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    Assessment of water quality in Lake Garda (Italy) using Hyperion

    Claudia Giardino a,, Vittorio E. Brando b, Arnold G. Dekkerb,Niklas Strmbeckc, Gabriele Candiani a

    a Optical Remote Sensing Group, CNRIREA, Milano, Italyb Environmental Remote Sensing Group, CSIRO-Land and Water, Canberra, Australia

    c Department of Limnology, EBC, Uppsala University, Uppsala, Sweden

    Received 22 August 2006; received in revised form 20 December 2006; accepted 23 December 2006

    Abstract

    For testing the integration of the remote sensing related technologies into the water quality monitoring programs of Lake Garda (the largest

    Italian lake), the spatial and spectral resolutions of Hyperion and the capability of physics-based approaches were considered highly suitable.

    Hyperion data were acquired on 22nd July 2003 and water quality was assessed (i) defining a bio-optical model, (ii) converting the Hyperion at-

    sensor radiances into subsurface irradiance reflectances, and (iii) adopting a bio-optical model inversion technique. The bio-optical model was

    parameterised using specific inherent optical properties of the lake and light field variables derived from a radiative transfer numerical model. A

    MODTRAN-based atmospheric correction code, complemented with an air/water interface correction was used to convert Hyperion at-sensor

    radiances into subsurface irradiance reflectance values. These reflectance values were comparable to in situ reflectance spectra measured during

    the Hyperion overpass, except at longer wavelengths (beyond 700 nm), where reflectance values were contaminated by severe atmospheric

    adjacency effects. Chlorophyll-a and tripton concentrations were retrieved by inverting two Hyperion bands selected using a sensitivity analysis

    applied to the bio-optical model. The sensitivity analysis indicated that the assessment of coloured dissolved organic matter was not achievable in

    this study due to the limited coloured dissolved organic matter concentration range of the lake, resulting in reflectance differences below the

    environmental measurement noise of Hyperion. The chlorophyll-a and tripton image-products were compared to in situ data collected during

    the Hyperion overpass, both by traditional sampling techniques (8 points) and by continuous flow-through systems (32 km). For chlorophyll-a the

    correlation coefficient between in situ point stations and Hyperion-inferred concentrations was 0.77 (data range from 1.30 to 2.16 mg m 3). The

    Hyperion-derived chlorophyll-a concentrations also match most of the flow-through transect data. For tripton, the validation was constrained by

    variable re-suspension phenomena. The correlation coefficient between in situ point stations and Hyperion-derived concentrations increased from

    0.48 to 0.75 (data range from 0.95 to 2.13 g m3) if the sampling data from the re-suspension zone was avoided. The comparison of Hyperion-

    derived tripton concentrations and flow-through transect data exhibited a similar mismatch. The results of this research suggest further studies to

    address compatibilities of validation methods for water body features with a high rate of change, and to reduce the contamination by atmospheric

    adjacency effects on Hyperion data at longer wavelengths in Alpine environment. The transferability of the presented method to other sensors and

    the ability to assess water quality independent from in situ water quality data, suggest that management relevant applications for Lake Garda (and

    other subalpine lakes) could be supported by remote sensing.

    2007 Elsevier Inc. All rights reserved.

    Keywords: Hyperspectral satellite data; Lake waters; Bio-optical modelling; In situ data

    1. Introduction

    Lake water is an essential renewable resource for mankind

    and the environment and it is important for civil (drinking water

    supply, irrigation, transportation), industrial (processing and

    cooling, energy production, fishery) and recreational purposes.

    Sustainable use of water resources requires the coupling of

    surface waters assessment monitoring programs and decision

    making and management tools. The Water Framework Di-

    rective (WFD) of the European Commission (Directive 2000/

    60/EC, 2000) is the major reference in Europe to guide efforts

    for attaining a sustainable aquatic environment in the years to

    Remote Sensing of Environment 109 (2007) 183195

    www.elsevier.com/locate/rse

    Corresponding author. Tel.:+39 0223699298; fax: +39 0223699300.

    E-mail address: [email protected] (C. Giardino).

    0034-4257/$ - see front matter 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2006.12.017

    mailto:[email protected]://dx.doi.org/10.1016/j.rse.2006.12.017http://dx.doi.org/10.1016/j.rse.2006.12.017mailto:[email protected]
  • 7/28/2019 Water Qual Hyperion 07

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    come. The WFD includes guidelines which define the cat-

    egories of quality and the required components and parameters.

    As some of these parameters can be determined by Remote

    Sensing (RS) with a reasonable accuracy, RS-related technol-

    ogies may be integrated in the monitoring programs defined by

    the WFD, provided they can be demonstrated to independently

    assess Water Quality Parameters (WQPs).Since the 1980s satellite RS represents an opportunity for

    synoptic and multitemporal viewing of water quality. To estimate

    WQPs from satellite data three different approaches can be used

    (Cracknell et al., 2001; Dekker et al., 1995). The (1) empirical

    approachis based on the development of bi-variate or multivariate

    regressions between RS data and measured WQPs. Digital

    numbers or radiance values at the sensor, as well as their band

    combinations, are correlated with in situ measurements of WQPs,

    usually collected in coincidence of the sensor overpass. A

    summary of empirical approaches for lakes can be found in

    Lindell et al. (1999). The (2) semi-empirical approach may be

    used when spectral characteristics of the parameters of interestare known. This knowledge is included in the statistical analysis

    by focusing on well-chosen spectral areas and appropriate

    wavebands used as correlates. An example of a semi-empirical

    approach with different sensors is reported by Hrm et al. (2001)

    over Finnish lakes. In the (3) analytical approach, WQPs are

    related to the bulk Inherent Optical Properties (IOPs) via the

    Specific Inherent Optical Properties (SIOPs). The IOPs of the

    water column are then related to the Apparent Optical Properties

    (AOPs) and hence to the Top of Atmosphere (TOA) radiance,

    such as described by the radiative transfer theory (Mobley, 1994;

    Vermote et al.,1997). The analytical method involves inverting all

    above relations (WQPsIOPsAOPsTOA radiances) to

    determine the WQPs from RS data. An example of such approach,using Landsat over lakes, can be found in Dekker et al. (2001) for

    the total suspended matter retrieval.

