Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters evaluation of SeaWIFS data from 1997 to 1999 in the Skagerrak, Kattegat and North Sea.
Date: 2003-08-13
Institutions: European Environment Agency, Joint Research Centre
Subjects: Denmark, North Sea, Norway, environmental monitoring, eutrophication, marine pollution, remote sensing, territorial waters

Technical report

79

Remote sensing’s contribution
to evaluating eutrophication
in marine and coastal waters

Evaluation of SeaWIFS data from 1997 to 1999
 in the Skagerrak, Kattegat and North Sea

Prepared by:
Kai Sørensen, Gunnar Severinsen, Norwegian Institute for Water Research, Norway
Gunni Ærtebjerg (Task leader), National Environmental Research Institute, Denmark
Vittorio Barale, Christian Schiller, EC Joint Research Centre, Italy

Project Manager:
Anita Künitzer

2

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Cover design: EEA
Layout: Rolf Kuchling, EEA

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ISBN92-9167-471-0

©EEA, Copenhagen, 2002

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Contents

Summary  ............................................................................................................................. 4

1.

 Introduction  ................................................................................................................ 6

2.

 Objective  .................................................................................................................... 7

3.
 Data and methods ...................................................................................................... 8
3.1 The investigation area and sampling stations  ............................................................. 8
3.2 The analytical methods of chlorophyll-a and CDOM  .................................................. 9
3.3 Satellite data and image production  ........................................................................... 10

 Validation of SeaWIFS chlorophyll-a ......................................................................... 13
4.
4.1 Control of SeaWIFS chlorophyll-a estimates  ............................................................... 13
4.2 Effects of subsurface chlorophyll-a maximum  ............................................................. 16
4.3 Effects of coloured dissolved organic material, CDOM .............................................. 17

5.

 Monthly mean SeaWIFS chlorophyll images  ............................................................ 19

6.

 Summer mean chlorophyll images  ....................................................................... 24

7.

 Conclusions  ................................................................................................................. 29

Bibliography ........................................................................................................................ 31

Acknowledgements ............................................................................................................ 32

List of acronyms .................................................................................................................. 33

Appendix A Monthly mean SeaWIFS chlorophyll-a images from

September 1997 to December 1999, rescaled to in situ chlorophyll-a 
measurements, and compared to monthly mean in situ chlorophyll-a 
measurements at monitoring stations. .......................................................... 34

4

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Summary

Monitoring eutrophication in large 
marine and coastal areas is difficult, and 
the use of advanced monitoring tools, 
such as satellite remote sensing, can 
provide additional qualitative and 
quantitative information. The satellite 
data can be an important tool to support 
the evaluation of the study of the 
eutrophication in European marine 
waters. Although the remote sensing 
technique is limited to the surface layer of 
the water column, it is considered to give 
useful additional or complementary 
information to traditional ’in situ’ 
measurements to assess the state of the 
marine environment. The biomass of 
phytoplankton algae in surface waters can 
be measured as ’chlorophyll-a-like 
pigments’ by remote sensing. Enhanced 
levels of these pigments, compared to 
natural background concentrations of 
chlorophyll-a, are an indication for 
eutrophication.

The objective of this task was to evaluate 
estimates of  ’chlorophyll-a-like-pigments’ 
by satellite with focus on the SeaWIFS 
sensor (Sea-viewing Wide-Field-of-View 
Sensor). Chlorophyll-a maps were 
controlled against in situ data from the 
Skagerrak, Kattegat and the North Sea for 
the period September 1997 to the end of 
1999. Based on in situ data the 
chlorophyll-a maps were tuned to reflect  
’true surface concentrations’ of 
’chlorophyll-a-like-pigments’. The 
information and concentrations in the 
maps were locally compared with in situ 
data and a priori general knowledge of the 
area. 

The information in terms of necessary in 
situ data such as the different optical 
quantities of suspended and dissolved 
organic material as well as atmospheric 
data to do such a study was either not 
available or limited as this kind of data is 
not collected in ordinary monitoring 
programmes. We have therefore tuned the 
satellite images to in situ concentrations 
from empirical statistical analysis only.

The most severe problems retrieving a 
correct chlorophyll-a concentration from 
satellite images was in the near coastal 
areas, and the problems with 
overestimation of chlorophyll-a were 
increasing when approaching less saline 
water as at the Norwegian coast. The water 
masses in this area have normally high 
concentrations of dissolved organic 
material that influences the retrieval 
algorithms for chlorophyll-a. But knowing 
the limitation of the data both before and 
after rescaling to in situ concentrations the 
satellite data gives important information 
on the environment. 

The satellite data gives good information 
not only on the relative phytoplankton 
distribution, but also the concentrations 
when tuning the SeaWIFS-data to the 
surface concentrations using in situ data 
from the area. Using monthly mean maps 
in the period May to August gives much 
information on the phytoplankton 
biomass in the period when the 
production most often is nutrient limited. 
The mean or median value in this period 
would be the best and most appropriate 
expression for the level of eutrophication.

The algorithms to produce the 
’chlorophyll-a-like-pigments’ from the 
water using radiance from SeaWIFS 
observations are presently overestimating 
the ’true’ concentration. In the open sea 
of the tested areas the overestimation is in 
the order of 60-70 %. It was not the 
objective of this study to investigate the 
scientific rationale behind these findings. 

Very limited satellite data was available 
from the winter period November to 
February in this area. If the goal is to use 
the satellite data to map eutrophication 
this is not critical, since this is a period 
when the phytoplankton is light-limited, 
and is therefore not related to the 
nutrient input. The chlorophyll-a maps 
from the period March to 

Summary   5

April/May reflect realistically the 
magnitude and geographical distribution 
of the spring bloom, and therefore also 
the level of eutrophication. Since the 
spring bloom could reflect the magnitude 
of the available winter nutrient pool, a 
chlorophyll-a map showing the maximum 
concentration in each pixel should be 
useful for studying the eutrophication 
level.  

In general, the satellite data and 
information that has been investigated in 

this study gives promising possibilities to 
improve monitoring by a combination of 
in situ data and satellite data. New satellite 
sensors with improved spatial and spectral 
resolution, and new insight in solving the 
problems with the algorithms will improve 
the accuracy of the satellite data products. 
From a eutrophication monitoring point 
of view the number of stations may be 
reduced in the open sea areas when the 
quality of the satellite data are controlled 
on other stations with in situ data.

