Mapping of the North Sea turbid coastal watersusing SeaWiFS data

Hans van der Woerd and Reinold Pasterkamp

Abstract. The spatial–temporal coverage provided by optical remote sensing can be effectively used to overcome some ofthe severe deficiencies in the current in situ monitoring programs for water-quality parameters in the coastal zone. Wepresent the outcome of a project to map the concentrations fields of total suspended matter in the North Sea, based on datafrom the sea-viewing wide field-of-view sensor (SeaWiFS) instrument. Next to a good infrastructure and standard automaticprocessing, a reliable atmospheric correction proved to be essential. A single-band algorithm, based on a representative setof inherent optical properties, is presented. The satellite data and data from a standard in situ monitoring program near theDutch coast are compared. For the year 2000, a total of 129 images could be used to present actual information and toderive monthly mean patterns and trends in the dynamic North Sea system. The results show the capacity of satellite data toprovide excellent temporal coverage. Spatial variations of suspended sediment, often caused by input from rivers plus wind-and wave-induced resuspension over shallow areas, are covered.

Résumé. La couverture spatio-temporelle proposée par la télédétection optique peut être utilisée efficacement poursurmonter certaines des principales lacunes associées aux programmes actuels de suivi in situ pour les paramètres de laqualité de l’eau dans les zones côtières. Nous présentons le résultat d’un projet pour cartographier les champs deconcentration de matière totale en suspension dans la Mer du Nord, basé sur des données de l’instrument SeaWiFS. En plusd’une bonne infrastructure et d’une procédure automatique standard de traitement, une correction atmosphérique fiable s’estavérée tout aussi essentielle. Un algorithme à une bande basé sur un ensemble représentatif de propriétés optiquesinhérentes est présenté. Les données satellitaires et les données d’un programme in situ standard de suivi réalisé près descôtes hollandaises sont comparées. Pour l’année 2000, un total de 129 images ont pu être utilisées pour présenterl’information existante actuelle et pour dériver les patrons mensuels moyens et les tendances moyennes dans le systèmedynamique de la Mer du Nord. Les résultats montrent le potentiel des données satellitaires à fournir une excellentecouverture temporelle. La question des variations spatiales des sédiments en suspension souvent causées par des apports derivières et par le phénomène de remise en suspension induit par le vent et les vagues au-dessus des zones peu profondes estaussi abordée.[Traduit par la Rédaction]

53Introduction

The North Sea is one of the nine sea basins that surround theEuropean continent. The quality of water in the North Sea isunder substantial environmental pressure because of organicand inorganic inputs from fluvial inflow from the sixsurrounding countries. It is well known that these waters areaffected by eutrophication, leading to increased occurrence ofalgae blooms in spring and autumn. To assess the extent ofpollution and (or) eutrophication and the effect of abatementmeasures, considerable efforts by European and nationalauthorities are put into the monitoring of relevant water-qualityparameters. The standard monitoring program is usually basedon measurement of a large number of biological and chemicalwater-quality parameters at a limited number of samplingpoints visited by a research vessel on a regular basis.

Downstream estuarine and coastal impacts are often poorlyunderstood and monitored, however, especially in large orremote ecosystems like the Friesian Front. This is because ofstrong inherent variability of the North Sea system and becauseof the difficulty and expense of monitoring marine indicators atappropriate time and space scales. The strong variability in thecomplex heterogeneous and dynamic coastal waters requires

improved monitoring coverage in space and time of relevantparameters.

The new generation ocean color sensors (sea-viewing widefield-of-view sensor (SeaWiFS), moderate resolution imagingspectroradiometer (MODIS), and medium resolution imagingspectrometer (MERIS)) provide daily global monitoring ofwater quality. Although originally designed to monitor theoceans at moderate spatial resolution (in the order of 1 km),these instruments have the capability (band settings andsensitivity) to deliver surface concentrations of chlorophyll andsuspended sediments in coastal waters (Sathyendranath, 2000).

Nevertheless, the production of reliable and accurate genericwater-quality maps for all coastal zones has not been feasible todate, the main reason being that turbid (case 2) waters can varyover a large range in composition and in the related (specific)inherent optical properties of the constituents. In addition, thelarge range of aerosol types and optical characteristics, ranging

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Can. J. Remote Sensing, Vol. 30, No. 1, pp. 44–53, 2004

Received 12 September 2002. Accepted 24 July 2003.

H. van der Woerd1 and R. Pasterkamp. Institute forEnvironmental Studies, De Boelelaan 1087, 1081 HV Amsterdam,The Netherlands.

1Corresponding author (e-mail: hans.van.der.woerd@ivm.falw.vu.nl).

from maritime to rural and urban origin, poses difficulties forthe atmospheric correction.