    Quantitatively, the relationships developed to assess water

    quality in lakes within empirical and semi-empirical approaches

    are often scene dependent and only apply to the data from which

    they are derived. Well-calibrated and validated physics-based

    approaches are instead applicable to every scene acquired over

    the selected lake (presuming constant SIOPs), giving the

    opportunity to assess water quality independently from ground

    measurements of WQPs. The monitoring of spatially heteroge-

    neous parameters, as re-suspension phenomena due to local

    variability in wind and circulation, or algal blooms at the surfacealso necessitates these (in situ independent) methods. Dekker

    et al. (2002) investigated the capabilities of Landsat-TM and

    SPOT data for retrospective analysis in Dutch lakes. Both

    sensors were capable of describing larger concentration

    gradients characterised by temporal changes that were not

    represented by point in situ data. Kutser (2004) used Hyperion

    data to map accumulation of aggregations of cyanobacteria in

    the Gulf of Finland, an assessment unachievable by traditional

    in situ sampling due to spatial and temporal issues. Kutser

    (2004) also showed that flow-through systems were only

    suitable to map chlorophyll from a fixed depth, and therefore

    inappropriate for assessing cyanobacteria blooms closer to or at

    the water surface.

    This study is part of ongoing research efforts aimed at de-

    veloping RS strategies towards the implementation of the WFD,

    ensuring systematic monitoring of water quality in Lake Garda,

    the largest Italian lake. Empirical and semi-empirical ap-

    proaches were previously investigated to retrieve chlorophyll

    concentrations in the lake (Brivio et al., 2001; Candiani et al.,

    2003; Giardino et al., 2005) but their results were scenedependent. The aim of this study is to provide a RS-based

    measurement tool, transferable to different RS-instruments. It

    would be useful for water management authorities of Lake

    Garda for coarse scale regular monitoring (with high revisiting

    time spaceborne sensors), for intermediate/fine scale studies

    (with high spatial resolution satellite and airborne sensors) and

    for retrospective analysis with time-series imagery (as in

    Dekker et al., 2005). As a test, hyperspectral Hyperion data

    (with a 30 m pixel size and a choice of more than 200 spectral

    channels), analytical modelling, and in situ measurements

    coincident with the satellite overpass for a validation or com-

    parison of the image-derived products, were considered ap-propriate. The approach used in this study builds on the method

    developed for Hyperion imagery of a sub-basin of a subtropical

    bay in Australia (Brando & Dekker, 2003). Based on a bio-

    optical model sensitivity analysis, Hyperion bands were

    selected and concentrations of chlorophyll-a and tripton (the

    non-algal particles of the suspended particulate matter) were

    retrieved. Point in situ data for an initial validation of the

    products, followed by a comparison of concentrations retrieved

    from Hyperion using high spatial resolution flow-through

    estimates of chlorophyll-a and tripton were used.

    2. Materials and methods

    2.1. Study area and fieldwork activities

    Approximately 500,000 lakes over 1 ha surface area exist in

    Europe. Most of the largest European lakes are located in the

    Nordic countries and in the Alpine regions (EEA, 1999). The

    most important Italian lake district is located in the subalpine

    region and represents more than 80% of the total Italian

    lacustrine volume (Premazzi et al., 2003). Lake Garda, located

    65 m a.s.l. around 4540 N and 1041 E at the eastern border

    of the subalpine region, is the largest Italian lake. It has a surface

    area of 368 km2, a volume of 49 million m3 and a maximum and

    a mean depth of 350 m and 133 m, respectively. The averagevalue of the Secchi disk depth is 4.5 m in summer and 16 m in

    winter. Chlorophyll-a (CHL-a) and suspended particulate matter

    (SPM) concentrations range from 0.5 to 12 mg m3 and from

    0.1 to 5.5 g m3, respectively. The coloured dissolved organic

    matter concentration (absorption coefficient at 440 nm, aCDOM(440)) ranges from 0.017 to 0.36 m1. Average concentrations

    of CHL-a, SPM and aCDOM(440) are around 2.7 mg m3, 2.5 g

    m3 and 0.09 m1, respectively (Premazzi et al., 2003; Zilioli,

    2002). According to the OECD guidelines (Vollenweider &

    Kerekes, 1982), Lake Garda can be classified as an oligo-

    mesotrophic basin.

    In collaboration with the local agencies in charge of

    limnological monitoring of Lake Garda, intensive fieldwork

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    activities were run in the whole basin, within several national

    and international projects (Lindell et al., 1999; Zilioli, 2002,

    2004). More than 30 days over 5 years of in situ measurements

    were performed to achieve a comprehensive dataset of

    concentrations of WQPs, of IOPs and AOPs, leading to the

    parameterisation of a three-component bio-optical model

    according to Strmbeck et al. (2003). In this study in situ datacollected on 22nd July 2003 to validate the atmospheric

    correction of Hyperion data, to validate the image-derived

    WQPs products, as well as to improve the bio-optical

    parameterisation are presented.

    Water samples of the first integrated meter of water column in

    9 point stations (Fig. 1) were analysed for CHL-a and tripton

    (TR) concentrations. The in situ samples were collected

    within a 3-hour interval around the image acquisition. Water

    was filtered through Watman GF/F glass fiber filters and the

    material retained was analysed for CHL-a concentrations

    according to the analytical method ISO 10260-E (1992).Phytoplankton was composed by 70% of Chlorophyta

    species and by 30% of almost equal parts of Cryptophyceae,

    Diatomeae and Cyanophyta species. Because the suspended

    particulate matter can be divided into phytoplankton and into

    the non-algal component (i.e., tripton), TR was indirectly

    estimated from SPM concentrations. They were measured

    on pre-combusted and pre-weighted Watman GF/F filters,

    dried at 95 C overnight. The biomass of phytoplankton

    was considered correlated to CHL-a and the formula

    TR=SPM0.07CHL-a (with TR and SPM in g m3 andCHL-a in mg m3) was used to separate TR from SPM. Gons

    et al. (1992) observed that for fresh water algae the part of

    SPM determined by the biomass of phytoplankton can vary

    between 0.02 and 0.1. The average value of 0.07 had been

    successfully adopted in Dutch lakes (Hoogenboom et al.,

    1998), Finnish lakes (Kutser et al., 2001) as well as in coastal

    waters (Brando & Dekker, 2003). Thus, itwas supposed to be

    valid for Lake Garda too.

