Assessment of GOCI Radiometric Products using MERIS, MODIS ...omtab.obs-vlfr.fr/fichiers_PDF/Lamquin_et_al_OSJ_`12.pdf · blue-green wavelengths (Antoine and Morel 1999). Water-leaving

Ocean Sci. J. (2012) 47(3):00-00http://dx.doi.org/10.1007/s12601-

Available online at www.springerlink.com

Assessment of GOCI Radiometric Products using MERIS, MODIS and Field Measurements

Nicolas Lamquin1*, Constant Mazeran

1, David Doxaran

2, Joo-Hyung Ryu

3, and Young-Je Park

3

1ACRI-ST, 260 route du Pin Montard, 06904 Sophia Antipolis, France2Laboratoire d’Océanographie de Villefranche, Université Pierre et Marie Curie, CNRS, UMR 7093, 06230 Villefranche-sur-Mer,

France3Korea Ocean Satellite Center, KIOST, Ansan P.O. Box 29, Seoul 425-600, Korea

Received 2012; Revised 2012; Accepted 2012

© KSO, KIOST and Springer 2012

Abstract− The first Geostationary Ocean Color Imager (GOCI)

launched by South Korea in June 2010 constitutes a major

breakthrough in marine optics remote-sensing for its capabilities to

observe the diurnal cycles of the ocean. The light signal

recorded at eight wavelengths by the sensor allows, after

correction for Solar illumination and atmospheric effects, the

retrieval of coloured biogeochemical products such as the

chlorophyll, suspended sediment and coloured dissolved

organic matter concentrations every hour between 9:00 am and

4:00 pm local time around the Korean peninsula. However

operational exploitation of the mission needs beforehand a

sound validation of first the radiometric calibration, i.e. inspection

of the top-of-atmosphere reflectance, and second atmospheric

corrections for retrieval of the water-leaving reflectance at sea

surface. This study constitutes a contribution to the quality

assessment of the GOCI radiometric products generated by the

Korea Ocean Satellite Center (KOSC) through comparison with

concurrent data from the MODerate-resolution Imaging

Spectroradiometer (MODIS, NASA) and MEdium Resolution

Imaging Spectrometer (MERIS, ESA) sensors as well as in situ

measurements. These comparisons are made with spatially and

temporally collocated data. We focus on Rayleigh-corrected

reflectance (ρRC) and normalized remote-sensing marine reflectance

(nRrs). Although GOCI compares reasonably well with MERIS

and MODIS, what demonstrates the success of Ocean Colour in

geostationary orbit, we show that the current GOCI atmospheric

correction systematically masks out data over very turbid waters

and needs further examination and correction for future release

of the GOCI products.

Key words −GOCI, MERIS, MODIS, remote sensing reflectance,atmospheric correction

1. Introduction

Remote-sensing of ocean colour radiometry has proven

in the last decades to be an unequalled technique for

providing a synoptic view of the ocean biology at daily to

yearly scale (IOCCG 2008). While major technological

evolutions in the past have consisted in either spatial or

spectral resolution enhancements of instruments carried on

polar orbiting platforms (e.g. the MEdium Resolution Imaging

Spectrometer (MERIS) with 300 m ground sampling resolution

and fifteen wavebands (Rast et al. 1999), or the MODerate

Resolution Imaging Spectroradiometer (MODIS) with short-

wave infrared wavelength (Barnes et al. 1998)), a new

breakthrough has been achieved in 2010 with the launch of the

first geostationary ocean colour mission, GOCI (Geostationary

Ocean Color Imager) onboard the COMS (Communication,

Ocean, and Meteorological Satellite) platform (Faure et al.

2007). Although limited to a field-of-view (FOV) of 2500×

2500 km2 centered around the Korean Peninsula, its temporal

acquisition frequency of one hour paves the way for

studying the diurnal scale of biogeochemical phenomena

(Ryu 2011, IOCCG 2012).

Prior to the successful exploitation of remote-sensing

biophysical quantities for such applications, great attention

must be paid to the quality of the radiometric marine signal,

the latter being the primary product of ocean colour and the

input of all bio-optical inversion algorithms. For instance,

the standard requirement for distinguishing 30 classes of

Chlorophyll-a concentration in the range of 0.3-30 mg/m3*Corresponding author. E-mail: [email protected]

Article

2 Lamquin, N. et al.

using a band ratio approach leads to a requirement of about

5% relative accuracy of the water-leaving reflectance in the

blue-green wavelengths (Antoine and Morel 1999). Water-

leaving reflectances are obtained from the top-of-atmosphere

(TOA) signal after the removal of the atmospheric contribution

(absorption and scattering by the atmospheric gas, air

molecules and aerosols), a step referred to as atmospheric

correction (e.g. Gordon and Wang 1994). This correction is

a major procedure of ocean colour retrieval, whose main

difficulty resides in an accurate estimation of the aerosol

content and type, variable in shapes and sizes. It has been

subject to many studies and a wide variety of methods are

available in the ocean colour remote-sensing community

for existing sensors (IOCCG 2010). Of course, a proper

calibration of the Level 1 TOA radiance itself is a key issue

for getting accurate marine signals. In general, a vicarious

calibration (i.e. calibration of the TOA signal through

ground-truth measurements) of the whole system comprised

by the sensor and the atmospheric correction algorithmic

chains is applied to achieve the marine signal accuracy in the

specifications mentioned above (see e.g. Franz et al. 2007

for SeaWiFS or MODIS, Lerebourg et al. 2011 for MERIS).

