BONUS FERRYSCOPE year 1 report

Transcription

FerryScope
Summary of Annual Report 1
July 2014 – June 2015
Version 1.0
30.06.2015
Martin Boettcher, Brockmann Consult
Jenni Attila, Mikko Kervinen,
Seppo Kaitala, SYKE
Tiit Kutser, EMI
Project full title:
Bridging the divide between satellite and shipborne sensing for
Baltic Sea water quality assessment
Project coordinator:
Dr. Martin Boettcher, Brockmann Consult GmbH
phone: ++49 4152 889 315 email:
[email protected]
Project applicants:
Brockmann Consult GmbH (BC), Germany
Finnish Environment Institute (SYKE), Finland
Estonian Marine Institute (EMI), Estonia
Grant identifier:
BONUS call2012inno-38
FerryScope Annual Report 1
Brockmann Consult GmbH
Table of Contents
1
Project FerryScope ..................................................................................................................... - 1 -
2
Work performed in 1st year of FerryScope ............................................................................... - 1 2.1
In situ Rrs data provision framework (WP1) ....................................................................... - 1 -
2.2
Innovative approaches in EO data interpretation (WP2) .................................................... - 2 -
2.3
Data assimilation engine (WP3) ........................................................................................ - 10 -
2.4
Service Deployment (WP4) ............................................................................................... - 11 -
3
Main results, achievements, and impact ................................................................................. - 11 -
4
References ............................................................................................................................... - 12 -
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1 Project FerryScope
FerryScope aims at improving water quality assessment of the Baltic Sea by the combination of satellite
data, time series of shipborne Rrs measurements, other in-situ data, and improved algorithms and
models. The FerryScope project has started in July 2014. FerryScope is a BONUS project.
The FerryScope objectives are
•
To provide quality assured, freely accessible NRT Rrs from ships of opportunity (SOOP)
•
•
To improve the accuracy of EO products by data assimilation with shipborne Rrs
To investigate the sources of uncertainty in EO products, particularly near the coast, and to
improve the underlying algorithms using large volumes of aggregated optical observations
•
To familiarize users (managers, researchers) with the new information system and to
streamline adoption of the system in national monitoring agencies
•
To develop an open framework for automated and continuous in-situ and satellite
measurement ingestion, processing and provision
•
To develop the commercial service model on top of the open source technical model
2 Work performed in 1st year of FerryScope
Main focus of the first year was on the in-situ data framework and the development of the spectral
library for data interpretation.
2.1 In situ Rrs data provision framework (WP1)
During WP1 an online OGC-compliant Web Feature Service for the Rflex in-situ data time series was
developed. FerryScope in-situ data server serves a combination of remote-sensing reflectance (Rrs)
and time-matched flow-through ferrybox data. The service can be accessed online at
http://ferryscope.ymparisto.fi/Rflex/index.xhtml
The interface is fully documented inFerryScope deliverable D1.2 Rflex WFS API Reference also
accessible at
http://ferryscope.org/wp-content/uploads/2014/10/FerryScope-D1.2-RflexAPIQuickStart-v1.0.4.pdf
The set of measurements served is extended daily shortly after one of the two ferrys reaches a harbour.
The Web Feature Service is a machine-to-machine interface. The provided web pages with a graphical
user interface only provide limited capabilities compared to the WFS. A detailed description is given in
[D1.2].
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Figure 2-1: Spatial distribution and temporal coverage of the FerryScope Rrs in-situ data time series (continuously
extended)
An OGC Web Feature Service client has been implemented to retrieve Rrs data from the FerryScope insitu data server. This client can be downloaded by users from the FerryScope web site
(www.ferryscope.org). It is implemented in Python. The client is installed in the Calvalus processing
environment at Brockmann Consult to provide all the Rrs data to the FerryScope Data Assimilation
Engine where also the Earth observation satellite data is processed. After the FerryScope in-situ data
service had been extended also to serve FerryBox data matched with Rrs measurements the client has
been extended to retrieve this combined in-situ data records, too.