    Five water samples collected during the day were used to

    measure the absorption spectra of phytoplankton aph()

    and tripton aTR(), according to the method showed in

    Strmbeck and Pierson (2001). The absorption spectra ofparticles ap() retained onto the GF/F filters, were measured

    using a laboratory spectrophotometer and the filter-pad

    technique (Tassan & Ferrari, 1995). The filters were then

    treated with cold Methanol to extract pigments and the

    absorption spectra of tripton aTR() of these bleached filters

    were measured. The absorption spectrum of phytoplankton

    aph() was derived by subtracting aTR() from ap() spectra.

    A 32-km-long transect (Fig. 1) of fluorescence and turbidity

    data were collected using a flow-through system. The system

    is composed of a hydraulic device, (essentially an intake

    pipe) continuously pumping water from 0.5 m depth into a

    Turner Design Scufa-II fluorometer/turbidimeter, and a GPS,

    both logged by a Campbell data-logger. Logged values offluorescence (in mV) and turbidity, in Nephelometric

    Turbidity Units (NTU), were corrected for delays caused

    by the flow-through system. Flow-through data were con-

    verted into chlorophyll-a and tripton using the concentrations

    derived from laboratory analysis on water samples. Eight

    laboratory-concentrations were regressed against the average

    of logged values over the nearest 100 m to the GPS location

    where the water samples were collected. By means of linear

    regression analysis, the measured in vivo fluorescence was

    transformed into chlorophyll-a concentrations (R2=0.55),

    and turbidity into tripton concentrations (R2=0.68) (Fig. 2).

    It was hence assumed that flow-through data were able todescribe both CHL-a and TR concentrations along the 32-

    km-long transect although turbidity, because it includes

    phytoplankton scattering, is more closely a measure of SPM.

    Spectroradiometric measurements of water radiance using

    the PR-650 spectroradiometer were performed to calculate

    the subsurface irradiance reflectance R(0, ) in three

    pelagic stations (4, 6 and 7 in Fig. 1). R(0, ) values were

    computed from remote sensing reflectances Rrs(0+, ),

    measured above-water according to the SeaWifs protocol

    (Fargion & Mueller, 2000). Effects of the lake surface

    roughness on above-water Rrs(0+, ) determinations were

    corrected by a sky-radiance reflectance factor and by an

    offset term, that does not impose a constrained normalisation

    Fig. 1. Study area and location of fieldwork activities performed within a 3-hour

    interval around the image acquisition. 1 to 9 are the stations where water was

    sampled for laboratory analysis for chlorophyll-a and tripton concentrations

    (stations 4, 6 and 7 have also PR-650 radiometric measurements). The flow-

    through system was cruised throughout all the stations, for a length of about32 km. The 7.5-km-wide portion of lake imaged by Hyperion is outlined.

    185C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183195

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    at 750 nm (Toole et al., 2000). Literature data (Toole et al.,

    2000) for clear sky and high wind speeds were used (on 22nd

    July, 2003 the average wind speed on the lake was 6 m s

    1

    ).Assuming an air/water interface parameter of 0.533 (Lee

    et al., 1994) and a Q-factor of 4.2 sr1 the roughness-cor-

    rected Rrs(0+, ) were then transformed into R(0, )

    values. As reported in Strmbeck et al. (2003) the Q-factor

    was the average, between 400 and 750 nm, of a spectral Q-

    factor computed using the HYDROLIGHT 4.2 model

    (Mobley, 1994; Mobley & Sundman, 2001).

    2.2. The bio-optical model

    The bio-optical model used in this study was similar to

    previously published three-components (i.e., chlorophyll-a,

    tripton and coloured dissolved organic matter) Case-2 or lakewater models, e.g., Pierson and Strmbeck (2001). The sub-

    surface irradiance reflectance R(0, ) was calculated as a

    function of absorption and backscattering coefficients according

    to Walker (1994):

    R 0;k 1

    1 Pld k Plu k

    dbb k

    a k bb k 1

    where, a() is the spectral total absorption coefficient, bb() is

    the spectral total backscattering coefficient, and d()/u() is

    the ratio of the average cosine of the downwelling light to that of

    the upwelling light (Mobley, 1994).The spectral total absorption coefficient a() was computed

    as:

    a k aw k CHL a aph k aCDOM 440 e

    SCDOM k440

    TR aTR 440 eSTR k440

    2

    where, aw() is the pure water absorption (Pope & Fry, 1997;

    Smith & Baker, 1981), aph () is the chlorophyll-specific

    phytoplankton absorption, SCDOM is the slope factor of the

    absorption spectra of coloured dissolved organic matter,

    aTR (440) is the absorption coefficient at 440 nm specific

    for 1 g m3 of tripton, STR is the slope factor of the absorp-

    tion spectra of coloured dissolved organic matter. In this study

    SCDOM and STR were equal to 0.021 and 0.012, respectively.

    The spectral total backscattering coefficient bb() wascomputed as:

    bb k bbw k CHL a bbph

    k

    TR bbTR

    550 k

    550

    ni

    3

    where, bbw() is the backscattering coefficient of pure water

    (Morel, 1974; Dall'Olmo & Gitelson, 2006), bbph () is the

    specific backscattering caused by phytoplankton, bbTR () is the

    specific backscattering coefficient at 550 nm for 1 g m3 of

    tripton, ni is an exponent describing the spectral dependency of

    tripton backscattering (mainly due to its inorganic components).

    The specific backscattering by phytoplankton was computedusing an expression based on Gordon et al. (1988), Morel

    (1988), Ammenberg et al. (2002), and Roesler and Boss (2003):

    bbph k bph

    555 aph 555

    k555

    nph

    kaph k

    bbph

    bph

    4

    where, bph (555) is the chlorophyll-a specific scattering, nph is

    an exponent describing the spectral dependency of the phy-

    toplankton beam attenuation, k is an empirical coefficient reg-

    ulating the effect by phytoplankton absorption and bbph /bph is

    the average spectral backscattering efficiency of phytoplankton.The parameterisation of the bio-optical model used in this

    study is largely based on the data presented in Strmbeck et al.