In this paper we present the key results in the development ofa monitoring service in the Dutch part of the North Sea, basedon SeaWiFS observations. We describe the main issues in thedevelopment of maps and an atlas of the distribution of totalsuspended matter (TSM) in the North Sea.

TSM has an impact on light climate and primary productionand adheres toxic substances, like organic micropollutants andheavy metals. The concentrations and budgets are stronglyinfluenced by sedimentation and local sources (rivers,dredging, dumping). The inflow and outflow of TSM areimportant for the ecological balance in the Waddensea tidalarea. The transport is influenced by the inflow from EnglishChannel waters and the interaction with the Atlantic waters inthe north. As such, TSM relates to the ecology, fishery, andeconomy of the North Sea. Mean TSM concentration in theDutch part of the North Sea ranges from 2 to 5 g·m–3,depending on the distance from the coast, but the distribution istailed towards high values of over 100 g·m–3.

The outline of this paper is as follows. First, a description isgiven of the data handling and processing and the generation ofa standardized end product. Subsequently, the two essentialprocessing steps are treated in more detail: the atmosphericcorrection, and the algorithm that relates the subsurfacereflectance spectrum to TSM concentration. Monitoringrequires that information can be repeated and integrated toproduce time series and to be combined with other information.All these aspects are reflected in the presentation of the basicproduct and the analysis of the bimonthly maps. It is shown thatcloudiness does not obstruct the imaging of the full North Sea,even in winter. The satellite-derived TSM concentrations arecompared with the in situ measurements near the Dutch coast.In the discussion the strong and weak points of this monitoringservice are addressed.

Data handling and processingSeaWiFS was launched on 1 August 1997 on board the

SeaStar spacecraft. It is in a sun-synchronous orbit with anorbital time of approximately 100 min. In combination with thewide (2800 km) field of view of the instrument, this results inan overpass frequency of one or two times a day over the NorthSea. The overpass time lies between 1100 and 1400 UTC. Thefirst selection of SeaWiFS images is performed automaticallyby querying the Dundee database for inclusion of the North Sea(52°N, 3°E) in the images, resulting in an initial number ofmore or less than 50 images a month.

If the region of interest is in the extreme sides of the swath,the image is rejected because the geographic representation ofthe image is to a large extent deformed and the surface area ofthe pixels increases, reducing the spatial information in theimage considerably. Moreover, the large viewing anglecomplicates the atmospheric correction and TSM retrieval. Theselection of images with an acceptable viewing zenith anglewas based on the SeaWiFS overpass time. In the year 2000, it

appeared that acceptable images had overpass times between1120 and 1340 UTC. All images that were captured outside thiswindow were rejected, resulting in approximately 30 images amonth. Furthermore, the number of images selected per monthis determined by the meteorological conditions, i.e., cloudinessand haziness. Generally, more images are available in summerthan in winter. As an indication, in the year 2000 up to 20images per month were selected, with a minimum number ofsix images per month.

The selected images are downloaded and preprocessed withthe SeaDAS software to ensure reduction in processing time(Fu et al., 1998). Preprocessing of the images involves thecropping of the image to the region of interest and preparing theimages for processing. The initial cropping was defined toinclude the region bounded by latitudes 51°N and 55°N andlongitudes 0°E and 9°E.

The processing consists of the correction for atmosphericeffects and translates the radiance at sensor to surface albedoand subsurface reflectance. Subsequently, the water-qualityparameter TSM is calculated from the reflectance spectrum.The image is masked with the flags for land (using the SeaDASland-cover database) and for cloud and ice (McClain, 2000). Ina number of cases the image had to be shifted one or morepixels to achieve georeferencing within one pixel accuracy. Theresulting TSM maps are incorporated in a geographicinformation system (GIS) environment (ArcView) for ease ofuse for the end-users.

This GIS environment is used for data transformation, datamanagement, and map production. Data transformation isneeded to obtain the desired geographic projection. For thisdata transformation, the original TSM images are imported intoArcView, rectified to a grid with a step size of 0.015° withnearest neighbor interpolation, and added to a projected view asa point-event theme. The use of a fixed grid is necessary forsubsequent statistical analysis and image-by-image comparison,although it will introduce a small loss of information for imageswith high spatial resolution (centre of swath) or doubling ofpixels in images with low spatial resolution (side of swath). Theresult of interpolation of each of these point datasets is anArc/Info grid that covers the original remote sensing image.The GIS environment also enables the consistent addition ofimportant meta-data such as wind direction and speed, aerosoltype, and tidal information. The ArcView project file that isbuild for the purpose of data management contains a table withmeta information for each individual map. Items in this tableinclude map name, name of the original remote sensing image,name and path name of the grid used to create the map, nameand path name of the tide chart, and information about theweather conditions.