2.2 Innovative approaches in EO data interpretation (WP2)
Remote sensing data can be interpreted in two main ways. The “classical” approach is developing bandratio-type or more sophisticated algorithm that describe the shape and magnitude of reflectance
spectrum with one number and then studying the regression between these numbers and water
quality parameters (like chlorophyll, turbisity, etc.). These statistical algorithms are computationally
simple and easy to apply. However, the relationships between the band-ratio-type algorithms and
water parameters are varying from site to site (i.e. there is need in tuning the algorithms to local
conditions) or the algorithms may not describe the water reflectance spectra at all (e.g. blue to green
band ratios do not work in optically complex waters).
More innovative approaches use the full spectrum measured by a remote sensing sensor and retrieve
either IOPs (absorption and backscattering coefficients) or concentrations of optically active
substances (CDOM, Chl, TSM) simultaneously. In principle, both measured in situ reflectance spectral
libraries and modelled spectral libraries can be used. For example SIOCS (The Sensor-Independent
Ocean Colour Processor) retrieves concentrations of optically active substances from reflectance data
based on a LUT derived from a spectral library. Using only Rflex and ferrybox data collected during the
FerryScope project would be biased towards the open parts of the Baltic Sea and towards the summer
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season. Therefore, we used also a modelling approach that allowed us to create a spectral library that
covers concentrations of optically active substances that occur in coastal waters and represents both
spring and summer conditions.
We use both “classical” approach and the LUT approach in the FerryScope project in order to find
optimal methods for interpreting remote sensing data.
2.2.1 WP 2.1 Developing of data quality filters (identification of glint, processing errors,
and other artefacts)
Rflex spectra collected in the frame of the FerryScope project (together with the water quality
parameters collected simultaneously by ferybox systems) can be used as an in situ spectral library for
processing satellite data, as a reference data needed for evaluating the performance of atmospheric
correction, or as a remote sensing database on its own right that can be used to retrieve water quality
parameters from it in case the ferrybox data is missing (i.e. if the ship of opportunity has only Rflex
system on board). In all these cases high quality reflectance data is needed. The Rflex system is
designed to collect data under angles that should provide the best quality and avoid artefacts like glint
or ship shadows. Nevertheless, the Rflex data has to be checked carefully and all suspicious data has
to be removed.
In this task we investigated several methods how to improve filtering of the Rflex data in order to
provide output reflectances we are confident in. The automated Rflex filtering utilizes methods
described in Simis and Olsson (2013). Additional rules below are used for removing bad Rrs spectra
out of the measured Rflex dataset. Rrs spectra are filtered based on the spectral information only.
R400<RPEAK> RNIR(800) = no anomalies
White errors removed only with threshold Rflex(λ400 )< 0.001 Rflex
wavelengths used for filtering:
λNIR =(λRrs >= 795 nm & λRrs < 810 nm )
λPEAK = (λRrs >= 570 nm & λRrs < 590 nm ) λ400
=(λRrs >= 395 nm & λRrs <= 405 nm) λ500 = (λRrs
>= 495 nm & λRrs <= 505 nm ) λRED = (λRrs >= 648
nm & λRrs <= 672 nm ) λ762 .2= (λRrs == 762.2nm
) λ765.5 = (λRrs ==765.5nm ) λ450 = (λRrs >=
445nm & λRrs <= 455nm) λ650 = (λRrs >= 645nm
& λRrs <= 655nm) Filtering rules:
•
Rrs (λPEAK) > Rrs (λ650) AND
•
•
Rrs (λ450) > Rrs (λ400) AND
Rrs (λNIR) < 0.001 AND
•
•
Rrs(λRED)/ Rrs (λNIR) > TH AND
Rrs (λ400)< Rrs (λ500) AND
•
•
Rrs(λ765.5 )/Rrs(λ762 .2)< 1.015)
TH = 1 -1.5 (varies according to season)
Figure 2-2 shows the resulting spectras after using the filtering rules above. Note that the Figures show
the wavelength range ( RRS >= 400 nm & RRS <= 900 nm).