    (2003), which have been acquired in Lake Garda on 10th and

    11th October 2002. The dataset contains discrete measurements

    of WQPs, total absorption a and total scattering b coefficients

    at 9 wavelengths obtained with a WET Labs ac-9, and total

    backscattering bb coefficients at 6 wavelengths obtained by a

    HOBILabs HydroScat-6. In particular, scattering b and back-

    scattering bb data of the lake, were used to parameterise Eq. (4)

    originally adopted for oceanic phytoplankton. Because the

    average specific absorption coefficients of phytoplankton,

    aph () and tripton, aTR () derived from data collected on 22nd

    July 2003 were comparable to data collected on 10th and 11th

    Fig. 2. Scatter plots of fluorescence vs. chlorophyll-a (using stations 1 to 8) and of turbidity vs. tripton (using stations 2 to 9) with the calibration lines (turbidity in

    Station 1 and fluorescence in Station 9 were no available).

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    October 2002, they were averaged and integrated in the existing

    dataset. Apparently, the natural modifications of algal and

    tripton compositions occurred between the two periods had a

    negligible effect on their absorption spectral properties.

    Together with the d()/u() ratio (average value from

    450 nm to 750 nm equal to 0.327), that was derived running

    HYDROLIGHT 4.2 with inputs typical of Lake Garda (e.g.,

    IOPs, averages values of wind speed and visibility ranges,

    summertime Sun zenith angles at 111 h UTC) (Strmbeck

    et al., 2003), these SIOPs uniquely characterise the parameter-isation of Eq. (1) for Lake Garda waters. Table 1 summarises the

    bio-optical model parameters with the day of acquisition and the

    data provider, Fig. 3 shows the SIOPs used in this study.

    The performance of the above parameterisation was

    evaluated using the 22nd July 2003 dataset. It consisted of the

    PR-650-derived measurements of R(0, ), collected in the

    stations 4, 6 and 7 (Fig. 1), in which concentrations of CHL-a

    and TR were also known. These concentrations, together with

    the long-term (i.e., 0.09 m1) average value of aCDOM(440) of

    Lake Garda (as aCDOM(440) concentrations were not measured

    in this campaign), were given as input to the bio-optical model

    to simulate R(0

    , ) spectra, assuming that SIOPs were thesame of October 2002. The optical closure between in situ

    determinations of R(0, ) and the simulated values from

    forward modelling was quantified with the Root Mean Square

    Error (RMSE) and the relative RMSE (in %):

    RMSE

    ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPNi1

    P

    Xi Xi

    2N1

    vuuut5

    Relative RMSE RMSE

    1

    NPNi1

    P

    Xi

    d 100 6

    where, N is the number of bands, and Xi and Xi are the

    subsurface reflectance values from in situ data and forward

    modelling, respectively. The number of bands N was 28; 22 of

    these in the visible (VIS) range, from 480 to 690 nm, and the

    remaining in the near-infrared (NIR) range, from 700 to 750 nm.

    The optical closure between in situ determinations of R(0,

    ) and the simulated values from forward modelling wasconsidered satisfactory in all stations (Fig. 4, Table 2). In

    particular, the convergence was good in the VIS range (average

    RMSE of the three stations 0.006, relative RMSE 12%) while

    beyond 700 nm a larger divergence was observed (average

    RMSE 0.012, relative RMSE 55%).

    2.3. Hyperion data and pre-processing analyses

    On 22nd June 2003, image data from an area of 7.5 by 42 km

    was acquired by Hyperion with a near-nadir viewing. At the

    time of the overpass Sun zenith and azimuth angles were 32

    and 136, respectively. For this study 28 Hyperion spectralbands ranging from 480 nm to 750 nm were selected to be

    relevant for WQPs estimation and reliable for the sensor

    calibration (Green et al., 2003). Following the approach by

    Brando and Dekker (2003), the image was convolved using a

    Table 1

    List of the bio-optical parameters and in situ water quality data presented in this

    studywith information about the day of acquisition and data source (APPA is the

    Environmental Protection Agency of Trento, ARPAV is the Environmental

    Protection Agency of Veneto)

    Parameter Day of acquisition Source

    aw()

    Smith and Baker (1981),Pope and Fry (1997)

    bbw() Morel (1974), Dall'Olmo

    and Gitelson (2006)

    aph () 10th11th October 2002 HelsinkiUniversity and

    Luode Consulting Oy

    22nd July 2003 APPA

    bbph () 10th11th October 2002 HelsinkiUniversity and

    Luode Consulting Oy

    aTR (440), STR 10th11th October 2002 HelsinkiUniversity and

    Luode Consulting Oy

    22nd July 2003 APPA

    bbTR () 10th11th October 2002 HelsinkiUniversity and

    Luode Consulting Oy

    SCDOM 10th11th October 2002 APPA

    u(), d() HYDROLIGHT 4.2CHL-a, TR 22nd July 2003 ARPAV

    Fluorescence, turbidity 22nd July 2003 CNRIREA

    Fig. 3. SIOPs of Lake Garda: spectra of absorption (upper graph) and

    backscattering (lower graph) coefficients used in the bio-optical model. aw is the

    absorption coefficient of pure water, aph is the chlorophyll-specific absorption

    coefficient of phytoplankton, aTR is the specific absorption coefficient of tripton,

    and aCDOM is the specific absorption coefficient of coloured dissolved organic

    matter. bbw is the backscattering coefficient of pure water, bbph is the

    chlorophyll-specific backscattering coefficient of phytoplankton, and bb

    TR isthe specific backscattering coefficient of tripton.

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    5 5 low pass filter to reduce the environmental noise-

    equivalent reflectance differences NER(0, )E. According

    to Brando and Dekker (2003) and Wettle et al. (2004), the value

    of NER(0, )E is about 0.001 for Hyperion data acquired

    over water. The filtered image was atmospherically corrected to

    R(0, ) using the MODTRAN-based c-WOMBAT-c procedure

    (Brando & Dekker, 2003). The procedure consists of: (i) a three

    step atmospheric inversion from at-sensor-radiance to apparent

    reflectance and (ii) a two step inversion of the airwater

    interface from apparent reflectance to subsurface irradiancereflectance. c-WOMBAT-c was run with actual measurements

    of visibility range (15 km) derived from sun-photometer ob-

    servations performed synchronously to the sensor overpass

    (Vermote et al., 1997). A Q-factor of 4.2 sr1, an air/water

    interface parameter of 0.533, a nadir-viewing geometry and a

    maritime extinction for aerosols were also given as inputs to the

    atmospheric correction code.