The third function of the GIS environment is the use for mapproduction. With an Avenue script that reads the meta-information from the meta-information table to compose themap, the map production is faster and more accurate. Thismeans that with the GIS environment developed for theproduction of the TSM maps, higher level products such asmonthly or seasonal mean and standard deviation maps can be

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generated on an operational basis. Bundled TSM maps orhigher level products can be delivered on a regular monthly–seasonal or maybe yearly basis, or a query for a specifictemporal or spatial window can be made.

Processing observations of turbid coastalwaters

The locations of the eight SeaWiFS wavebands in relation tothe absorption of the main water constituents are plotted inFigure 1. The blue and green bands (bands 2 and 5,respectively) are specially designated for the retrieval of thechlorophyll (CHL) concentration in ocean waters (O’Reilly etal., 2000). Above turbid coastal waters, however, the blue–green ratio is not suitable because of high concentrations ofcolored dissolved organic matter (which absorbs strongly in theblue) and highly varying concentrations of atmosphericaerosols.

The bands in the near infrared (NIR) (bands 7 and 8) arenecessary for the retrieval of the aerosol type and aerosoloptical depth (AOD) at the visible wavelengths (Gordon, 1997).The standard SeaWiFS atmospheric correction algorithm(Gordon and Wang, 1994), designed for open-ocean water,tends to fail over turbid (coastal) water. This failure can beattributed to the assumption of zero water-leaving radiance inSeaWiFS bands 7 and 8 (centre wavelengths 765 and 865 nm,respectively). Ruddick et al. (2000) extended the standardSeaWiFS algorithm by fixing the ratio of water-leavingreflectance for bands 7 and 8 to a theoretically derived value of1.72 and by choosing a constant ratio of aerosol reflectance inbands 7 and 8, based on a scatterplot of Rayleigh-correctedreflectances throughout the image. The resulting two equations

with two unknowns can be solved to yield the aerosolreflectance and thus radiance in the NIR.

Given the (multiple scattering) aerosol radiance in the NIR,two aerosol models are selected from a set of 12. This selectionis based on the single scattering aerosol reflectance ratio. Foreach candidate model, the single scattering aerosol reflectancein bands 7 and 8 is derived from a look-up table with tabulatedvalues of the inverse relationship ρas � ρam, where ρas is thesingle scattering aerosol reflectance, and ρam is the multiplescattering aerosol reflectance. The resulting aerosol singlescattering ratio of bands 7 and 8 is then compared with theobserved single scattering ratio of this particular aerosol model.The optimum solution is given by a weighted linearinterpolation between the best-fitting aerosol models. Thesetwo aerosol models are then used to calculate the singlescattering aerosol reflectance at shorter wavelengths; themultiple scattering aerosol reflectances at shorter wavelengthsare derived with the aid of look-up tables.

There are 12 aerosol models, but during different processingupdates some additional models were added in favor of othermodels (Wang, 2000). An example of the Rayleigh-correctedreflectance in bands 7 and 8 from a large subset of the NorthSea region for 25 February 2000 is shown in Figure 2. In thisfigure the lines labelled 1.72 and 1.05 are drawn that delineatethe cluster of points.

The assumption of spatial homogeneity of the ratio of765 nm to 865 nm of the aerosol reflectance and of the water-leaving reflectance restricts the operational use to limited areasof satellite data because this ratio has to be assessed manuallyfor each region by inspection of a scatterplot. It isacknowledged that variations of this parameter over theprocessed satellite image may introduce errors in specific partsof the image. Currently, this parameter was evaluated over thewhole North Sea area presented in the atlas referred to earlier inthe paper. Large inland lakes in the Netherlands and theWaddensea were masked out in the scatterplots and were notpresented in the final product. In the present SeaDAS version(4.4 at the time of writing), additional options for atmosphericcorrection are available that do not assume zero water-leavingreflectance in the NIR and are appropriate for atmosphericcorrection over coastal turbid waters (Siegel et al., 2000). Thefirst TSM maps for the Dutch coastal zone were processed inSeaDAS version 3.3, however, and these options were notavailable at that time. Continuous processing over the years hasnot indicated substantially reduced accuracy of the Ruddickatmospheric correction algorithm, and the use of anotheralgorithm has not been considered.

The output of the turbid water atmospheric correctionalgorithm is subsurface irradiance reflectance (R(0–, λ )) ofSeaWiFS bands 1–8 (i.e., corrected for the air–water interface).The parameters used in the relation between water-leavingreflectance and subsurface irradiance reflectance were set tothe default values in the Ruddick SeaDAS extension. Theconstant accounting for the air–water interface was set to 0.523,and the Q factor accounting for the ratio of upwelling radianceto upwelling irradiance just below the water surface was set to π.