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Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

1. Introduction

The application of remote sensing 
techniques in monitoring marine and 
coastal waters have shown potential to 
provide synoptic data/information for a 
number of physical and bio-geochemical 
parameters. Remote sensing should have 
potential to support the evaluation of 
eutrophication in marine and coastal 
waters. In this context the possibility to 
measure the biomass of phytoplankton as 
‘chlorophyll-a-like pigments’ is promising.  
So far this has been difficult in coastal 
waters due to the influence by other 
optical constituents such as yellow 
substance and particles. New development 
of algorithms is ongoing and new sensors, 
such as MERIS (Medium Resolution 
Imaging Spectrometer) to be launched in 
2002, will improve the use of satellite data 
in coastal areas.  Although remote sensing 
techniques are limited to the uppermost 
few metres of the water column they are 
considered to give useful additional or 
complementary information to traditional 
in situ measurements to assess the state of 
marine and coastal waters. 

In general, in situ data are point 
measurements giving a good vertical 
resolution of the relevant parameters in 
the water column. However, it is very often 
difficult to derive an extra- or 
interpolation of, in situ data to prepare 
maps showing the status of coastal and 
marine areas. The satellite derived 
chlorophyll-a will be a valuable 
complement to the in situ measurements 
and will, for example, give important 
information about the representativity of 
the in situ stations.

Chlorophyll-a concentrations in summer 
periods are indicators of a semi-steady 
state situation where the production of 
phytoplankton is equalised by loss mainly 

through zooplankton grazing. High 
summer chlorophyll concentrations 
indicate high supply rates of nutrients and 
thus provide an indicator of the state of 
eutrophication. Satellite maps of 
chlorophyll in the surface waters will 
therefore help in assessing the spatial 
magnitude of phytoplankton biomass and 
eutrophication. 

Chlorophyll maps from satellite data are 
derived from algorithms based on the 
radiance signal in the visible part of the 
electromagnetic spectrum leaving the 
water. Cloudy conditions will limit the 
number of available satellite data since 
optical satellite sensors will not be able to 
map under such conditions. Therefore 
the analyses and conclusions of the 
report are highly dependent on the 
number of available satellite images. 
Fortunately, more images are available 
during the summer period than during 
winter.

The optical constituents: phytoplankton 
(measured as chlorophyll-a), inorganic 
suspended material and coloured 
dissolved organic material (CDOM) will 
contribute to the signal leaving the water. 
The algorithms to derive the chlorophyll-a 
concentration are developed for oceanic 
waters. In coastal waters these algorithms 
have, in the former CZCS (Coastal Zone 
Colour Scanner) (1979-1985) and in the 
present SeaWIFS (Sea-viewing Wide Field-
of view Sensor), observation problems in 
discriminating the three optical 
components with the result that the 
chlorophyll-a values normally are 
overestimated. The challenge and subject 
of this report is to extract meaningful 
information on chlorophyll-a 
concentrations in coastal and marine 
waters from the satellite images. 

Objective   7

2. Objective

The objective of this report is to evaluate if 
estimates of ‘chlorophyll-a-like pigments’ 
from satellite data can support the 
evaluation of eutrophication in European 
marine and coastal waters.

Information derived from selected remote 
sensing is used:

• to evaluate its meaningfulness for the 

assessment of the eutrophication status in 
marine and coastal waters, and, if applicable, 

• to compare the information derived from 

remote sensing techniques with monitoring 
data collected by Denmark, Norway and 
Sweden. 

• to assess the use of remote sensing as a 

eutrophication monitoring tool.

The SeaWIFS (Sea-viewing Wide Field-of 
view Sensor) images were used in 
combination with in situ measurements of 
chlorophyll-a to evaluate the applicability 
of satellite images for eutrophication 
assessment. Data from the launch of 
SeaWIFS in 1997 to the end of 1999 were 
used. The region to be considered, and 
which has sufficient monitoring data to 
allow this investigation, is the Kattegat, 
Skagerrak, and eastern North Sea. In situ 
data are here readily available and 
knowledge of the area from monitoring 
and research is considerable. 
Nevertheless, in situ data on optical 
constituents as dissolved organic material 
(yellow substance, or Gelbstoff) and 
suspended material is limited, but 
important in the evaluation. 

8

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

3. Data and methods

3.1 The investigation area and 

sampling stations

The investigation area, Skagerrak, 
Kattegat and eastern North Sea where 
field sampling was performed during the 
period 1998–1999 is shown in Figure 1. 
The sampling frequency of the stations 
varies considerably. Some stations are 
frequently monitored and others only 
occasionally. In situ data from Danish, 
Norwegian and Swedish waters is provided 
by different institutions and counties as 

shown in Table 1. The in situ data includes 
that from routine monitoring 
programmes run by NERI, NIVA, Institute 
of Marine Research (IMR) and Swedish 
Meteorological and Hydrological Institute 
(SMHI) as well as the different local 
monitoring projects run by the counties. 
The content of the in situ database is 
shown in the table, but for this report only 
data from the uppermost metre of the 
water column was used. 

Figure 1

The investigation area with the stations and sampling frequency of in situ data measured by different 
institutions in 1998 and 1999

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(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)
(cid:0)(cid:1)(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)
(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)(cid:0)(cid:1)

(cid:0)(cid:1)

(cid:0)(cid:1)

 Number of observations of Chlorophyll a in 1998-1999 with depth 1m or less. 

                                        <10     10-20    20-40      40-60      >60  number of observations 

 
 
  
Table 1

The following institutions have contributed with data to this evaluation. The total number of data 
(all depths) available for the period 1998 and 1999 is indicated in the table

Data and methods   9

Code

BOR

DMU

HFF

FRB

FYN

KBH

KBK

NIVA

NJY

RIB

RKB

ROS

SJY

SMH

STR

VEJ

VIB

VSJ

ÅRH

Name of institution

Number of data available in the
database

County of Bornholm, Denmark

National Environmental Research Institute, Denmark (NERI)

Institute of Marine Research, Research Station Flödevigen, Norway
(IMR)

County of Frederiksborg, Denmark

County of Fyn, Denmark

Copenhagen Municipality, Denmark

County of Copenhagen, Denmark

Norwegian Institute for Water Research

County of Nordjylland, Denmark

County of Ribe, Denmark

County of Ringkjøbing, Denmark

County of Roskilde, Denmark

County of Sønderjylland, Denmark

Swedish Meteorological and Hydrological Institute (SMHI)

County of Storstrøm, Denmark

County of Vejle, Denmark

County of Viborg, Denmark

County of Vestsjælland, Denmark

County of Århus, Denmark

65

1554

1693

164

533

415

35

335

426

220

124

32

250

1795

265

222

12

339

300

3.2 The analytical methods of 

chlorophyll-a and CDOM

Since the data is collected from different 
institutions the analytical techniques vary. 
Chlorophyll-a is determined with both 
spectrophotometric and fluorometric 
methods, as well as using different 
extraction methods. All Danish and most 
Swedish chlorophyll-a samples are 
analysed according to the Manual for 
Marine Monitoring in the COMBINE 
Programme of HELCOM 
(www.helcom.fi), which has shown good 
agreement between NERI and SMHI 
(HELCOM 1991). The Norwegian 
chlorophyll-a values are either based on 
spectrophotometric (NIVA) or 
fluorometric methods (IMR). Even so, 
differences in the methods used by 
different institutions can affect the 
evaluation. However, these differences 
might be less than the fact that the 
SeaWIFS algorithms are based o n 
chlorophyll-a data determined by HPLC 

techniques. The HPLC chlorophyll-a 
method gives lower chlorophyll-a values 
than the spectrophotometric or 
fluorometric methods since these are not 
corrected for interference from other 
pigments such as chlorophyll-b and –c or 
degradation products of chlorophyll-a 
(phaeopigments). Assuming that SeaWIFS 
chlorophyll-a algorithms give correct 
chlorophyll-a data, it should in general 
give lower values compared to the routine 
in situ chlorophyll-a data based on e.g. 
spectrophotometric methods.  