    The atmospherically corrected image was geo-located and

    image-derived R(0, ) values were compared to in situ data

    measured during the Hyperion overpass in the pelagic stations

    4, 6 and 7 (Fig. 1). The optical closure (Fig. 4, Table 2) between

    in situ and Hyperion spectra was on average good in the VISrange (from 480 to 690 nm, the average reflectance RMSE of

    the three stations was 0.007 and the relative RMSE 14%) and

    inferior in the NIR bands (beyond 700 nm, the average

    reflectance RMSE was 0.011 and the relative RMSE 77%).

    More in detail, in Station 7 the near-infrared wavelength re-

    flectance values from image data were over-estimated compared

    to in situ values (beyond 700 nm the RMSE was 0.015 and the

    relative RMSE 159%). The most likely cause of such over-

    estimation in the atmospherically corrected image data was the

    contamination of Hyperion radiances by adjacency effects, due

    to multiple reflections of radiation coming from the neighbour-

    ing environment, specifically in the northern part of Lake

    Garda. Here adjacency effects were probably caused by thevegetation growing over the steep sides, laterally delimiting the

    northern narrow part of the lake, where Station 7 is located

    (Fig. 1). The contribution of reflections from the vegetated

    environment on the water surface increases the signal measured

    by the sensor at longer wavelengths (Floricioiu & Rott, 2005)

    and c-WOMBAT-c can fail in removing these quantities.

    2.4. Band selection and model inversion

    A direct inversion of the bio-optical model was applied to

    the Hyperion image using a linear Matrix Inversion Method

    (MIM), as in Brando and Dekker (2003). They ran MIM on a

    Table 2

    List of RMSE and relative RMSE (in %) measuring the optical closure of in situ determinations of R(0, ) vs. forward-modelled and Hyperion-derived R(0, )

    values

    In situ vs. forward-modelled In situ vs. Hyperion-derived

    St. 4 St. 6 St. 7 AvMod St. 4 St. 6 St. 7 AvHyp AvAll

    VIS 0.006 0.007 0.005 0.006 0.005 0.006 0.010 0.007 0.006

    12% 13% 10% 12% 10% 10% 21% 14% 13%

    NIR 0.015 0.020 0.002 0.012 0.009 0.009 0.015 0.011 0.011

    72% 73% 22% 55% 42% 31% 159% 77% 66%

    RMSE and relative RMSE for each station are reported together with their average value (in bold); AvMod is the average for stations 4, 6 and 7 of RMSEs computed

    from in situ and forward-modelled R(0, ) values, AvHyp is the average for stations 4, 6 and 7 of RMSEs computed from in situ and Hyperion-derived R(0, )

    values. AvAll is the average of AvMod and AvHyp RMSEs and measures how R(0

    , ) values from Hyperion, forward bio-optical modelling and in situ optical dataconverge. RMSEs and relative RMSEs are separately computed for VIS (22 bands from 480 to 690 nm) and NIR (6 bands from 700 to 750 nm) ranges.

    Fig. 4. In situ, forward-modelled and Hyperion-derived subsurface irradiance

    reflectance R(0, ) spectra in stations 4, 6 and 7 ( Fig. 1): In situ spectra are

    derived from above-water measurements of Rrs(0+, ); Model spectra are

    calculated from forward bio-optical modelling using CHL-a (in mg m3) and TR

    (in g m3) concentrations measured in situ (shown in each graph) and assuming

    aCDOM(440)=0.09 m1 in all stations; Hyp spectra are computed from

    Hyperion imagery using the c-WOMBAT-c atmospheric correction code. All the

    data were collected on 22nd July 2003. Average reflectance RMSEs (see also

    Table 2) are 0.006 (relative RMSE 13%) between 480 and 690 nm, and 0.011

    (relative RMSE 66%) beyond 700 nm.

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    Hyperion image of a coastal site to retrieve chlorophyll, tripton

    and aCDOM(440) by inverting three bands (490 nm, 670 nm and

    700740 nm). The bands were chosen through some iterations

    starting from wavelengths closest to spectral feature typical for

    each of the WQPs and avoiding the shortest wavelengths (blue)

    where Hyperion was noisy. In this study, the large number

    of Hyperion bands was exploited by performing a sensitivityanalysis of the bio-optical model on aCDOM(440), CHL-a

    and TR independently, based on the first derivative approach

    (Hoogenboom et al., 1998).

    The first derivative spectra relative to variations of aCDOM(440) were obtained from 10 forward runs of the bio-optical

    model changing aCDOM(440) from 0.03 to 0.30 m1 and fixing

    CHL-a and TR to 1 mg m3 and to 1 g m3, respectively. The

    first derivative was rescaled by a 0.01 factor to appreciate the

    sensitivity of the model in discriminating aCDOM(440) at con-

    centration ranges of the lake of around 0.09 m1. The first

    derivative spectra relative to variations of CHL-a were computed

    by 10 forward runs of the bio-optical model incrementing CHL-aby 1 mg m3 withina range between 1 and 1 mg m3, and fixing

    TR to 1 g m3 and aCDOM(440) to 0.09 m1. Similarly, the first

    derivative spectra relative to variations of TR were computed by

    10 forwardruns of the bio-opticalmodel incrementing TR by 1 g

    m3 within a range between 1 and 10 g m3, and fixing CHL-a

    to 1 mg m3 and aCDOM(440) to 0.09 m1.

    Fig. 5 presents the first derivative spectra of R(0, ) vs.

    each of the WQPs. The maximum variation of the first

    derivative of R(0, ) for aCDOM(440) occurred at shortest

    wavelengths (Fig. 5a), in a region where Hyperion data are too

    noisy and ill calibrated (Green et al., 2003). Moving towards the

    region where Hyperion provides calibrated data (i.e.,N

    480 nm),the variation of the first derivative dR(0, ) / daCDOM(440)

    falls within the Hyperion NER(0, )E of 0.001. This implies

    that estimates of aCDOM(440) at the concentration range of the

    lake was not achievable and therefore fixed to the long-term

    average value for Lake Garda (i.e., 0.09 m1). This, a priori

    determination of an unmeasurable signal due to small aCDOM(440) variations illustrates the usefulness of the first derivative

    approach. Based on sensitivity analysis for CHL-a and TR, the

    Hyperion bands at 490 nm and 550 nm were selected for the

    inversion. The 490 nm band was chosen because of the location

    of the maximum variation of the first derivative of R(0, ) for

    CHL-a concentration (Fig. 5b). The 550 nm band was chosen

    because it is the hinge point of the first derivative of R(0, )for CHL-a concentration (Fig. 5b), as well as being in the region

    where the maximum variation of the first derivative of R(0, )

    for TR concentration occurred (Fig. 5c). However, since Hy-

    perion spectra show band to band spikes or dips (Cairns et al.,

    2003), a selection based on single bands could match some

    spikes (Fig. 4). The binning of bands 480500 nm and of bands

    550560 nm were thus used instead of the two single channels,

    centred at 490 nm and 550 nm, respectively.

