2012/msc/wrem/chawira.pdf
2012-10-31
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Dogliotti, A.I., Ruddick, k., Nechad, B., Lasta, C.. Improving water reflectance retrieval from MODIS
imagery in the highly turbid waters of la Plate River. Proceedings of VI International Conference
Current problems in optics of natural waters (ONW’2011), St. Petersburg, Russia, 2011
Gons, H.J., Rijkeboer, M., and Ruddick, K.G., 2005. Effect of a waveband shift on chlorophyll retrieval
from MERIS imagery of inland and coastal waters. J. Phytopl. Res. 27, 125-127.
Heege, T., van der Piepen, H., Fischer, J. & Amann, V. (1998): Gewässerfernerkundung am Bodensee:
Verfahren und Anwendungsbeispiele. DLR Forschungsbericht 98-22
Heege, T., Häse, C. , Bogner, A., Pinnel, N. (2003): Airborne Multi-spectral Sensing in Shallow and Deep
Waters. Backscatter p. 17-19, 1/2003
Heege, T., Kiselev V., Gebhard S., Huth J., Trinh Thi Long, Vo Khac Tri (2009). Processing of Multiple
Sensor Images of Aquatic Systems. Proc. of 33rd International Symposium on Remote Sensing of
Environment (ISRSE), May 4-8, 2009, Stresa, CD-ROM publication.
Hommersom, A., Kratzer, S., Laanen, M., Ansko, I., Ligi, M., Bresciani, M., Giardino, C., Beltran, J.,
Moore, G., Wernand, M., Peters, S., submitted. An inter-comparison in the field between the new
WISP-3 and other radiometers (TriOS 4 Ramses, ASD FieldSpec, and TACCS). Submitted to the Journal
of Applied Remote Sensing.
Kisselev, V. and B. Bulgarelli. Reflection of light from a rough water surface in numerical methods for
solving the radiative transfer equation. J. Quant. Spectrosc. Ra., 85:419–435, 2004.
Odermatt, D., V. Kiselev, T. Heege, Kneubühler, M., Itten, K.I. (2008)b: Adjacency effect considerations
and air/water constituent retrieval for Lake Constance. Proc. 2nd MERIS/AATSR Workshop, 22-26 Sep.
2008, Frascati, Italy, CD-ROM.
Rijkeboer, M., 2000. Algoritmen voor het bepalen van de concentratie chlropfyl-a and zwevend stof
met de Optische Teledetectie Methode in verschillende optische watertypen. [in Dutch]. Insituut voor
Milieuvraagstukken, Amsterdam (2000).
Ruddick K., Ovidio F. & Rijkeboer M. (2000). Atmospheric correction of SeaWiFS imagery for turbid
coastal and inland waters. Applied Optics, Vol. 39(6), pp. 897–912.
Peters, S.W.M. (in preparation): The Water-optics Iterative Semi-analytical Processing Framework for
seamless water quality estimation in case1 to case 2 waters.
Nixdorf, B., Hemm, M., Hoffmann, A. & Richter, P.(2004): Dokumentation von Zustand und
Entwicklung der wichtigsten Seen Deutschlands Teil 11 Bayern [in German]. Brandenburgische
Technische Universität Cottbus, Lehrstuhl Geässerschutz.
Santer R, Zagolski F (2009) Algorithm Theoretical Basis Document— the MERIS level 1c. Issue 1, rev. 1,
6 Jan 2009. p. 15
Simis, S., 2006. Blue-green catastrophe: remote sensing of mass viral lysis of cyanobacteria. Ph.D.
thesis, Vrije Univ. Amsterdam.
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Appendix
User feedback and validation report from SYKE Fresh Water Centre:
FRESHMON_User_feedback_and_validation_Report_SYKE_FWC_20121030.doc
2012-10-31
73
User feedback and validation report
User:
Ahti Lepistö
Email:
Organisation:
SYKE FWC
Provider:
Address:
[email protected]
Sampsa Koponen
SYKE/GEO
SYKE Freshwater Centre
P.O.Box 140
[email protected]
FIN-00251 Helsinki, Finland
User requirements
Please evaluate the service (check with an “x”):
1. Availability of the required products/information
Very satisfying
unsatisfying
2. Accessibility of the delivered products/information
very efficient
inefficient
3. Readability / Comprehensibility, Reliability
fine
complicated
4. Validity of the delivered products
high
low
5. Is the service of benefit for your current work (please check with an “x”)?
I totally
disagree
I disagree
I partly
agree
I agree
Cost reduction
x
Improved spatial coverage
x
Improved temporal coverage
x
Optimisation of in-situ expenditure
x
Others (please specify)
6. Which expected user needs does the service meet/not meet?
I absolutely
agree
The service meets the demand when planning new approaches for the lake and coastal water quality
monitoring, in large spatial scales. These new approaches still need a considerable amount of work
(standardization, verification, comparability etc.) before operational use.
7. Please list any important improvements of the service/product to be implemented:
Further efforts to reduce the so called ‘coastal errors’, and further efforts to test the product in
varying types of lakes (see also 12)
8. Is the spatial/temporal coverage of the product sufficient? If not, which coverage requirements
do you have?
In large lakes it is sufficient, but in smaller ones the spatial resolution could be higher if possible.
The lakes with a high number of islands provide an extra challenge.
Product is not ready for humic lakes, because chlorophyll a is not yet estimated well enough.
Temporal coverage may not be sufficient enough if there is too much cloudiness in summer period.
9. Is the product quality and validity compliant with the specifications and user requirements?
Yes, it is ok for us. If we think of citizens as users of the products, the colour scales of the water
quality maps should be thought carefully: e.g. to separate somehow between ‘normal’ springtime
algae blooms and harmful blue-green algae blooms typically occurring in late summer.
10.Please evaluate the available feedback possibilities to provide independent data for product
validation:
very sufficient
insufficient
11.Please list any suggestions to be integrated into the feedback possibilities:
Water quality data banks of SYKE include large amounts of spatial and temporal independent
data which can be used for product validation. There are numerous small lakes in Finland
that are not sampled at all. By grouping similar lakes and using sample data to calibrate the
satellite products the coverage of monitoring can be improved.
12.(Optional) General comments regarding validation activities.
Use of real-time lake float data sets seems to be an efficient tool in validation of the results.
These activities should be expanded to variable types of lakes (with varying eutrophy, humus
content etc.)

WP54.3.1Report on FRESHMON data quality and data comparability

Transcription

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