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Figure 2-2: Examples of filtered Rflex spectra on different periods of annual measurement period. a) Spring cases a)
20.4.2014 and b) 13.4.2014, c) summer minimum 6.6.2014 and d) cyanobacteria bloom period 21.7.2014. Median is taken
at based on wavelength
2.2.2 Parameterising HydroLight radiative transfer model for simulation of Baltic Sea Rrs
spectra
As was mentioned above, collecting the spectral library that contains examples from spring and
summer bloom conditions and from varios coastal waters is time consuming and expensive even if we
have such powerful data collection system like the Rflex-ferrybox pair. For example, extreme blooms
occur with such frequency that capturing them within a two-year project is unrealistic. Therefore, it is
reasonable to create a modelled spectral library. For example, using the HydroLight radiative transfer
model. In the FerryScope project the parameterization of the Hydrolight model was done based on
extensive field campaigns on the open Baltic Sea and on coastal waters of Estonia and Sweden. The
procedure is described in detail in D2.1. The outcomes of this work are Look-upTables that can be
futher utilized together with SIOCS (The Sensor-Independent Ocean Colour Processor) processor, see
2.2.5 below.
Specific inherent optical properties of Estonian and Swedish coastal waters proved to be extremely
variable and often different from the SIOPs of open parts of the Baltic Sea. Therefore, the D2.1 contains
a modelled spectral library for the open parts of the sea and for two seasons with distinctly different
SIOPs as well as in situ spectral libraries for near coastal waters. Analysis on the near coastal SIOPs are
still work in progress and it is not determined yet in which parts of the Baltic Sea we can use the open
sea spectral library and which part of the near coastal waters require different libraries for processing
the remote sensing data.
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2.2.3 Creating Baltic Sea Rrs spectral library for interpretation of satellite data
The open Baltic Sea spectral library has been created using the procedure documented in D2.1 and
given as a D2.2 LUT file. This spectral library is complemented with Excel files of the actual
measumements made on ship cruises on the open Baltic sea and on coastal waters of Estonia and
Sweden. The LUT delivered is compatible to SIOCS model and their joint use will be tested and utilized
during the second year of the FerryScope project. The work on developing further the coastal model
continues during the FerryScope project.
b)
a)
Figure 2-3: Example of modelled spectras with a) varying pigment absorption (from 0.0061 to 2.27 with 14
steps), nadir viewing angle and sun angle 30o and b) Example of modelled spectras with varying b_part (from
0.01 to 10.013), nadir viewing angle and sun angle 30o.
2.2.4 Improving empirical algorithms for retrieving water characteristics from satellites
Evaluating the performance of different band-ratio-type remote sensing algorithms requires
statistically large amount of test data. We have reflectance data from 42 sampling stations in Estonian
and Swedish waters that is accompanied by the concentrations of optically active substances as well
as SIOPs and IOPs. Minimum and maximum concentrations of optically active substances in these sites
are given in Table 2-1.
Table 2-1: Concentrations of optically active substances observed in Estonian and Swedish coastal waters and used in
testing the empirical algorithms
Chl, mg/m3
TSM, mg/l
aCDOM(400), m-1
Min
0.79
1.2
0.18
Max
9.03
8.8
3.92
The in situ database is too small to draw any major conclusions about suitability of the empirical
algorithms. Therefore, we used the modelled spectral library created in the WP2 and described in the
D2.2 in testing the empirical algorithms. The modelled LUT contains 655 200 simulated Rrs spectra in
for spring and 561 600 for summer. The creation of spectral library and its input parameters have been
documented in [D2.1] (Attila et al., 2015). The first version of spectral library is included in [D2.2].
Concentrations used in creating the spring and summer LUTs are shown in Table 2-2 and Table 2-3.