To be able to do a proper evaluation all 
available chlorophyll-a data from the area 
had to be used. Using only HPLC 
chlorophyll-a is impossible on historical 
data since this technique is not used in 
routine monitoring. When using in situ 
chlorophyll data one can regard this as a 
‘tuning’ of the satellite chlorophyll data to 
‘normal’ chlorophyll-a values used in 
routine monitoring and in general 
classification systems. The number of in 

 
10

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

situ chlorophyll-a data used together with 
the SeaWIFS images is shown in Table 2.  

The determination of coloured dissolved 
organic material (CDOM) is based on 
standard spectrophotometric 
methodology at 375 or 380 nm and 

expressed as m-1 at 375 nm. Data from 
Danish waters (Stedmond et al., 2000) are 
measured at 375 nm and data from 
Norwegian waters at 380 nm. The CDOM 
data measured at 380 nm is converted to 
375 nm by using a factor of 1.105 
(Højerslev and Aas, 2001)

Table 2 

The number of in situ chlorophyll-a data from the area used in the monthly mean SeaWIFS chlorophyll-a 
images for the three years

Month

January

February

March

April

May

June

July

August

September

October

November

December

1997

1998

1999

49

125

57

155

114

61

65

147

94

80

96

63

61

146

71

159

70

71

75

153

96

95

88

53

101

90

99

65

3.3 Satellite data and image production

The algorithms used to derive 
‘chlorophyll-a-like pigments’ 
concentration, from the radiance data 
collected by the SeaWiFS, are those 
encoded in the REMBRANDT processing 
code, developed by the Marine 
Environment (ME) Unit of the Space 
Application Institute (SAI), Joint Research 
Centre (JRC) EC. The software package 
can be used to process SeaWiFS data from 
Level-1A (top-of-atmosphere radiance, as 
provided by the data distributors - NASA’s 
GSFC DAAC) to Level-2 (derived 
geophysical parameters), and then to 
Level-3 (daily/monthly composite data 
products, remapped over specified 
geographical grids). 

The Level-1A data collected by all 
European satellite data receiving stations, 
during a given satellite pass, are first 
merged into a high-resolution image. This 
step avoids any unnecessary calculations 
and is particularly well suited to 
processing all high-resolution data for a 
given continental zone. The Level-1A data 

are calibrated using the pre-launch 
absolute calibration coefficients provided 
by NASA, corrected by various calibration 
coefficients and a factor representing the 
decay in time of the sensitivity of the 
sensor. The determination of the various 
calibration coefficients takes account of 
the calibration/validation activities 
conducted in the European Seas by the 
ME Unit. 

The Level-1A to Level-2 processing is 
based on a combined land-sea algorithm. 
For marine areas, the REMBRANDT code 
(Mélin et al., 2000) involves an 
atmospheric correction scheme and in-
water algorithms that provide standard 
products such as water-leaving radiance (6 
channels), aerosol radiance and optical 
thickness at 865 nm, chlorophyll-a and 
sediment concentrations, and diffuse 
attenuation coefficient. The atmospheric 
correction scheme (Sturm and Zibordi, 
2001) accounts for Rayleigh multiple 
scattering, aerosol single scattering and 
Rayleigh/aerosol coupling. It calls on 
ancillary data for atmospheric pressure, 

Data and methods   11

wind velocities and ozone values, provided 
by National Centre for Environmental 
Prediction (NCEP) and Total Ozone 
Mapping Spectrometer (TOMS) data on a 
daily basis. The calculation of chlorophyll 
concentration is based on an algorithm, 
combining surface leaving radiances 
(O’Reilly et al., 1998; Maritorena and 
O’Reilly, 2000). The data products for 
terrestrial applications result from a 
vegetation index algorithm developed by 
the Global Vegetation Monitoring Unit of 
the SAI, JRC EC (Gobron et al., 1999, 
2001). The algorithm formulation is 
optimised so that the index value actually 
represents the Fraction of Absorbed 
Photosynthetically Active Radiation 
(FAPAR).  

In the REMBRANDT processing scheme, 
each pixel of a SeaWiFS image first goes 
through the vegetation index procedure, 
where a first classification is made using 
the top-of-atmosphere signal. The pixels 
for which the presence of vegetation is 
detected yield a value of vegetation index 
/ FAPAR. In this way pixels covering land 
are identified and removed. The pixels 
that are considered as marine surface are 
processed through the atmospheric 
correction algorithm, which retrieves the 
part of the total radiance due to the 
surface of the sea. The optical properties 
of the water surface, then, allow estimates 
of its constituents. It must be stressed that 
no a priori assumption is made with regard 
to the nature of the pixel (sea or land). 
Throughout the processing, a flag value 
records the performance of the algorithm. 
Various flags allow for high viewing 
conditions, cloud-ice tests, cloud edges, 
bright surface detection (snow, ice, 
desert), high optical thickness, and so on.

Each individual scene (Level-2) is re-
mapped onto geographical grids (2 km 
resolution), to generate Level-3 data 
products. Daily, ten-day and monthly 
composite products are generated for the 
European region and its marginal Seas 
(North Sea, Baltic Sea, Mediterranean 
Sea, Black Sea, Caspian Sea), as well as for 
zones such as north-eastern Atlantic and 
Northwest Africa, the Middle East, 
including Red Sea and Persian Gulf, and 

the northern Indian Ocean. The first step 
for obtaining a daily product is testing if a 
particular scene has an intersection with 
the set geographical grid, in other words if 
the orbital sensor recorded any 
information for that area. In that case, the 
scene is re-mapped with a nearest-
neighbour technique onto the grid. The 
various scenes that have been re-mapped, 
then, are combined to provide a daily 
product on the map considered. SeaWiFS 
orbits overlap for high latitudes, so it is 
possible for the sensor to 'see' twice the 
same grid point, during the same day, for 
2 consecutive orbits. In such a case, the 
value that is taken for that grid point is 
chosen from the scene for which that grid 
point was observed with the lowest viewing 
angle (satellite zenith angle). 
Furthermore, only the pixels smaller than 
the resolution of the considered map (2 
km) are taken into account. This means 
that the edges of the images are discarded 
(for viewing angles higher than 43º). 
These parts of the images are also those 
for which the accuracy of the atmospheric 
correction is significantly decreasing. 
Once daily products have been obtained 
for a given geographical grid, all daily files 
are combined to provide ten-day and 
monthly composites. The marine variables 
are averaged over the number of days 
where the information was retrieved, with 
simple tests to detect unrealistic outliers. 
The number of samples used for the 
average is stored. As for the terrestrial 
outputs, long-term images are obtained by 
selecting, at each grid point, the 
vegetation index value of the day, which is 
considered to be the most representative 
of a given period. The choice is made so as 
to select the value of the day that provides 
the vegetation index closest to the mean 
index over the period. So each grid point 
bears a value that is an actual 
measurement of a particular day, and the 
conditions of observations for that day are 
stored with the outputs. 