    To implement the MIM algorithm to retrieve CHL-a and TR

    concentrations, Eq. (1) (in which Eqs. (2) and (3) were

    substituted) was rewritten to a set of 2 equations (for 2 wave-

    lengths), where each equation has the form:

    CHL a aph ki bbph

    ki 11 Pld ki =

    Plu ki

    R 0; ki

    TR aTR ki bbTR

    ki 1

    1 Pld ki =Plu ki

    R 0; ki

    aw ki bbw ki 11 Pld ki =

    Plu ki

    R 0; ki

    aCDOM 440 eSCDOM ki440 7

    Fig. 5. Sensitivity analysis for the band selection of the MIM image inversion.

    (a): first derivative spectra ofR(0, ) vs. aCDOM(440), rescaled by a 0.01 factor

    (aCDOM(440) concentration was changed from 0.03 to 0.30 m1, the other two

    WQPs were kept constant to CHL-a=1 mg m3 and TR=1 g m3); (b): first

    derivative spectra of R(0, ) vs. CHL-a (CHL-a concentration was changed

    from 1 to 10 mg m3, the other two WQPs were kept constant to aCDOM(440)

    =0.09 m1 and TR=1 g m3); (c): first derivate spectra of R(0, ) vs. TR (TR

    concentration was changed from 1 to 10 g m3, the other two WQPs were kept

    constant to aCDOM(440)=0.09 m1 and CHL-a=1 mg m3). The NER(0,

    )E of Hyperion is overlaid on each of the graphs as dot lines. The box indicates

    the wavelength range of Hyperion bands that may be used. Note the different yaxis ranges of each plot.

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    where, R(0, i) is the Hyperion-derived subsurface irradiance

    reflectance, i is the band used in the inversion and aCDOM(440)

    is fixed to 0.09 m1.

    3. Results and discussion

    Fig. 4 and Table 2 show how subsurface irradiance re-flectance values from atmospherically corrected image data, in

    situ optical data and bio-optical modelling converge. A

    reasonable optical closure was achieved in the range from 480

    to 690 nm (average reflectance RMSE 0.006 and relative RMSE

    13%, Table 2). Below 480 nm Hyperion data were not reliable

    for calibration (Green et al., 2003) while beyond 700 nm

    Hyperion reflectances did not seem very well corrected for

    adjacency effects and the optical closure was inferior (average

    reflectance RMSE 0.011 and relative RMSE 66%, Table 2). At

    the two wavelengths (490 and 550 nm) where bands used for

    MIM were located and a good convergence to the same

    subsurface irradiance reflectance was obtained (the reflectancedifferences were on average less than 0.005, or 7% as relative

    value).

    Fig. 6 presents the pseudo true colour Hyperion image and

    the two WQP maps retrieved applying the MIM to the image.

    The pseudo true colour Hyperion image qualitatively describes

    the diversity of waters within the lake. The turquoisecyan

    colours in the southern part of the lake are due to bottom effects

    of bright substrates. Most of the deep waters are dark-blue but

    lighter blue waters come up about the middle of the scene, as

    well as in the south, on the western side of the peninsula. On

    22nd July 2003, re-suspension of sediments was caused by the

    strong wind action resulting in variable patterns of more highly

    scattering waters (i.e., light-blue colours in the pseudo true

    colour Hyperion image). The two MIM-retrieved Hyperion

    WQP maps describe the chlorophyll-a and tripton concentra-

    tions. In these product maps, pixels where bottom depth was

    less than 10 m, were masked because the bio-optical model we

    used (Eq. (1)) is applicable in optically deep waters only. Both

    maps show ranges of CHL-a and TR between 0 and 5 mg m3

    and 0 and 5 g m3, respectively. The patterns in TR map seem

    correlated to the patterns in pseudo true colour Hyperion image:

    TR concentrations are higher in the light-blue waters, whilst

    they are lower in the dark-blue (less-scattering) waters. The fact

    that the CHL-a map is uncorrelated to the TR map indicates

    successful decomposing of the R(0, ) signal and independent

    assessment of CHL-a and TR concentrations following this

    method.

    Validation of CHL-a and TR Hyperion-derived maps was

    performed using in situ point stations. Fig. 7 shows the two

    scatter plots depicting the Hyperion-derived CHL-a and TR

    estimations vs. in situ concentrations measured in 8 pelagicstations (all the stations in Fig. 1, except Station 1 which is

    located in shallow waters where Hyperion data were masked to

    avoid bathymetric effects). Hyperion data was averaged on a 3

    by 3 pixel region of interest centred on the location of in situ

    sampling stations. The Hyperion-derived CHL-a was in good

    agreement with in situ point data, showing a correlation

    coefficient (r) of 0.77, a determination coefficient (R2) of

    0.59, a RMSE of 0.36 mg m3 (relative RMSE 20%), a bias of

    0.12 mg m3 (relative bias 6%), and being close to the 1:1 line

    (Fig. 7). The RMSE and the relative RMSE were computed with

    Eqs. (5) and (6), where Nis now equal to 8 (i.e., the number of

    stations), and Xi and X i are the in situ observed and the

    Fig. 6. The pseudo true colour Hyperion image (with locations of point in situ stations) and the two MIM-retrieved products obtained from Hyperion data acquired on22nd July 2003. In the product maps shallow waters are masked.