Table 2-2: Concentration steps used in the spring simulations
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Variable
N
Chl (µg l-1)
0.10
1.0
2.0
4.0
6.0
8.0
10.0
14.0
20.0
26.0
32.0
42.0
TSM(mg l-1)
0.05
0.4
0.8
1.3
1.8
2.3
4.0
5.7
7.4
8.9
18.0
50.0
aCDOM412(m-1)
0.05
0.2
0.3
0.5
0.7
0.9
1.2
1.5
3.0
20.0
84.0
250.0
14
12
10
Table 2-3: Concentration steps used in the summer simulations
Variable
N
Chl (µg l-1)
0.10
0.8
1.7
2.5
3.3
4.2
6.2
8.2
10.2
12.8
26.0
120.0
12
TSM(mg l-1)
0.05
0.2
0.3
0.8
1.3
1.8
3.4
5.0
6.6
8.1
16.0
50.0
12
aCDOM412(m-
0.05
0.2
0.3
0.5
0.7
0.9
1.2
1.5
3.0
20.0
10
1
)
The band ratio algorithms used in this study were mainly chosen from those developed for optically
complex coastal and inland waters rather than open ocean waters. Authors of the algorithms and
equations are provided in Table 2-4.
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Table 2-4: Summary table of the empirical remote sensing algorithms used in this study
Reference
Equation
Regression type
General form
Code
Chlorophyll
Zimba, 2006
(1/R650-1/R710)*R740/0,0003+17,33
linear
(1/R650-1/R710)*R740
CHL1
Moses ,2009a
(R665e-1-R708e-1)*R753
linear
(1/R665-1/R708)*R753
CHL2
Gitelson, 2009
(R670e-1-R710e-1)*R750
linear
(1/R670-1/R710)*R750
CHL3
Mayo, 1995
0,164((R450_520-R620_690)/R530_610)^-0,98
power
(R485-R660)/R570
CHL4
Hunter, 2008b
log10(L710/L670)
logaritmic
log10(R710/R670)
CHL5
Han, 2005
-9,5126+12,8315*(log R450_515/log R630_690)
linear
logR482.5/logR660
CHL6
Schalles, 1998
max(670-850)-670-850 line value at the location of maximum
linear
max(670-850)-670-850 line value at the location of maximum
CHL7
Brezonik, 2005
-1,7237*L450_515/L630_690+9,6487
linear
R482.5/R660
CHL8
Östlund, 2001
1409*(R525_605/(R450_515+R525_605+R630_690)-421,4
linear
R565/(R482,5+R565+R660)
CHL9
Wang, 2006
381,932-259,602*R630_690/R525_605
linear
R660/R565
CHL10
Dierberg, 1994
151,6*L689_698/L673_685-114,6
linear
R693.5/R679
CHL11
Duan, 2007
93,67*R700/R670-90,4
linear
R700/R670
CHL12
Menken, 2006
5,91*(R700/R670)^4,96
power
R700/R670
CHL12
Dierberg, 1994
174,5*R690_710/R673_687-156,6
linear
R700/R670
CHL12
Kutser, 1999
89,8*L701/L673-64,1
linear
R701/R673
CHL13
Kallio, 2001
L699_705/L670_677
linear
R702/R674
CHL13
Koponen, 2007
166*L705/L663-106
linear
R705/R663
CHL14
Ammenberg, 2002
85.01*R(705)/R(664)-51
linear
R705/R664
CHL14
Kallio, 2003
108,5*(L700_710/L680_665)-68,7
linear
R705/R673
CHL15
Kallio, 2003
112,1*(L700_710/L680_665)-77,1
linear
R705/R673
CHL15
Kallio, 2001
L699_714/L670_685
linear
R706.5/R677.5
CHL16
Kallio, 2001
L699_714/L661_667
linear
R707.5/R664
CHL17
Moses, 2009a
R708/R665
linear
R708/R665
CHL17
Kallio, 2001
L705_714/L670_677
linear
R709.5/R673.5
CHL18
Jiao, 2006
0,0282*(R719/R665)^3,0769
power
R719/R665
CHL19
Härmä, 2001
(L705-L754)/(L665-L754)
linear
R730/R710
CHL20
Härmä, 2001
(L705-L775)/(L665-L775)
linear
R735/R720
CHL21
Moses, 2009b
R748/R667
linear
R748/R667
CHL22
Yacobi, 1995
-32,35+43,08*Rmax/R670
linear
Rmax(670-850)/R670
CHL23
Schalles, 1998
sum 670-850 - sum 670-850 linear - %
linear
sum 670-850 - sum 670-850 linear - %
CHL24
Total Suspended Matter
Dekker, 2002
0,7517*e^65,736((R500_590+R610_680)/2)
exponential
(R545+R645)/2
TSM1
Dekker, 2002
0,7581*e^61,683((R525_605+R630_690)/2)
exponential
(R565+R660)/2
TSM2
Kutser, 1999
6,2*(Lmax-L750)/(L476-L750)-8,59
linear
(Rmax-R750)/(R476-R750)
TSM3
Kutser, 2014/2015??