The algorithm for the determination of 
the chlorophyll concentration has been 
developed for global (and mostly open 
ocean) processing and is not specific for 
the Baltic/North Sea or Skagerrak area 
nor for the coastal zones in general.

12

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

After the processing at JRC-SAI the Level-3 
data products were transferred to NIVA to 
be used with the in situ data. The area 
used covers the range latitude Min / Max: 
53.0N / 60.0N and longitude Min / Max: 

4.0E / 13.0E. The resolution is 2-km (at 
centre) and image-size (Pixel / Line): 277 
/ 391. The activities at NIVA were 
performed on an imaging processing 
system (ERDAS IMAGINE). 

Validation of SeaWIFS chlorophyll-a   13

4. Validation of SeaWIFS chlorophyll-a

4.1 Control of SeaWIFS chlorophyll-a 

estimates

A few daily processed (Level-2) images 
from JRC-SAI were compared with in situ 
chlorophyll-a data to control the level of 
chlorophyll-like-pigments concentration 
from SeaWiFS data. Satellite data should 
be chosen as close as possible to the time 
of the water sampling. Exact timing is not 
possible, so in practice data within the 
same day was used. Since not only the time 
but also the sampling depth and the 
patchiness in the water will influence such 
a comparison discrepancy must be 
expected.   

Three daily SeaWIFS images from May 15, 
August 26, 1998 and April 27, 1999 
(Figure 4) were compared with 
’simultaneously’ (same day) in situ 
chlorophyll-a data. The scenes and the 
area around the stations were controlled 
for clouds, and statistics were extracted 
only from stations without clouds. 
Advanced Very High Resolution 
Radiometer (AVHRR) data from the 
weather satellite data was also used as a 
quality control for absence of clouds and 
AVHRR channels 1-5 were investigated. 
No visible clouds were seen around the in 
situ stations chosen for the comparison. In 
Figure 3 the AVHRR channel 4 for the 
three dates are shown. The positions of 
the in situ stations for the three dates are 
shown in the satellite images (Figure 4).

Chlorophyll-a data from the surface water 
(upper 0-1 m) has been used in the 
comparison with the SeaWIFS 
chlorophyll-a data. Chla SeaWiFS data are 
taken from an area around each in situ 
station. The area is 0.04° North/South 
and 0.08° East/West (about 20 km2). This 
gives statistics (min, max, average, sigma) 
from 9 to16 pixels, which was compared 
with the in situ measurements. The 
correlation is shown in Figure 2. The data 
is scattered, but assuming that the 
average value is relatively correct the 
SeaWIFS data are rescaled according to 
the equation: 

Chla modified = 0.6 · ChlaSeaWiFS + 0.1
adjusted           R2 = 0.67       N=23

It is nevertheless recognised that the 
statistical basis (both in space and time) 
for such a correction is weak. 

However, new monthly mean SeaWIFS 
images were processed from the equation 
and presented in maps. The values for 
‘Chlorophyll-a-like pigments’ were divided 
into 8 classes. The classes were divided 
logarithmically to better resolve 
differences in the high and low 
concentrations, which gives a better 
resolution and discrimination in the 
chlorophyll-a values near the coast. 

14

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

. 

Figure 2 

Comparison of SeaWIFS (Level-2) chlorophyll-a data and in situ surface chlorophyll-a data from same locations 
at three different dates 

20

18

16

14

12

10

8

6

4

2

0

New Chl-a = 0,6051x SeaWIFS + 0,1131
R2 = 0,6763

0

2

4

6

8

10

12

14

16

18

20

SeaWIFS Chlorphyll-a (µg/l)

Figure 3

Quick-look images of clouds based on channel 4 of weather satellite NOAA AVHRR data for three different 
dates. The data is used for control of cloud cover at the in situ stations.   

AVHRR 1998-05-15 1223 UTCCCh4                                                         AVHRR 1998-08-26 1329UTC Ch4

AVHRR 1999-04-27 1423 UTC Ch4

SeaWIFS image from three dates used in control of the original SeaWIFS Chl-a data against in situ data of
the same dates.

Figure 4

Validation of SeaWIFS chlorophyll-a   15

 15.05.1998

26.08.1998

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

27.04.1999

  
 
    
 
16

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

4.2 Effects of subsurface chlorophyll-a 

maximum

Optical quantities in the surface water 
(upper 0-5-(10) m) contributes to the 
water-leaving radiance from which the 
SeaWIFS chlorophyll-a data is calculated.  
Since this comparison between SeaWIFS 
and in situ data is based on the surface 

chlorophyll-a data it was relevant to 
investigate if any subsurface chlorophyll-a 
maximum could significantly contribute 
to the signal. In order to get an estimate of 
the magnitude of this effect a comparison 
of chlorophyll-a in the surface and at 5 m 
depth was performed (Figure 5 and 
Figure 6).

Figure 5 

Correlation of in situ chlorophyll-a data from surface (0-1 m) and 5 m depth. Based on all data from the area. 

50

45

40

35

30

25

20

15

10

5

0

Chl-a (5m) = 0,7312 x Chl-a (Surf.) + 0,7089
R2 = 0,70

0

5

10

15

20

25

30

35

40

45

50

Chlorophyll-a in surface water (µg/l)

Figure 6

Difference between surface and 5 m chlorophyll-a concentration. Based on all data from the area. 

)
l
/
g
µ
(
)

m
5
(
a
-
l
l

y
h
p
o
r
o
h
C

l

-
)
e
c
a
f
r
u
S
(

a
-
l
l

y
h
p
o
r
o
h
C

l

30

20

10

0

-10

-20

-30

 Higher Chlorop hyll in surface water

Higher Chlorop hyll-a at 5 m eter

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

% of observations

  
 
 
Validation of SeaWIFS chlorophyll-a   17

In 10-15 % of the observations the values 
are higher at 5 m than in the surface, but 
only approximately 5 % of the 
observations had concentrations that 
could significantly contribute to the water-
leaving radiance. In 10-20 % of the 
observations, the surface chlorophyll-a 
value was highest. It seems that using only 
the surface value should not introduce any 
significant error in the rescaling of the 
SeaWIFS data to the in situ chlorophyll-a 
values at the surface.   