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    Hyperion-derived concentrations of WQPs, respectively. The

    Hyperion-derived TR did not match in situ point data (r=0.48,

    R

    2

    =0.23 and RMSE 1.1 g m3

    ), due to divergence betweenHyperion and in situ point data observed in Station 5. Removing

    this measurement from the dataset the regression analysis

    performed better (r=0.75, R2=0.57, RMSE 0.55 g m3,

    relative RMSE 31%, bias 0.27 g m3, and relative bias 15%)

    and data become closer to the 1:1 line (Fig. 7). Station 5 is

    located in the region of the light-blue, more scattering waters.

    Due to the strong wind blowing on the day of the image

    acquisition the light-blue water pattern was characterised by a

    high rate of change (both in time and space). The water at

    Station 5 was probably sampled when these more scattering

    waters (imaged by Hyperion at 9:50 UTC) had already moved

    elsewhere (assuming that the 6 m s1 northsouth direction

    wind resulted in a 0.06 m s1 water current, within 10 min a oneHyperion pixel displacement may occur).

    The ability to monitor water quality in highly dynamic

    systems could be hindered by the spatial or temporal density of

    point sampling offered by traditional sampling techniques

    rendering them inappropriate to validate RS-derived products.

    Lindfors et al. (2005) suggested that validation of remotely

    sensed data products and locations of point measurements

    needed for monitoring work should be based on continuously

    measured flow-through values. They discussed IOPs, salinity

    and temperature in Lake Vnaren (Sweden) and in the Gulf of

    Finland. In this study, to qualitatively evaluate the spatial

    variation of the WQP retrievals, the flow-through calibratedtransects of CHL-a and TR were resampled according to the

    30 m size of Hyperion pixels. Flow-through data were first

    cleaned of anomalies, e.g., spikes due to bubbles, saturation and

    lack of data (caused by wave-related difficulties in pumping

    water from subsurface into the onboard instrumentation). Fig. 8

    illustrates the comparison between two indirect estimates of

    WQPs: the Hyperion-derived concentrations and the flow-

    through-derived transect in situ data. In Fig. 8, the location of

    transect in situ data with respect to the time of the Hyperion

    overpass is indicated to show the temporal mismatch between

    the acquisition of transect in situ data and image data.

    The spatial trend of CHL-a concentrations, derived from

    Hyperion data and from flow-through data is plotted in Fig. 8

    (a). Overall, Hyperion-based estimations are in agreement with

    transect measurements. The first part of the transect (04 km of

    the length) is not shown since it is not included in the imagefootprint. In Section I of the transect (46 km of the length),

    only calibrated transect data are plotted because Hyperion data

    were masked to avoid bathymetric effects. Section II (614 km

    of the length) shows a good agreement in range and spatial

    behaviour between the Hyperion-derived CHL-a and the

    transect fluorescence-derived CHL-a data, even if the peak

    occurrences in concentration are sometimes shifted in phase. In

    Section III (1420 km of the length), when the transect data

    were acquired almost at the same time of Hyperion data,

    imagery-derived CHL-a match flow-through-derived CHL-a

    data. Unfortunately this is the section where many flow-through

    data were missed through filtering of wave-related anomalies.

    In Section IV of the transect (2022 km of the length),Hyperion-derived CHL-a values were lower than flow-through-

    derived concentrations but presented a similar ascending

    gradient. Section V (2232 km of the length), shows a rea-

    sonable agreement in range and spatial behaviour between

    imagery-derived CHL-a and the flow-through-derived CHL-a

    transect data with a descending gradient towards the transect

    end. The last kilometre depicts Hyperion data only because the

    fluorescence measurements were not available.

    Fig. 8(b) describes the spatial variation for tripton. As for the

    chlorophyll-a, the first 4 km of the transect were outside the

    image footprint. In Section I of the transect, only calibrated

    transect data are plotted because Hyperion data were masked toavoid bathymetric effects. In Section II, estimations derived

    from Hyperion and flow-through transect data are comparable

    in values, except for the 3 g m3 peak assessed by Hyperion at

    the 7th kilometre. This peak also exists in the transect flow-

    through data but it is located at the 5th kilometre, in Section I.

    Hyperion-derived concentrations appeared shifted in phase with

    respect to flow-through data. As forFig. 8(a), Section III is the

    region where collections of flow-through data were closer to the

    Hyperion overpass. Within this section, both Hyperion-derived

    TR concentrations and flow-through-derived data show a close

    agreement exhibiting a steep ascending gradient and compara-

    ble concentration ranges. Unfortunately between the 18th and

    the 20th kilometres many flow-through data were missed due to

    Fig. 7. Scatter plots of Hyperion-derived products and in situ concentrations measured in point stations: on left for chlorophyll-a, on right for tripton. Dot lines indicate

    the 1:1 relation. Both graphs do not include data from Stations 1 because it is located in the shallow waters. The statistic in the tripton graph does not include the black

    symbol (i.e., Station 5).

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    wave-related anomalies. In Section IV of the transect, flow-

    through data exhibit a flat trending, contrary to the image data

    that presents a peak with two times higher concentrations. This

    is the region where the transect track crosses the light-blue

    waters. As observed before, when point in situ data at Station 5were compared to image-derived TR estimations (Fig. 7), it

    seems that the light-blue water front, quickly changing its

    position in time and space, was not described by in situ

    observations. A re-suspension of tripton due to the strong wind

    action, which was synoptically imaged at 9:50 UTC by

    Hyperion, had been easily missed by in situ observations

    collected at about 0.30.5 h later. In Section V, both the flow-

    through data and the Hyperion retrieved TR concentrations

    present a descending gradient but with different concentration

    ranges and slopes. Moreover, the peak of 4 g m3 of TR

    observed by Hyperion (at the 25th kilometre), did not occur in

    the transect in situ data. Hyperion synoptically assessed the

    tripton distribution at 9.50 UTC: higher concentrations in the

    first part of Section V, lower concentrations in the last part of

    Section V. The flow-through system gave the TR concentrations

    (actually geo-coded to Hyperion pixels) 11.5 h later with

    respect to Hyperion, when the front had probably moved

    elsewhere and the distribution of tripton was changed. Ingeneral, Fig. 8(b) shows that Hyperion-derived tripton con-

    centrations were not comparable to flow-through-derived

    values, expect for few kilometres in Section III (from 15th to

    19th kilometre) where Hyperion and transect acquisitions match

    in time. These results suggest that Hyperion-derived tripton

    concentrations, in occasion of events subjected to local vari-

    ability in wind, re-suspension and circulation, are difficult to

    compare to in situ data due to the incompatibilities of methods

    used for tripton assessments. Even fast monitoring methods like

    flow-through measurements are time consuming (3 h for a 32-

    km-long transect) and they could become inappropriate to

    describe natural events with a high rate of change as may occur

    in wind driven currents in lakes.