812-(770-840)base
linear
812-(770-840)base
TSM4
Wang, 2001
e^(5,6394+1,5493*ln((R630_690+R750_900)/(R450_515+R525_605))
logaritmic
ln((R660+R825)/(R482,5+R565))
TSM5
Neukermans, 2009
38,02*R635/(0,162-R635)+2,32
linear
R635/(0.162-R635)
TSM6
Miller, 2004
-1,91+1140,25*M1
linear
R645
TSM7
Doxaran, 2006
29,022 e^(0,0335 R750_900/R525_605)
exponential
R660/R565
TSM8
Wang, 2006
46,638-27,062*R630_690/R525_605
linear
R660/R565
TSM8
Kallio, 2001
R699_705
linear
R702
TSM9
Kallio, 2001
R699_705 - R747_755
linear
R702-R751
TSM10
Koponen, 2007
1,47*L705+0,13
linear
R705
TSM11
Ammenberg, 2002
174.8*R(705)-0.12
linear
R705
TSM11
Thiemann, 2000
-52,9+73,6*R705/R678
linear
R705/R678
TSM12
Härmä, 2001
L705-L754
linear
R705-R754
TSM13
Kallio, 2001
R705_714
linear
R709,5
TSM14
Doxaran, 2006
27,424 e^(0,0279 R790_890/R500_590)
exponential
R825/R565
TSM15
Doxaran, 2003
29,022*e^0,0335(R750_900/R525_605)
exponential
R825/R565
TSM15
Onderka, 2008
4,17*L(TM4)-43,22
linear
R840/R545
TSM16
Doxaran, 2003
27,424*e^0,0279(R790_890/R500_590)
exponential
R840/R545
TSM16
Doxaran, 2003
18,895*e^0,0322(R790_890/R500_590)
exponential
R840/R545
TSM16
Doxaran, 2005
3.2846*(R850/R550)*100-7,3959
linear
R850/R550
TSM17
Doxaran, 2002a
162,03x^3-394,45x^2+339,88x+1,027
polynomial
R850/R550
TSM17
Doxaran, 2003
26,083*e^0,0365(R855/R555)
exponential
R855/R555
TSM18
Colored dissolved organic matter
Brezonik, 2005
23,65-0,3528*L450_515-0,657(L450_515/L750_900)
linear
L482,5-0.657(L482,5/L825)
CDOM1
Koponen, 2007
4,41*L663/L490-0,52
linear
L663/L490
CDOM2
Doxaran, 2005
10,253*(R400/R600*100)^-0,9149
power
R400/R600
CDOM3
Kallio, 2008
23,33*e^(-0,970 TM2/TM3)
exponential
R560/R660
CDOM4
Kallio, 2008
a*e^(b*TM2/TM3)
exponential
R560/R660
CDOM4
Kutser, 2005
5,13*R525_605/R630_690-2,67
power
R565/R660
CDOM5
Ammenberg, 2002
5.894*R(664)/R(550)-1.53
linear
R664/R550
CDOM6
Menken, 2006
R670/R571
linear
R670/R571
CDOM7
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Analysis of the results indicate that there was no algorithm that perfprmed well in the case of in situ
data. It does not mean necessarily that the algorithms are unsuitable in the Baltic Sea conditions. The
reason may be the the number of samples is small (42) and the range of concentrations isnot
representative for the whole Baltic Sea. Although the data from the sampling stations was carefully
evaluated and stations with suboptimal data quality were excluded, there still may be measuring errors
both in the case of reflectance measurements and further laboratory analysis.