4.3 Effects of coloured dissolved 
organic material, CDOM

The main optical quantities that will 
influence the satellite retrieving 
algorithms are the coloured dissolved 
organic material, CDOM (yellow 
substance). This variable is normally not a 
routine measurement in coastal 
monitoring programmes and is not well 
documented. The variability and 

concentration is, however, measured at 
the Norwegian coast, and this and some 
other data of CDOM is investigated.

The CDOM values measured at 375 nm 
relative to the salinity are shown for the 
eastern North Sea off the Danish west 
coast and in the Kattegat (Figure 7). The 
North Sea data is high in salinity, but has 
relatively high CDOM values with the 
main input from the rivers to the German 
Bight. In the open sea area of Kattegat the 
CDOM show a weak relationship with the 
salinity. At the Norwegian Coast (Figure 8) 
the CDOM is slightly higher and, in the 
area of river influence, (Outer Oslofjord) 
up to 5-8 times higher. In this area one 
could expect strong effect in the SeaWIFS 
algorithm for chlorophyll-a retrieval, 
resulting in a higher overestimation of the 
chlorophyll-a values. The source for the 
high CDOM concentration in the Outer 
Oslofjord is the River Glomma, but the 
concentration is in the same range as the 
Elbe River (Aas and Højerslev, 2001).

Coloured dissolved organic material (CDOM) versus salinity in the Kattegat and the Jutland Coastal Current
1999. (Data from Stedmon et al., 2000)

Figure 7

m
n

5
7
3

t
a
d
e
r
u
s
a
e
m
M
O
D
C

2

1,8

1,6

1,4

1,2

1

0,8

0,6

0,4

0,2

0

5

Kattegat
Danish West Coast

10

15

20

Salinity

25

30

35

 
 
 
 
18

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Figure 8 

Coloured dissolved organic material (CDOM) versus salinity at the Norwegian coast and in the Outer 
Oslofjord (with strong influence from River Water).

Outer Oslofjord

Norwegian Coast

8

7

6

5

4

3

2

1

0

5

10

15

20

25

30

35

40

Salinity

The deviation between SeaWIFS 
chlorophyll-a and  in situ chlorophyll was 
controlled against salinity. CDOM was not 
measured at these samples. In general 
one can assume that lower saline water 
has higher CDOM values. The deviation 
between SeaWIFS and in situ data varies 
with the salinity (Figure 9) giving higher 
difference with lower saline water as 
expected. Since low saline water often is 

associated with higher concentration of 
suspended material the effects could also 
be due to higher scattering from 
particles. One should also be aware that 
low salinity is often found at short 
distance from land, which means that 
errors in the pixel data could be caused 
by land or atmospheric effects on the 
radiance signal. These effects need 
further investigation. 

Figure 9

Deviation between SeaWIFS and in situ chlorophyll-a versus salinity. Trend lines are indicated. 

3,00

2,50

2,00

1,50

1,00

0,50

0,00

-0,50

-1,00

15,000

20,000

25,000

30,000

35,000

40,000

Salinity

 
Monthly mean SeaWIFS chlorophyll images   19

5. Monthly mean SeaWIFS 

chlorophyll images

In this evaluation, monthly mean data was 
processed and recalculated as described in 
chapter 4. However, due to temporal and 
spatial variation of chlorophyll-a 
concentrations evaluation is difficult when 
the satellite data and in situ data are not 
simultaneously collected. Most of the in 
situ data is from situations where cloud 
free satellite data is not available. Also, 
different numbers of daily satellite images 
have been included in the calculated 
monthly mean image both between 
months, but also between different areas 
within the same month due to patchiness 
of cloud cover. This gives different 
numbers of data behind the mean values, 
and some areas have therefore a ‘better 
mean value’ than others. This had to be 
taken into account when using and 
interpreting the data. The comparison 
must be based on all the available in situ 
data whenever this is collected and all the 
recalculated month mean SeaWIFS 
images. Doing such a comparision, the in 
situ and SeaWIFS chlorophyll-a data 
should, on average, give realistically the 
same level of magnitude if the SeaWIFS 
data is correct. In the following figures all 
available in situ data is superimposed on 
the monthly mean SeaWIFS image from 
the same month to make the comparison 
easy. 

The monthly mean chlorophyll-a images 
from September 1997 to December 1999 
are presented in Appendix A. For the 3 
months after the launch of SeaWIFS in 
1997 both September and October are 
partly cloud covered and in November 
only a small area of the North Sea is cloud 

free. No SeaWIFS data are available from 
December 1997. For winter and spring 
1998, the cloud cover varies from about 
100 % in January and is also highly cloud 
covered in February, but both March and 
to some extent April are good. The 
summer months mean images in May to 
August are more or less cloud free giving a 
lot of information. In September and 
October the area west of Denmark is 
mostly cloud covered and only the 
Skagerrak and Kattegat have some cloud 
free areas. The winter months November 
and December are of no use. 

The winter period in 1999 is the same as in 
1998 with no data in January and partly 
clouded in February. However, February 
1999 is better than the 1998 situation, and 
March and April are nearly cloud free. 
The summer months May to September 
1999 are as good as in 1998, but October is 
partly covered and November and 
December are of no use. 

In the following, some of the monthly 
mean SeaWIFS images will be discussed in 
further detail. The extensive Chatonella 
bloom in April and May 1998 is obvious in 
both Skagerrak and the North Sea. In 
April (Figure 10) high chlorophyll-a 
values outside Skagen are well 
documented in the SeaWIFS data with 
concentrations of 5-10 mg/m3 and even > 
10 mg/m3 are also seen in the in situ data. 
Off the west coast of Denmark the 
SeaWIFS values were flagged as erroneous 
due to atmospheric effects (clouds), high 
concentrations of phytoplankton or 
suspended material. 

20

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Figure 10 

Month mean SeaWIFS chlorophyll-a image and in situ data from April 1998.   

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

In May (Figure 11) the Chatonella bloom 
is dominating near the Norwegian Coast 
with chlorophyll-a values >10 mg/m3 
measured by SeaWIFS. The in situ 
measurements in the central Skagerrak 
and at the Norwegian Coast have not 
detected the high concentrations that 
are indicated by the satellite, but high 

concentrations are well documented off 
the west coast of Denmark. The 
explanation of this discrepancy is that 
the in situ data is only based on one 
cruise which is very different in time 
from the daily satellite data that is 
dominating in the monthly mean 
SeaWIFS image. 