    Fig. 8. Comparison of Hyperion-derived products and in situ concentrations estimated from the flow-through data along the horizontal transect: (a) chlorophyll-a, (b)

    tripton. Hyperion and flow-through transect data are extracted from a 30 m per 30 m pixel grid (see text for labels I to IV). The approximate location of transect in situ

    data with respect to the time of Hyperion overpass is also indicated.

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    4. Conclusions

    This work presents a procedure to map CHL-a and TR

    concentrations in Lake Garda from hyperspectral satellite data

    based on forward and inverse bio-optical modelling. The per-

    formance of the analytical inversion approach was measured by

    the optical closure between forward-modelled reflectance, insitu reflectance and atmospherically corrected Hyperion

    reflectance. The closure was sufficient for the purposes of this

    study but further investigations on atmospheric adjacency ef-

    fects, focused on surface reflected vegetation spectra from steep

    slopes, are recommended to obtain a better closure at longer

    wavelengths. The bio-optical model sensitivity analysis indi-

    cated the optimal bands to run the inversion as well as the

    inability to detect aCDOM(440) in this study. The matrix in-

    version method was applied to run the inversion on the spatially

    and spectrally convolved Hyperion image. The MIM algorithm

    provided ranges of CHL-a concentrations comparable to in situ

    data collected the day of the satellite overpass. Results fortripton were less satisfactory but an improvement was found if

    data from a re-suspension zone were avoided. A further eval-

    uation of image-products was based on high spatial resolution

    transect in situ data: about 32 km (some transect in situ data

    were missed because of the wave-related anomalies) of flow-

    through-derived measurements of CHL-a and TR were

    qualitatively compared to concentrations retrieved from Hyper-

    ion. For chlorophyll-a the Hyperion-derived concentrations

    were on average comparable to transect in situ data. The com-

    parison was more difficult for tripton since some incompatibil-

    ities of methods happened. On the day of the Hyperion overpass

    a strong wind occurred over the lake resulting in re-suspension

    of sediment (tripton). Further investigations are therefore nec-essary, mainly addressing the compatibilities of methods for

    monitoring water body features with high rate of wind or wave

    driven change. Matthews et al. (2001) already observed how

    continuous fluorometers towed behind boats may offer an

    increased capability to monitor chlorophyll-a with respect to

    traditional sampling technique in highly dynamic coastal zone

    but, the linear track estimates, may be themselves inadequate to

    describe the wide-scale heterogeneous phenomenon as synop-

    tically retrieved by RS. The results also indicate that fast

    processing of hyperspectral images is feasible: once the pre-

    processing was done the Hyperion image processing took only

    180 s on a standard desktop PC. Another advantage of themethod is that each good set of in situ AOPs, IOPs and SIOPs

    measurements added to the spectral library of the lake will

    improve the algorithm performance (thus at a certain moment

    no further in situ measurements will be required as all source

    materials, e.g., inflowing waters, re-suspended material and

    algal populations are characterised properly). In such a context,

    next studies could also benefit from more information about

    variability in the SIOPs over time and space. The presented

    procedure is also transferable to other lakes, for which the

    optical characterisation of the water body is known and in-

    formation about atmospheric properties during the satellite

    overpass is accessible. In particular, accurate visibility ranges

    are required since Keller (2001) observed how incorrect values

    may produce large errors in obtaining R(0, ) and conse-

    quently in retrieving concentrations of water quality parameters.

    This study aimed to use Hyperion imagery as a bench-mark

    for moving towards operational use of RS-related technologies

    that, integrated with traditional survey programmes, could

    provide useful information to implement the European WFD.

    Within the WFD it is possible, for each water body, to monitoronly the water quality elements most sensitive to a certain risk or

    pressure. For Lake Garda this could be the deviation from a

    trophic level assessed with two causal elements (i.e., phospho-

    rous and nitrogen) and with one response parameter, the

    chlorophyll-a concentration. The Hyperion data processing

    presented in this study will be transferred to the assessment of

    lake water quality (mainly chlorophyll-a) using more operational

    instruments (being a part of a technology validation/demonstra-

    tion mission, Hyperion cannot be considered suitable for a long-

    term monitoring). Large swath MODIS and MERIS sensors

    (both having the spectral bands used by MIM) offer almost-daily

    imagery of northern Italy and the method presented could beextended to the other large (relative to the spatial resolution of

    the remote scanner that is) lakes of the subalpine region, where

    visibilities ranges are provided by airports or Aeronet stations.

    An onerous activity needs however to be completed mainly to

    asses the lakes SIOPs or to evaluate how they differ from the

    Lake Garda ones. To start, Premazzi et al. (2003) discussed that

    the in the subalpine region composition of the phytoplankton

    communities would register marked similarities from one lake to

    another, as regards density, biomass and species.

    Acknowledgements

    This work is in memory of Eugenio Zilioli who passed awayin 2004 and who made a considerable effort in Europe to

    establish remote sensing of lakes as a tool for environmental

    monitoring. Hyperion data were acquired by the Helge Axson

    Johnsons Foundation, Sweden. IOP data were collected by A.

    Lindfors and K. Rasmus at the Dep. of Geophysics, Helsinki

    University and Luode Consulting Oy, Helsinki. This study was

    funded by the Italian Space Agency (Ninfa Project), and by

    ESA and Regione Lombardia with financial support grants to N.

    Strmbeck and to G. Candiani, respectively. The CNR/CSIRO

    Agreement (200406 Program) and the Scientific Office at the

    Embassy of Italy in Canberra supported the collaboration

    among our institutes. This work would not be possible withoutthe assistance and contributions provided during these years by

    L. Alberotanza from CNR-ISMAR, G. Zibordi from JRC, and

    by C. Defrancesco and G. Franzini from the Environmental

    Protection Agencies of Trento and Veneto, respectively. We are

    grateful to T. Kutser from Estonian Marine Institute for his

    valuable suggestions. Constructive comments from the anon-

    ymous reviewers, including ones on an earlier version of the

    manuscript, were greatly appreciated.

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