The modelled spectral library covered wider range of concentrations than has be published for the
Baltic Sea in different studies we were abe to find. Also the number of samples used in the study was
over 1.2 million. There were several band ratio type chlorophyll algorithms that had very high
correlation (R2>0.8 and up to 0.97) with the chlorophyll concentration used in the model. Most of
these algorithms used spectral bands in the reflectance peak near 700-710 nm and/or from the
chlorophyll-a absorption feature near 660-680 nm. There were also several algorithms that produces
good correlation with TSM. These algorithms used spectral bands either in the 700-750 nm range or
hight of the reflectance peak near 810 nm. Three CDOM algorithms had correlation that was higher
than 0.8. All of these algorithm used different red to green band ratios. This was not surprising as the
reflectance in blue bands (where the CDOM influence on water colour is the highest) is negligible in
such high CDOM waters like the Baltic Sea.
2.2.5 Developing analytical method for retrieving optically active substances
concentrations from satellite data
The Sensor Independent Ocean Colour processor SIOCS (under development by BC) is an Earth
observation data processor for water quality that will provide spectral inversion algorithms for use with
several optical satellite sensors. It retrieves water constituents from optical satellite data using sensorspecific lookup tables (LUTs) that relate spectra to water constituents. Such LUTs can be generated
using radiative transfer models for the respective water type. The LUT’s described in D2.1 and delivered
as D2.2 were tested with current version of SIOCS. Initial test results were made using MERIS/OLCI
band configurations. The configuration of Baltic LUT and SIOCS model is under progress.
Using Rflex dataset from April 2015, the first experiments using SIOCS processor and Baltic LUT have
been generated. The results indicate that, using Rflex data, modified to MERIS band configurations, the
Baltic LUT and current version of SIOCS gives pigment absorption estimates that can be used to derive
realistic chlorophyll-a concentrations. The first test set contained Rflex reflectances at the end of spring
chl-a bloom period. The simulations with Baltic LUT and SIOCS led to concentrations between 3.2 - 5.45
µg/l. The algorithm testing and further specification will continue in the WP3.
The following figure shows the LUT browser tool for LUT inspection.
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v1.0, 30.06.2019
Figure 2-4: Part of the Baltic Sea LUT that has been derived from the model-generated Baltic Sea Spectral Library. The
spectra ara shown graphically and in tabular form in the LUT browser for LUT inspection.
During the second year of FerryScope project, the joint use of SIOCS and Baltic Sea LUT will be
experimented and tested thoroughly in varying optical conditions typical for the Baltic Sea. It is
foreseen, that both Baltic LUT and SIOCS will undergo updates before their full cababilities can be
utilized. However, the combination of Baltic LUT and SIOCS is a first attempt to generate a Baltic Sea
specific spectral inversion algorithm package that can be updated and modified for varying satellite
and field instruments.
2.2.4 Adjustment of vicarious calibration using Rflex data – example with MODIS
instrument data
Prior to the launch of Sentinel3a OLCI (Ocean Land Colour Instrument) instrument, the available
satellite instrument for testing the usability of Rflex data with Calvalus processing (match-up) system
is MODIS (Moderate Resolution Imaging Spectroradiometer) by NASA. Unfortunately, MODIS is at the
end of its lifetime and is degradating in time. The atmospheric correction by MODIS instrument on the
Baltic Sea shows very poorly corrected reflectance spectra - thus resulting in nonrealistic chl-a
concentrations [Darecki and Stramski 2004]. However, MODIS data allowed us to experiment Rflex
data capabilities using an instrument with non-optimal radionmetric calibration. A method for
adjusting the MODIS gain settings utilizing Rflex reflectance data as a reference was tested. First, initial
tests were made using 4 sample dates. The method and more specific Baltic gain parameters can be
futher optimized using Calvalus to generate automated Rflex and MODIS match-ups.