  
 
    
 
Monthly mean SeaWIFS chlorophyll images   21

Monthly mean SeaWIFS chlorophyll-a image and in situ data from May 1998.

Figure 11

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

The satellite images give correct 
geographic distribution of chlorophyll 
and therefore, indirectly, also the level of 
eutrophication. But one cannot assess 
whether an area is eutrophicated from 
only the chlorophyll-a concentration 
without knowing the background 
concentrations of chlorophyll-a. 

The Jutland Coastal Current from the 
German Bight and along the west coast of 
Denmark is shown well in many of the 
monthly mean images e.g. Figure 13 This 
current normally has high concentrations 
of particulate material and yellow 
substance, which would tend to 
overestimate the SeaWIFS chlorophyll-a 
values, but the chlorophyll-a level 
retrieved from SeaWIFS is in good 
agreement with the in situ measurements. 

This is true especially for higher 
concentrations when the concentration 
interval is large, but partly also for lower 
concentrations (Figure 11). 

In February 1999 (Figure 12) the 
estimation of chlorophyll-a from SeaWIFS 
is higher than the in situ data and this may 
be caused by the suspended material, 
which at that time is normally high off the 
west coast of Denmark.

In general there are very high 
concentrations of suspended material in 
the Wadden Sea in the German Bight and 
in the Jutland Coastal Current off the west 
coast of Denmark, highest at the coast and 
decreasing toward west. In winter the 
surface water in the North Sea can also be 
very turbid due to re-suspended fine sand.

  
 
    
 
22

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Figure 12 

Monthly mean chlorophyll-a image and in situ data from February 1999. 

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

In the German Bight and along the west 
coast of Denmark, in the image of August 
1999 (Figure 13), the concentration of 
chlorophyll corresponding to values 
> 3 mg/m3 is generally in areas of high 
concentrations of suspended material 
during summer. But, as in May 1998, there 
is a reasonable agreement between 

SeaWIFS and in situ chlorophyll-a data. No 
other Danish areas in Kattegat or Belt Sea 
have input of high levels of suspended 
material. There is no big river outlet 
except the Göta River in Sweden, which 
affects the coast and the archipelago areas 
north of Gothenburg and the eastern 
Skagerrak. 

 
  
 
    
 
Monthly mean SeaWIFS chlorophyll images   23

Monthly mean chlorophyll-a image and in situ data from August 1999.

Figure 13

In the north-east Skagerrak the Glomma 
River gives inputs of suspended material, 
which normally is highest in April to June. 
The concentrations could rise to 5 mg/l in 

the open area outside the archipelago. 
The SeaWIFS chlorophyll values are too 
high close to the coast in this area 
(Figure 13).

   
 
24

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

6. Summer mean chlorophyll images

The summer mean values based on 
monthly mean values from May to 
September 1998 and 1999 are shown in 
Figure 14. The in situ data used and 
presented in the summer mean images is 
based on mean values of 5 or more dates 
of observations in the period. In the 
Kattegat region the satellite data is in good 
agreement with the field data. In the small 
part of the western Baltic Sea the 
concentrations are higher but also in 
agreement with the in situ data. Off the 
west coast of Denmark, the satellite 
derived chlorophyll-a tend to be generally 
higher than the in situ data. This might be 
because the in situ data are few and the 
satellite data represents a mean value of 
several data sets. But it could also be due 
to the generally higher concentrations of 
CDOM and suspended matter along the 
Danish west coast. Anyhow, the in situ and 
satellite data supplement each other and 
are in relatively good agreement even in 
some of the near-coast areas. 

The Chatonella bloom (May 1998) affects 
the summer mean image from 1998, while 
the summer mean of 1999 indicates a 
more ‘normal’ distribution with highest 
concentration along the coasts and in the 
shallow areas. At Horns Rev off the Danish 
west coast this is very evident, as well as the 
shallow areas in the western Kattegat and 
in the areas around the Kattegat islands 
Læsø and Anholt. That the chlorophyll-a 
concentrations are lower in the open areas 
of eastern Kattegat is in agreement with 
results from the Danish monitoring 
programme (Hansen et al. 2000).

The low level of chlorophyll-a in the 
central North Sea, which also is seen as a 
‘tongue’ into central Skagerrak, is well 
documented in the images. This tongue is 
in good agreement with the main currents 
in the area (Figure 15). The in situ data 
from the Arendal—Hirtshals transect 
between Norway and Denmark is very well 
reflected in the satellite image and the 
station in the ‘tongue’ of Atlantic water 
that comes into the central Skagerrak. But 
the Skagerrak front that during summer 
extends from Skagen towards the 
northeast is not seen in the summer mean 
images, only in some of the monthly mean 
images e.g. in the September images. This 
is probably due to the fact that the 
chlorophyll during summer is mainly 
concentrated in the subsurface halocline 
at the stratified side of the front 
(Heilmann et al., 1994)

Six stations, four close to the coast and two 
offshore, (Figure 16) in the area were 
selected for a closer evaluation of the 
SeaWIFS derived chlorophyll-a 
concentrations compared to in situ 
chlorophyll measurements. Due to the 
high level of coloured dissolved organic 
material at the Norwegian coast, three 
stations in a gradient from the Glomma 
River towards west were chosen. At these 
stations all the SeaWIFS chlorophyll-a 
statistics were picked out from the 
monthly mean images during the summer 
period May-September and presented 
cumulatively with the in situ data from the 
same period. The data are presented in 
Figure 17 and Figure 18.

Maps of summer mean values of SeaWIFS chlorophyll-a from 1998 and 1999. Mean value of all SeaWIFS
data from May to September. The in situ data presented represents mean value of more than 5
observations in the same period.

Figure 14

Summer mean chlorophyll images   25

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

Summer 1998

Summer 1999

Map showing the main currents in the area. From OSPAR COMMISSION (2000).

Figure 15

  
 
    
 
26

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Figure 16

Map of stations where all summer (May-September) in situ chlorophyll-a data and SeaWIFS chlorophyll-a data 
have been compared. 

Norway

Oslo

St.2

St. 1112

Arendal

Glomma 
River

St.B

St.1105

Skage
n
Hirtshals

Læsø

Sweden

Gotha 
River

St.905

Anholt

Denmark

Horns Rev

St.1500099
510009

Elbe 
River

Looking at the near coastal stations off the 
Norwegian coast (Figure 18) the situation 
is opposite compared to the Kattegat 
station with SeaWIFS giving higher 
chlorophyll-a concentrations than the in 
situ measurements. The station off 
Arendal (Station 1112) shows slightly 
higher SeaWIFS chlorophyll values. The 
deviation between in situ and satellite data 
is increasing when moving eastwards, and 
at the river influenced station B in the 
outer Oslofjord the SeaWIFS gives higher 
values by approximately a factor of 2. In 
general this area has higher coloured 
dissolved organic material (CDOM) than 
the coastal areas of Denmark and Sweden 
indicating that CDOM seems to have 
larger effects than particles in the 
chlorophyll-a algorithms. 