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Figure 1. Graph presenting the location of the eight SeaWiFSspectral bands in relation to the spectral absorption of the mainwater constituents (note arbitrary vertical scale): pure water,chlorophyll, total suspended matter (TSM, tripton), and coloreddissolved organic matter (CDOM).

Retrieval of total suspended matter(TSM)

For the retrieval of the TSM concentration from thesubsurface irradiance reflectance, a single-band algorithm wasdeveloped, since the SeaWiFS standard products do not includea TSM-concentration product. The routine processing requiressome additional characteristics of such an algorithm: (i) thealgorithm should be relatively simple to reduce computationaltime; and (ii) the algorithm must be robust, i.e., it should workin the expected concentration range and for the expected solarzenith angles, seasons, etc.

It was acknowledged that it is favorable to use a simplealgorithm and recognize the limitations and possibleinaccuracies instead of a state-of-the-art (multispectral)algorithm with badly described behavior and error sources. Thealgorithm is based on the inversion of a forward modelproposed by Gordon and Brown (Gordon et al., 1975):

R fb

a b( , )

( )( ) ( )

0− =+

λ λλ λ

b

b

(1)

where the inherent optical properties absorption (a) andbackscattering (bb) are a linear function of the definedconstituents, namely pure water, phytoplankton pigment(CHL), total suspended matter (TSM), and colored dissolvedorganic matter (CDOM). The pre-factor f depends mainly onsun zenith angle and other factors such as volume scatteringfunction. The TSM concentration is defined here as the dryweight of all particles retained on a Whatman GF/F filter.

The main influence of the TSM concentration on thereflectance spectrum lies in the backscatter coefficient bb. Anincrease of suspended sediments will augment bb and thusincrease the reflectance R. Because the spectral shape of thebackscattering coefficient is nearly flat, the effect on thespectral shape of the reflectance spectrum is relatively smallcompared with that of constituents with a more pronouncedspectral fingerprint, such as chlorophyll. It is for this reasonthat the use of ratios is less suitable for TSM retrievalalgorithms. However, it must be noted that the use of ratiosusing bands that are spectrally close together might reduceinaccuracies because of atmospheric correction errors andvariations in the Q factor and (or) f factor.

The choice of the band for the TSM retrieval was based onthe following considerations: there should be maximalsensitivity for TSM, minimal sensitivity to other constituentslike CHL and CDOM, and minimal sensitivity to inaccuraciesin atmospheric correction. The location of the eight SeaWiFSspectral bands in relation to the spectral absorption of the mainwater constituents is presented in Figure 1. Bands 1–4 arestrongly influenced by the absorption of CDOM andchlorophyll pigments, and band 6 coincides with the redabsorption peak of chlorophyll a. Because of the noncorrelativevariation of chlorophyll and CDOM in the North Sea, thesebands are less suited to use in a single-band TSM algorithm.For average chlorophyll concentrations and increased TSMconcentrations (approximately larger than 15 g·m–3), band 6performs considerably better, but band 5 is preferred foraverage TSM concentrations. The choice between band 5 andthe NIR bands 7 and 8 is more difficult. The main differencelies in the sensitivity of the reflectance for changes in TSMconcentration in turbid and clear areas. Band 5 is very sensitive

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Figure 2. Scatterplot of Rayleigh reflected radiances in SeaWiFS bands 7 and 8, extracted fromthe image of 25 February 2002 above the North Sea. Note the clusters with high TSMconcentrations (ratio 1.72) and high aerosol loadings (ratio approximately 1.05). Extractingpixels within the box bounded by latitudes 50°N and 61°N and longitudes 10°E and 4°W.

at small concentrations but saturates at high concentrations ofTSM (above 30 g·m–3). Band 7 (and to a larger extend band 8)is relatively insensitive to changes in TSM concentrationsbecause the light at that wavelength is subject to strong waterabsorption but lacks the problem of saturation. Because themedian of TSM concentrations in the North Sea isapproximately 5 g·m–3, high sensitivity at low concentrations isconsidered to be of more importance than the reflectancesaturation at high concentrations, and band 5 was selected asthe most appropriate band for TSM retrieval in the turbid NorthSea.

The use of a representative optical model is a key issue forthe use of analytical inversion algorithms because it willdirectly influence the values and thus the accuracy of theretrieved concentrations. An optical model is defined here asthe absorption and backscattering of chlorophyll, suspendedsediments, and colored dissolved organic matter per unitconcentration of CHL, TSM, and CDOM, respectively. Thesevalues are also called specific inherent optical properties(SIOPs). Because the SIOPs in the North Sea may vary in timeand space (Althuis et al., 1996, p. 147), much effort was putinto the selection of an appropriate SIOP model out of severalmodels that were available for the North Sea from differentcruises.