Examples of filtered Rflex spectra and different settings for gain values for MODIS data for different
seasons during the annual measurement period are given in Figure 2-5. Spring cases a) 20.4.2014 and
b) 13.4.2014, c) summer minimum 6.6.2014 and d) cyanobacteria bloom period 21.7.2014. The
atmospherically corrected MODIS data with original gain provided by SEADAS is given as green line,
other colors represent adjusted gains. The first versions of the Baltic Sea adjusted gains are given in
Table 2-5. Change is different for each MODIS band, varying between 0.2% to 3.2% from the original
gain (original given on the first column). No changes were made for bands with central wavelength
longer than 748 nm.
Table 2-5: MODIS default gain (by NASA, available on Seadas) and Baltic adjusted versions
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Gains/wavelenths
412
443
488
531
551
667
678
748
MODIS default
0.9731
0.9910
1.0132
0.9935
1.0002
0.9994
1.0012
1.0280
Baltic + SD
0.9858
1.0118
1.0472
1.0277
1.0358
0.9784
0.9902
1.0280
Baltic Sea gain
0.9809
1.0009
1.0416
1.0253
1.0322
0.9694
0.9812
1.0280
Baltic - SD
0.9760
0.9900
1.0359
1.0229
1.0286
0.9604
0.9722
1.0280
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Figure 2-5: Examples of filtered Rflex spectra and different settings for gain values for MODIS data. The
examples represent different seasons during the annual measurement period. Spring cases a) 20.4.2014 and
b) 13.4.2014, c) summer minimum 6.6.2014 and d) cyanobackteria bloom period 21.7.2014. The
atmospherically corrected MODIS data with original gain provided by SEADAS is given as green line, other
colors represent adjusted gains.
2.3 Data assimilation engine (WP3)
In WP 3 FerryScope develops the software environment for data harvesting, processing, assimilation,
and aggregation. This software environment is based on the Calvalus processing system platform at
Brockmann Consult.
As counterpart to the in-situ data service at SYKE the retrieval client for Rrs in-situ data and for
FerryBox in-situ data has been integrated into the processing system. All in-situ data acquired by
Finnmaid and Transpaper is available as input for FerryScope processing and validation on Calvalus.
Newly acquired data can be retrieved.
In preparation of the FerryScope processing chain the systematic harvesting and retrieval of Earth
observation satellite data from MODIS and VIIRS has been configured and is operational. All MODIS
L1A data since the beginning of 2015 and all VIIRS Level 1 data from beginning of March has been
retrieved systematically and is available for FerryScope on Calvalus. This is a prerequisite for a later
NRT water quality service of FerryScope. The time series of data is also required in FerryScope for
algorithm development, validation and improvement.
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11 -
The FerryScope processing chains on Calvalus automatically generate matchups between in-situ and
satellite data. This will be combined with the different processors and processing steps to be
integrated for FerryScope in the next period. Processing is currently being tested with the standard
NASA processor SeaDAS with different parameterisations and the comparison of results with actual
FerryScope Rflex data.
2.4 Service Deployment (WP4)
This WP formally has not yet started except for preparatory work. A list of users and groups interested
in FerryScope data and results is continuously extended. A request has been submitted to the
European Maritime Day 2016 coordinators in order to have a FerryScope user workshop at that event.
3 Main results, achievements, and impact
In agreement with the plan the WP1 (In-situ data framework) is finished, WP 2 (Earth observation data
interpretation methods) and WP3 (Data assimilation engine) are ongoing. WP 4 (Service deployment)
is planned to be started in January 2016.