Off the west coast of Denmark (Station 
1510009) the SeaWIFS data is in good 
agreement with the in situ data (Figure 
17), even though this area normally has 
high concentrations of particles. This 
could be explained by the fact that the 
CDOM have the most severe influence on 
the chlorophyll-a retrieval from the 
algorithms. Also in the central Skagerrak 
(Station 1105) where Atlantic water is 
dominating, there is good agreement 
between the two data sets, which is to be 
expected since this area, on average, has 
the lowest values of CDOM and particles. 
The Kattegat station (905) is the only area 
in this comparison where the SeaWIFS 
chlorophyll-a are lower than the in situ 
chlorophyll-a data, which means that in 
this area the satellite data would 
underestimate the eutrophic situation. 
The present data cannot explain this 
difference.

Comparison of cumulative in situ values of chlorophyll-a and the satellite chlorophyll-a off the west coast of
Denmark (Station 1510009), in central Skagerrak (Station 1105) and in the Kattegat (Station 905).

Figure 17

Summer mean chlorophyll images   27

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

In Situ

SeaWiFS

Station 1510009

0

2

4

6

8

10

12

14

16

18

20

Chlorophyll a, µg/l

In Situ

SeaWiFS

Station 1105

0

2

4

6

8

10

12

14

16

18

20

Chlorophyll, a µg/l

In Situ

SeaWiFS

Station 905

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

0

2

4

6

8

10

12

14

16

18

20

Chlorophyll-a, µg/l

28

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Figure 18

Comparison of cumulative in situ values of Chlorophyll-a and the satellite Chlorophyll-a at the Norwegian 
Coast (Station 1112, Station 2) and in the outer Oslofjord (Station B) close to the river outlet.

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

0

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

In Situ

SeaWiFS

Stasjon 1112

2

4

6

8

10

12

14

16

18

20

Chlorophyll-a, µg/l

In Situ

SeaWiFS

Station 2

0

2

4

6

8

10

12

14

16

18

20

Chlorophyll a, µg/l

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

In Situ

SeaWiFS

Station B 

0

2

4

6

8

10

12

14

16

18

20

Chlorophyll-a, µg/l

Conclusions   29

7. Conclusions

The analysis of chlorophyll-a maps, 
obtained from SeaWiFS satellite images 
processed with the REMBRANDT (version 
01) code, has shown significant 
overestimate with respect to in situ 
observations of chlorophyll-a.  The 
overestimate is mostly attributed to the use 
of a ’global’ bio-optical algorithm for 
chlorophyll-a computation not specific for 
the Skagerrak, Baltic or North Sea waters 
characterised by high absorption values of 
coloured dissolved organic material. Even 
in the open areas of Skagerrak the present 
algorithms overestimate the chlorophyll-a.  
The use of a ’rescaling’ function for 
chlorophyll-a values, defined with in situ 
data taken at the same time as the satellite 
images, has significantly decreased the 
uncertainties in the chlorophyll-a maps 
even though some coastal areas still 
highlight chlorophyll-a overestimates. 

Clearly algal bloom monitoring can 
significantly benefit from satellite data and 
cannot be simply monitored by a fixed net 
of stations, dedicated cruises or ships-of-
opportunity.  Eutrophication mapping 
through satellite colour data (i.e. SeaWiFS 
or MERIS) should mostly address the 
creation of  ’time composite’ products for 
those periods of the year when blooms 
may occur.  In the months of November 
through February, phytoplankton 
production is mostly light limited and is 
not related to the input of nutrients.  
Because of this the satellite data, which at 
that period of the year are highly affected 
by cloud cover, are not considered 
essential. 

In the months of March through April 
(and in some cases in February too), 
chlorophyll-a maps could be of some 
interest to identify and characterise spring 
blooms, which may reflect the magnitude 
of the winter nutrient pool even though 
frequent and strong winds may limit their 
development.  

In the months of May through September, 
the chlorophyll-a maps could ensure the 
definition of the level of eutrophication, 

even though frequent and strong winds 
could again mix the deep water nutrients 
into the euphotic zone increasing biomass 
in the stratified areas.  In contrast, under 
calm conditions, the phytoplankton 
stratifies in subsurface maxima at depths 
making their detection impossible 
through space sensors. On the other 
hand, Cyanobacteria, which dominate in 
the Baltic Sea during summer, accumulate 
at the surface during calm periods. These 
conditions make questionable the 
comparability of satellite data collected 
over different years. 

Thus, in the operational use of satellite 
colour data for the detection of 
eutrophication levels in the Nordic Sea 
areas, the most relevant information 
seems to be the capability of detecting and 
mapping the maximum spring 
concentrations and the mean summer 
concentrations of chlorophyll-a. 

Whether satellite data makes it possible to 
exclude any routine monitoring station is 
not easy to elaborate from the present 
investigation and the images so far. 
Further investigation is needed where the 
variability at the station must be compared 
with the monitoring frequency and also 
other objectives of the station (e.g. 
investigation of deep water oxygen 
deficiency). In the North Sea it is maybe 
from a eutrophication point of view 
possible to exclude stations west of the 
34.5 isohaline where winter nutrient 
concentrations are close to background 
values. In this central area of the North 
Sea the surface water is normally not 
much affected by land-based nutrient 
loads, but this is also the area where 
oxygen deficiency problems sometimes 
arise in the bottom water. 

Information from the central North Sea 
can also be used to improve the present 
algorithms or be used in later work with 
new sensors such as MERIS on ENVISAT. 
The MERIS-sensor will have improved 
spectral capability to handle difficulties in 
the coastal areas (Doerffer et.al., 1999). 

30

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Ongoing research on algorithm 
development and improvements in this 
area will be performed within the 
framework of validation activities on 
MERIS where projects such as e.g. VAMP 

— Validation of MERIS data products 
(NIVA, UIO, IMR and GKSS) and the EU-
project REVAMP — Regional Validation of 
MERIS Chlorophyll-a Products in North 
Sea Coastal Waters.    

Bibliography   31

8. Bibliography

Aas, E. and N.K. Højerslev, 2001. 
Attenuation of ultraviolet 

irradiance in North European coastal 
waters. Oceanologia 43: 139-168.

Berthon, J.F. et al.,  2001. Coastal 
Atmosphere and Sea Time Series 
(CoASTS): Data set analysis for ocean 
color modeling in the North Adriatic Sea 
coastal water.  Journal of Atmospheric and 
Oceanic Technologies (submitted). 