The selection was based on a comparison between thestatistical distributions of in situ TSM values and valuesderived from the remote sensing imagery. To this purpose, weused the concentrations measured in situ over the whole NorthSea between April 1993 and July 1994 within the particulatematter North Sea (PMNS) project (Althuis et al., 1996),consisting of 110 samples. The distribution of retrievedconcentrations from SeaWiFS images in the North Sea should(neglecting long-term trends) coincide with the distribution ofconcentrations found in the PMNS project. The log-normalprobability density distribution (P) is a useful model for bio-optical variability at a variety of spatial and temporal scales(Campbell, 1995) and is defined in the following equation:

P xx

( | , )( )

µ σσ π

µσ=

− −1

2

2

22eln x

(2)

where x is the CHL, CDOM, or TSM concentration; and µ andσ are the log-normal mean and standard deviation, respectively.The concentrations in the PMNS database do have a log-normaldistribution, with mean and standard deviation as shown inTable 1. In the analytical model (Equation (1)), the CDOMabsorption was fixed at 0.37 m–1 at 440 nm, an average valuefor the Dutch coastal zone, and the pre-factor f was fixed at0.38, a consistent value for Dutch coastal waters that was basedon theoretical calculations for a sun zenith angle ofapproximately 40°. It was then found that the SIOP modelobtained during the field cruise in 1998 (Ruddick et al., 1998)gave the best overall statistical agreement between the TSMconcentration distribution in the PMNS database and thedistribution in the SeaWiFS images.

When substituting this SIOP model in Equation (1), keepingthe CDOM absorption (0.37 m–1 at 440 nm) and CHLconcentration (5 mg·m–3) fixed, we find the following opticalproperties for SeaWiFS band 5: absorption and backscatteringby pure water 0.059 and 0.001 m–1, respectively; absorption byCHL 0.032 m–1; absorption by CDOM 0.11 m–1; and specificabsorption and backscattering by TSM 0.0056 and 0.0059 g–1·m2,respectively. Solving for TSM, with f = 0.38, gives thefollowing relationship:

TSM0.53= − +

−R

R555

555

0001003 00059

.. .

(3)

where the TSM concentration is in grams per cubic metre; andR555 is the subsurface irradiance reflectance in SeaWiFS band 5(dimensionless), centred at 555 nm. The minimum reflectance,for a (hypothetical) zero concentration of TSM, is 0.002.Applying the algorithm to a reflectance smaller than this valuewould result in negative TSM values, which is undesirable.However, reflectance smaller than 0.002 in band 5 was notencountered in the atmospherically corrected SeaWiFS images.

When this algorithm is applied to the atmosphericallycorrected SeaWiFS image, the resulting TSM map is stored inHDF Scientific Data format, together with navigational(latitude and longitude), meteorological, and auxiliaryinformation for successive postprocessing steps in the GISenvironment.

The estimated error in the retrieved TSM concentration wasbased on the error analysis presented in Table 1 in Ruddick etal. (2000). This table contains estimated reflectance errors inSeaWiFS bands for a clear and turbid atmosphere and for clearand turbid water. In our analysis, all reflectance values wereconverted to subsurface irradiance reflectance first. Using theSIOP model presented in this study, the clear and turbid waterreflectance values given for SeaWiFS band 8 were estimated tocorrespond to TSM concentrations of 7 and 150 g·m–3,respectively. Because 150 g·m–3 is very high, even for NorthSea coastal waters, we recalculated the values for turbid water,now corresponding to a reflectance of 0.0096 (ρw

(8) = 0.005)and a TSM concentration of 37 g·m–3. Using the inverse ofEquation (3), reflectance values for clear and turbid water inSeaWiFS band 5 were calculated as 0.057 and 0.133,respectively. For the clear atmosphere, we estimated errors inTSM concentration for clear and turbid water to be 0.7 and6 g·m–3, respectively (9% and 16%, respectively). For the turbidatmosphere, we estimated errors in TSM concentration forclear and turbid water as 1.6 and 10 g·m–3, respectively (22%

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Mean, µStandarddeviation, σ

ln(TSM) (g·m–3) 1.538 0.871ln(CHL) (mg·m–3) 1.607 0.897

Table 1. Mean and standard deviation of thenatural logarithms of the TSM and CHLconcentrations in the PMNS database.

and 27%, respectively). These numbers show that the TSMconcentration calculated with the method presented in thispaper is twice as accurate in clear atmospheres as it is in turbidatmospheres. Note that this error analysis only reflects thepropagation of errors in the atmospheric correction to the TSMconcentration and does not treat errors in, for example, theSIOP model used.