Figure 3-1: Deliverable D1.2 – detailed description how to access the in-situ Rrs data service of FerryScope
The deliverables D1.1 and D1.2 have been delivered (and accepted). D1.1 is the in-situ data service
itself that is accessible online (http://ferryscope.ymparisto.fi/Rflex/index.xhtml and
http://ferryscope.org/) and continuously serves data that is acquired daily. D1.2 is the report that
describes structure, content and access to the data.
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Figure 3-2: Deliverable D2.1 – Description of the Spectral Library for the Baltic Sea
Also deliverables D2.1 and D2.2 have been delivered (and accepted). D2.1 is the description of the
Hydrolight model for the Baltic Sea. The document also contains the format description of the Baltic
Sea Spectral Library. The Spectral Library itself is a database that is identified in D2.2. Scientific
publications on the Spectral Library are in preparation. After their publication the Spectral Library will
be made accessible online on the FerryScope web site.
4 References
The following documents are referenced in this document.
ID
Title
Issue
Date
[DoW]
BONUS project FerryScope Document of Work, Brockmann
Consult GmbH, Geesthacht
1.0
23.04.2014
[D1.1]
Rflex WFS Service Web Layer, BONUS project FerryScope
Deliverable D1.1, SYKE, Helsinki
1.0
13.04.2015
[D1.2]
Rflex WFS API Reference, BONUS
Deliverable D1.2, SYKE, Helsinki
FerryScope 1.0
13.04.2015
[D2.1]
Hydrolight Baltic, BONUS project FerryScope Deliverable D2.1, 1.2
SYKE, Helsinki and EMI, Estonia
30.06.2015
[D2.2]
Baltic Sea Rrs Spectral Library, BONUS project FerryScope 1.0
Deliverable D2.2, SYKE, Helsinki and EMI, Estonia
30.06.2015
[D2.3]
Algoithm Theoretical Basis Document
forthcoming
v1.0, 30.06.2015
project
13 -
[Darecki and
Stramski
2004]
Darecki M. and D. Stramski, 2004. An evaluation of MODIS and
SeaWiFS bio-optical algorithms in the Baltic Sea. Remote Sensing
of Environment, 89(3), 326-350.
2004
[Simis
and Simis, S. G. H., and J. Olsson. 2013. Unattended processing of
Olsson 2013] shipborne hyperspectral reflectance measurements. Remote
Sens. Environ. 135: 202–212. [doi: 10.1016/j.rse.2013.04.001]
2013
[Simis et al.
2014]
Stefan Simis, Jenni Attila, Mikko Kervinen: Automated
hyperspectral remote sensing from ships-of-opportunity in the
Baltic Sea: progress, system performance, and new services;
presentation, 6th FerryBox Workshop 2014, Tallinn, Estonia
(http://ferryscope.org/wpcontent/uploads/2014/10/SIMIS_rflex_baltic_FB2014.pdf)
09.09.2014
[Simis et al.
2015]
Stefan Simis, Linhai Li, Mariano Bresciani, Claudia Giardino, Lin Li,
Mark Matthews: Remote sensing of sun-stimulated fluorescence
from phycobilipigments, oral presentation, Association for the
Sciences of Limnology and Oceangraphy
(ASLO), Aquatic Sciences Meeting, Granada
27.02.2015
[Kutser et al.
2015]
Tiit Kutser, Stefan Simis, Martin Boettcher, Kari Kallio, Jenni
Attila, Carsten Brockmann, Birgot Paavel, Martin Ligi, Mikko
Kervinen, Seppo Kaitala: Improving the performance of remote
sensing products in optically complex waters, poster
presentation, Sentinel-3 for Science Meeting, Venice
04.06.2015
[Simis et al.
2015b]
Stefan Simis, Jenni Attila, Mikko Kervinen, Philipp Grötsch:
Phytoplankton products derived from automated along-track
hyperspectral reflectance: do we need satellites? Seminar,
University of Cape Town
05.06.2015
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BONUS FERRYSCOPE year 1 report

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