Doerffer, R., K. Sørensen, and J. Aiken, 
1999. MERIS: Potential for coastal zone 
application.  International Journal for Remote 
Sensing Vol. 20, no 9: 1809-1818.

Hansen, J., Hansen, J.L.S., Pedersen, B., 
Carstensen, J., Conley, D., Christiansen, T., 
Dahl, K., Henriksen, P., Josefson, A., 
Larsen, M.M., Lisbjerg, D., Lundsgaard, 
C., Markager, S., Rasmussen, B., Strand, J., 
Ærtebjerg, G., Krause-Jensen, D., Laursen, 
J.S., Ellermann, T., Hertel, O., Skjøth, 
C.A., Ovesen, N.B., Svendsen, L.M. and 
Pritzl, G., 2000. Marine områder - Status 
over miljøtilstanden i 1999. NOVA 2003. 
Danmarks Miljøundersøgelser. 230 pp. - 
Faglig rapport fra DMU nr. 333. (In Danish 
with an English summary).

Heilmann, J.P., K. Richardson and G. 
Ærtebjerg, 1994. Annual distribution and 
activity of phytoplankton in the 
Skagerrak/Kattegat frontal region. Mar. 
Ecol. Prog. Ser.112: 213-223.

Højerslev, N. K. and E. Aas, 2001. Spectral 
light absorption by yellow substance in the 
Kattegat-Skagerrak area. Oceanologia 43: 
39-60.

Melin, F., B. Bulgarelli, N. Gobron, B. 
Pinty and R. Tacchi, 2000. An integrated 
tool for SeaWiFS operational processing. 
JRC Publication, No. EUR 19576 EN.

Gobron N., B. Pinty, M. Verstraete and F. 
Melin, 1999. Development of a vegetation 
index optimized for the SeaWiFS 
instrument. Algorithm Theoretical Basis 
Document. JRC Publication, No. EUR 
18976 IN.

Stedmon C.A., S. Markager and H.Kaas, 
2000. Optical properties and signatures of 
Chromophoric Dissolved Organic Matter 
(CDOM) in Danish Coastal Waters. 
Estuarine Coastal and Shelf Science 51: 267-
278.  

Gobron, N., F. Mélin, B. Pinty, M.M. 
Verstraete, J.-L. Widlowski and G. Bucini, 
2001. A global vegetation index for 
SeaWiFS: design and applications. In: 
Satellite Remote Sensing Data and Climate 
Model Simulations: Synergies and Limitations, 
edited by M. Beniston and M. M. 
Verstraete (Dordrecht: Kluwer Academic 
Publishers), in press.

Maritorena, S. and J.E. O'Reilly, 2000. 
OC2v2: update of the initial operational 
SeaWiFS chlorophyll a algorithm. NASA 
Tech. Mem. 2000-206892, 11, Ed. S.B. 
Hooker and E.R. Firestone.

O'Reilly J.E., S. Maritorena, B. G. Mitchel, 
D.A. Siegel, K.L. Carder, S.A. Garver,  M. 
Kahru and C. McClain, 1998. Ocean color 
chlorophyll algorithms for SeaWiFS.  J. 
Geophys. Res. 103, C11: 24937-24953.

Sturm, B. and G. Zibordi, 2001. SeaWiFS 
atmospheric correction by an 
approximate model and vicarious 
calibration, Int. J. Remote Sensing, in press.

http://www.me.sai.jrc.it/me-website/
contents/shared_utilities/frames/
index_windows.htm

32

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Acknowledgements

We would like to thank Mr. Didrik 
Danielsen at the Institute of Marine 
Research, Norway for supporting this 
project with important data. We are also 
grateful to the other contributors from the 
Swedish Meteorological and Hydrological 
Institute and persons involved in sampling 
and analysis in the different countries and 
institutions. 

The authors would also like to thank the 
SeaWiFS Project (Code 970.2) and the 
Distributed Active Archive Centre (Code 
902) at the Goddard Space Flight Centre, 
Greenbelt, MD 20771, for the production 
and distribution of the SeaWiFS data, 
respectively. NASA’s Mission to Planet 
Earth Program sponsors these activities.

List of acronyms   33

List of acronyms

EC:European Commission

HRPT:High Rate Picture Transmission

FAPAR:Fraction of Absorbed 
Photosynthetically Active Radiation

GVM:Global Vegetation Monitoring

IMR:Institute of Marine Research, Norway

JRC:Joint Research Centre

LAC:Local Area Coverage

ME:Marine Environment

NASA:National Aeronautics and Space 
Administration

NERI:National Environmental Research 
Institute, Denmark

NIVA:Norwegian Institute for Water 
Research, Norway

NCEP:National Centre for Environmental 
Prediction

REMBRANDT: REtrieval of Marine 
Biological Resources through ANalysis of 
ocean colour DaTa

SAI:Space Application Institute.

SeaWiFS:Sea-viewing Wide-Field-of-View 
Sensor

SMHI:Swedish Meteorological and 
Hydrological Institute, Sweden

TOMS:Total Ozone Mapping 
Spectrometer.

GSFC:Goddard Space Flight Centre.

DAAC:Data Active Archive Centre.

34

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Appendix A
Monthly mean SeaWIFS chlorophyll-a 
images from September 1997 to 
December 1999, rescaled to in situ 
chlorophyll-a measurements, and 
compared to monthly mean in situ 
chlorophyll-a measurements at 
monitoring stations.

Monthly mean SeaWIFS Chlorophyll-a Images from September to November 1997.

Appendix A   35

September 1997

October 1997

No

SeaWiFS

Data

November 1997

December 1997

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

  
 
    
 
36

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Monthly mean SeaWIFS Chlorophyll-a images from January to March 1998.

January 1998

February 1998

March 1998

April 1998

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

  
 
    
 
Monthly mean SeaWIFS chlorophyll-a images from April to August 1998.  

Appendix A   37

May 1998

June 1998

July 1998

August 1998

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

  
 
    
 
38

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Monthly mean SeaWIFS chlorophyll-a images from September to December 1998

September 1998

October 1998

No

SeaWiFS

Data

November 1998

December 1998

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

  
 
    
 
Monthly mean SeaWIFS chlorophyll-a images from January to April 1999.

Appendix A   39

January 1999

February 1999

March 1999

April 1999

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

  
 
    
 
40

Remote sensing’s contribution to evaluating eutrophication in marine and coastal waters

Monthly mean SeaWIFS chlorophyll-a images from May to August 1999.

May 1999

June 1999

July 1999

August 1999

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value 

  
 
    
 
Monthly mean SeaWIFS Chlorophyll-a images, September to December 1999.

Appendix A   41

September 1999

October 1999

No

SeaWiFS

Data

November 1999

December 1999

    Chlorophyll-a-like pigments 

<0.5  0.5-1  1-2  2-3  3-5  5-10  >10  mg/m³       cloud or flagged value