ResultsA good example of the final product is shown in Figure 3,

which is the TSM map of 25 February 2000 in the standardlayout. The box in Figure 3 covers part of the East Anglianplume that shows remarkable “sawtooth” patterns. It is unclear,however, if these structures are correlated with the bathymetrystructures of the backs off the coast in East Anglia, but thefigure shows the superior spatial coverage and resolution inrevealing these structures. The smooth shape of the plumefarther north indicates a sharp boundary between relativelyclear waters in the northern part of the North Sea and relativelyturbid waters in the southern part (Van Raaphorst et al., 1998).The entire continental coast is very rich in TSM content, partlyowing to resuspension of material over the Flemish banks.Clearly discernable is “coastal river”, which flows along theDutch coast from southwest to northeast and exhibits a variableband of high TSM concentrations (Visser et al., 1991). Inwinter, an average strong southwesterly wind pushes relativelyclear water through the English Channel into the North Sea(Dyer and Moffat, 1998).

Next to the TSM maps, additional information is providedfor better interpretation. The legends have eight colors, rangingfrom dark blue to red and roughly indicating the range in TSMconcentration in the North Sea: the brighter the color, thehigher the concentration. Since the distribution of TSM in theNorth Sea has a mean value less than 5 g·m–3, the patterns withelevated TSM values can be identified at a glance. Because allmaps have the same legend, they are easy to compare. Thedisadvantage of using one legend for all maps is that thepatterns are more difficult to identify in periods of lowconcentrations. In some areas, clouds prevent the observationof the water surface by the satellite. These areas are coloredwhite in the maps. Surface water that does not belong to theNorth Sea is indicated by a grey color. Wind speed anddirection are important because they are driving forces for thetransport of suspended matter (Dolata et al., 1983). The windpushes water masses in certain directions and the waves stir upsilt particles that take time to settle again. Because the responsetime of the North Sea to changes in the wind speed variesbetween 6 and 34 h (Ishiguro, 1983), graphs with wind speedand direction in the days before the observation are included.Wind data were obtained from the K13 measuring station(53.22°N, 3.22°E) located approximately 100 km west of theisle of Texel (source Koninklijk Nederlands MeteorologischInstituut (KNMI)).

Bimonthly mean mapsThe number of observations by the SeaWiFS instrument in

the North Sea area that can be used amounts to more than 100per year. This allows the construction of time series andintegration over months to reveal the seasonal change. A longseries of images reveals normal patterns of suspended matterand unusual events. The large number also overcomes theproblems posed by the cloud cover in coastal areas. Cloudscause white spots in individual maps, but full coverage can beachieved through the combination of maps. Bimonthly meanmaps were constructed from 15–33 maps.

Figure 4 shows the average TSM field, constructed from acomposite of 15 images in the period January and February2002. Again, the most striking phenomenon in this image is the“plume” of suspended matter that stretches across the NorthSea from England towards the Waddensea. The plume shows arapid increase in TSM concentration above the Oyster Groundsand subsequently curves towards the north. In winter, a strongsouthwesterly wind pushes water through the English Channelinto the North Sea. Strong currents in the English Channelresult and cause increased suspended matter input through theStraits of Dover. Increased TSM concentrations can be seenalong the southeast coast of England. The dominant source offine (and coarse) material to the East Anglian coast is from clifferosion (McCave, 1987). Part of the suspended matter thatreaches the southern North Sea in winter settles off the coasts ofEngland and Belgium (Eisma and Kalf, 1987). Erosion andresuspension, especially in shallow coastal areas as a result ofwave action, lead to the transport of sediment farther north.

Other maps are constructed (not shown here) that provideinformation on the standard deviation and number ofobservations per pixel. In addition, maps have been constructedto provide information on the mean summer and winterdistributions.

ValidationIn a highly dynamic and nonhomogeneous TSM field, it is

very hard to compare the satellite-based information with insitu measurements owing to the strict limits on the co-locationin time and place of the observation. Here we present anexample of four monitoring sites along the coast from Holland(see, e.g., http://www.waterbase.nl) sampled on 31 March 1999in comparison with a SeaWiFS TSM map acquired the sameday at 1159 UTC. The in situ concentrations and the valuesderived from the SeaWiFS images are presented in Table 2.Time differences between the remote sensing image and in situsampling vary from 15 min to more than 3 h. Close to the coast,the gradient in TSM concentration is very large, and togetherwith tidal currents the TSM concentrations can varysignificantly within a few hours time. In Figure 5, the spatialvariability as derived from the remote sensing image is shown,together with the locations of the in situ sampling stations.Within a few kilometres the TSM concentration changesconsiderably, which makes the absolute validation near the

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coast (stations Noordwk2 and Noordwk10) unreliable (theTSM gradient along the transect at station Noordwk10 isapproximately 2–3 g·m–3·km–1). The station Noordwk20 is alsopositioned within a TSM gradient, but the overpass timedifference of only 15 min makes this validation point more

reliable, and indeed the in situ and remotely sensedconcentrations match this time exactly. Station Noordwk70 hasa time difference of more than 3 h. Thus, absolute validationbased on the temporal and spatial coincidence of sample station

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Figure 4. End-product example from the North Sea atlas of a bimonthly mean map of totalsuspended matter for January–February 2002. See text for a discussion of the main visiblepatterns.

Figure 3. End-product example from the North Sea atlas of total suspended matter for25 February 2002 in the standard layout. See text for a discussion of the main visible patterns.

and remote sensing image is difficult to achieve, and exactmatches will be very sparse.

A more versatile approach was sought to make better use ofthe extensive database of in situ measurements available frommore than 10 years of monitoring by the Dutch managementauthorities. A number (approximately 20) of stations on theDutch part of the North Sea are visited twice a month as part ofthe regular monitoring program by the National Institute forCoastal and Marine Management (RIKZ). The conceptdeveloped for this study is to compare TSM distributions ratherthan single measurements to improve statistical accuracy and tobe less vulnerable to errors due to small but importantdifferences in space and time. In situ TSM measurements takenin the period 1989–1999 were selected for three stations thatare progressively more offshore from the Dutch coast (stationsNoordwk10, Noordwk20, and Noordwk70 are 10, 20, and70 km offshore, respectively). The combined distribution of thethree stations (775 measurements) has a log-normal shape withmean and standard deviation similar to that of the PMNSdataset (µ = 1.48, σ = 0.76), indicating that these datasets coverthe concentration ranges to be expected in the North Sea. Notethat for the validation an in situ dataset different from that forthe selection of the SIOP model is used. For the same three

stations, TSM values were derived from the 129 remote sensingimages in the year 2000. With the exclusion of clouded pixels,this resulted in 72, 66, and 80 usable observations forNoordwk10, Noordwk20, and Noordwk70, respectively. Thisimplies an annual temporal coverage rate of around 20%. Notethat for station Noordwk70 the satellite has a samplingfrequency five times higher than that for the in situ sampling.

As part of the validation, comparative statistics between insitu and remote sensing derived values were computed for eachof the three stations separately, and the empirical cumulativedistribution plots were calculated and are presented inFigure 6.

The mean and standard deviation of the log-normal statisticsfor the in situ TSM values and the values derived from theremote sensing images are presented in Table 3. Both datasetsshow a consistent pattern of decreasing concentrations towardsthe offshore, and the difference in the log-normal means is lessthan 0.5 g·m–3, illustrating that the atmospheric correction andTSM retrieval method do not introduce serious systematicbiases. In the remote sensing dataset the standard deviation forstation Noordwk10 is much higher than that for stationNoordwk70, which is likely because close to the coast muchstronger dynamics in TSM are expected owing to resuspensionby tidal movements and offshore winds. Remarkably enough,the standard deviation in the in situ dataset demonstrates anopposite trend and, according to this dataset, concentrationsgreater than approximately 15 g·m–3 are more likely to occur atstation Noordwk70 than at station Noordwk10. At the momentwe have no explanation for this behavior.

Two-sample Kolmogorov–Smirnov goodness-of-fit hypothesistests (K–S test) were used to determine if the in situ and remotesensing samples were drawn from the same underlyingcontinuous population. For station Noordwk70, a probability of0.40 indicates that the datasets are consistent with a singledistribution function. For stations Noordwk20 and Noordwk10,however, the probabilities are 0.014 and 0.003, respectively,proving that they arise from different distribution functions.

The reasons for this difference might be the mixing ofcontinental and maritime aerosols (sea spray) close to the coast,causing the atmospheric correction to be less accurate, becausein the atmospheric correction the ratio of 765 nm to 865 nm ofthe aerosol reflectance is assumed constant. Another possibilityis a significant difference in the SIOP, causing overestimationof the TSM concentrations. The coastal river close to the coastis a conveyor belt of suspended sediment from the Rhine Riverand is likely to have different scattering characteristics.Another point to note is the fact that the datasets were acquiredover different time intervals and the measurements have aslightly different distribution over the seasons. Although thelong-term trends in the North Sea for TSM are supposed to bevery small, there might be enough annual variation to make thecomparison less accurate, especially along the coast where tidaland wind effects cause large variations in TSM concentrations.Therefore, future validation research will focus on selectingand using in situ and remote sensing datasets acquired in thesame period and compensating for seasonal effects.

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Canadian Journal of Remote Sensing / Journal canadien de télédétection

TimeUTC

TSM (g·m–3)

Station In situ RS

1352 Noordwk2 7 101304 Noordwk10 5 111214 Noordwk20 7 70840 Noordwk70 2 8

Table 2. Comparison between in situ andremote sensing (RS) concentrations on31 March 1999.

Figure 5. In situ stations (diamonds) plotted over a SeaWiFS TSMmap, which was acquired the same day, and used for validation.

Discussion and conclusionThe monitoring of the North Sea coastal waters is carried out

by government organizations. Based on the existingmeasurement strategies in Belgium and The Netherlands,requirements can be set to the information from satellites. Themain range of interest for total suspended matter (TSM) incoastal waters is 1–20 g·m–3. However, higher concentrationsdo occur because of wave and tidal effects. The demand for thistype of information is related to sedimentation rates nearshipping lanes, models of pollution dispersion, or informationon the underwater light field for biological monitoring. Therequired accuracy is 10% or better. The temporal resolutionshould be equal to or better than that of non-remote sensingmethods; presently, monitoring stations are visited 5–20 timesper year.

In this paper, we have shown that images from the SeaWiFSinstrument that are processed with appropriate algorithms forturbid coastal waters can fulfill most of the requirements. Thespatial and temporal resolution are better than those of thetraditional monitoring systems. All seasons are well covered,and the large seasonal variations, because of salt stratification,wind fluctuations, transport, and erosion, show up clearly in thebimonthly maps of TSM distribution. Spatial variations ofsuspended sediment, often caused by input from rivers plus thewind- and wave-induced resuspension over shallow areas, arecovered.

Presently, a single-band algorithm is chosen as a simple andstraightforward test of matrix inversion that will be applied inthe future. This robust TSM algorithm is based on the inversionof an analytic model applied for band 5 (555 nm). The forwardmodel is based on measurement of the specific inherent opticalproperties of the North Sea waters, and the accuracy of thealgorithm is estimated to be between 9% and 27% in areas inthe North Sea that can be described by our SIOP dataset and asimple aerosol model in the lower atmosphere.

Validation remains a critical point in the monitoringinformation from satellites. We have presented two examples ofvalidation that are not yet conclusive on the accuracy of theTSM product in the North Sea. One promising way is tocompare the seasonal distributions from in situ data andsatellite data over a period of years. The use of automaticbuoys, including probing of optical depth of aerosol, might alsoimprove the validation of remote sensing products in the NorthSea.

Because coastal processes induce natural variations inoptical properties and concentrations on a regional scale (e.g.,river inputs, resuspension over shallow areas), bio-opticalmodels should be based on SIOPs of the regional water type.The regional approach will allow the development of regionalconcentration retrieval algorithms.

In fact, the match between the actual SIOP in the water andthe SIOP used in the model limits the accuracy of the single-band algorithm. Using local SIOPs in the inversion models canlead to accurate remote sensing products. The match betweenthe modeled reflectance spectra (using TSM obtained from

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Mean TSM,exp(µ) (g·m–3)

Standarddeviation, σ(g·m–3)

Station In situ RS In situ RS

Noordwk10 5.1 4.9 0.68 1.13Noordwk20 3.9 3.4 0.76 1.02Noordwk70 3.2 3.4 0.84 0.73

Table 3. Log-normal statistics for the in situ andremote sensing (RS) derived TSM values.

Figure 6. Cumulative distribution plot for the (cloud-free) TSMconcentrations derived from SeaWiFS images for the year 2000(solid line) and for the in situ TSM concentrations sampledbetween 1989 and 1999 (dotted line): (A) station Noordwk70,(B) station Noordwk20, (C) station Noordwk10. The numbers ofsamples used in the distribution are given in parentheses.

image processing) and the satellite-obtained subsurfacereflectance spectra in multiple bands could provide the measureof confidence.

AcknowledgementsWe thank Erin Hoogenboom from the Directorate for the

Coastal and Marine Management (RIKZ) for his continuoussupport over the last years. The Data User Program (DUP) fromthe European Space Agency funded part of this work(http://styx.esrin.esa.it:5000/DUP/). We are grateful to thereceiving station of Dundee University, the SeaWiFS projectteam, the Ocean Color Data Support Team, the DistributedActive Archive Center at Goddard Space Flight Center, and theSeaDAS development team for acquiring, providing,distributing, and supporting the use of SeaWiFS data. Theprocessing of the SeaWiFS images was performed with theSeaDAS 4.0 software (available from http://seadas.gsfc.nasa.gov/), adapted with the MUMM atmospheric correctionextension for coastal waters (available from http://www.mumm.ac.be/OceanColour/) on a SUN Ultra 10 workstation.Data were exported to hdf scientific data format(http://hdf.ncsa.uiuc.edu/) for further processing. The results canbe downloaded from http://www.watermarkt.nl/watermarkt/digiproducts/noordzee-atlas2000.pdf or visit www.watermarkt.nl, click on “productcatalogus”, and search for “atlas”.

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