In this study the main objective is to assess the accuracy

of GOCI radiometric products based on independent datasets

from existing ocean colour sensors and in situ oceanographic

campaigns providing concomitant observations. Considering

the first ever set of GOCI data publically distributed, this

study must be understood as a preliminary assessment of the

geostationary sensor, through case studies, to show its overall

capabilities, limits and potential sources of improvement.

The reference ocean colour sensors considered in the

study are the MERIS and MODIS instruments, still in-flight

for comparison with GOCI, and which have undergone several

reprocessings, sound analyses and calibration updates since

their launch in 2002 (see e.g. Franz et al. 2005 for MODIS,

Antoine et al. 2008, or Lerebourg and Bruniquel 2011 for

the MERIS 3rd reprocessing) and have proven to contribute

to many scientific investigations in ocean colour remote-

sensing in particular for long-trends analysis (e.g. Morel et

al. 2007).

These instruments, onboard polar orbiters, provide a

different geometry of observation: while GOCI has satellite

zenith angles between 26 and 55° (Ahn 2012), MODIS

(resp. MERIS) has viewing zenith angles typically varying

from 0° (nadir viewing) up to 55° (resp. 40°) at the edge of

the swath. They are designed to cover the whole globe on a

nearly daily-basis and their FOVs consequently cross the

GOCI FOV nearly every day. The full GOCI FOV cannot

be covered during one single overpass of any of these

instruments because of the size of their swaths (1150 km for

MERIS, 2330 km for MODIS) and the changing location of

their orbiting track. Nevertheless, these configurations provide

spatially and temporally coincident measurements over

portions of the GOCI FOV large enough to allow inter-

comparisons over specific regions (e.g. East China Sea, Yellow

Sea, Bohai Sea, East Sea/Sea of Japan) representative of

various oceanic water types. The East China and Yellow

Seas are typical of turbid to highly turbid coastal waters

characterized by a large and shallow (<50 m depth)

continental platform directly influenced by the discharge of

two major world rivers, namely the Yangtze and Yellow

Rivers, and human pressure (agriculture, industrialization).

The corresponding river deltas are representative of highly

turbid coastal waters characterized by several maximum

turbidity zones and turbid plumes which may extend up to

hundreds of kilometers offshore (e.g, Tang et al. 1998, Shen

et al. 2010). These river plumes have a strong impact on

primary production over the adjacent coastal waters, up to

several hundreds of kilometers offshore (Tang et al. 1998).

Specific objectives of the present study are to assess the

capability of the GOCI sensor over turbid waters, known as

representing a great challenge for the ocean colour community

because of the coupling between the atmospheric and

residual marine signal in the near-infrared bands (see e.g.

Moore et al. 1999, Stumpf et al. 2003, Ruddick et al. 2006,

Wang and Shi 2007). We first compare the multi-spectral

seawater reflectance signal (namely the normalized remote

sensing reflectance) retrieved from GOCI, MERIS and

MODIS data. To explain some of the differences observed

due to correction of TOA data for the aerosol contribution, we

then compare the Rayleigh-corrected reflectance measurements

recorded by the three sensors. The considered radiometric

products are therefore:

• The normalized remote-sensing reflectance above surface

(nRrs in sr-1), obtained after atmospheric correction of

the TOA signal normalized to the solar downwelling

irradiance and corrected for bidirectional effects at sea

surface (Morel and Gentili 1991, 1993, 1996). This quantity

is the input of most bio-optical inverse algorithms. As we

shall see, its retrieval by the standard GOCI algorithm is

only partial over highly turbid regions;

Assessment of GOCI Radiometric Products using MERIS, MODIS and Field Measurements 3

• The TOA reflectance corrected for gaseous absorption,

Sun glitter and Rayleigh scattering (ρRC, dimensionless),

which is independent of the atmospheric correction,

hence provided on any water pixel. In the absence of

Sun glint, which is the case for most of the geostationary

geometries (see IOCCG 2012), and because the Rayleigh

scattering is generally well controlled, this quantity gives

clues on the quality of TOA calibration of the sensor.

Along with further presentations of the sensors and the

datasets used in this work, section 2 of the paper presents the

collocation scheme, case studies and comparison methodologies.

Results obtained by multi-sensor inter-comparisons are

presented in section 3 considering successively the nRrs

and ρRC radiometric signals. Comparisons with in situ data

end this part and open a discussion on results presented in

section 4. We conclude on the benefits of multi-sensor and

in situ comparisons and the need of further examinations of

the standard atmospheric correction of GOCI data over

highly turbid waters.

2. Materials and Methods

In this section we first present data from each instrument.

The GOCI data is presented first as it is used as a reference

(time and geolocation) to extract the other datasets. The

constraints and methodologies of the inter-comparisons are

then addressed and we justify the selection of a set of

concomitant MERIS and MODIS observations that are

relatively free of clouds over the regions of interest. At last

we present the in situ data.

GOCI data

GOCI Level 1B (L1B) data along with the GOCI Data

Processing System (GDPS) software are available for

downloading from the Korea Ocean Satellite Center (KOSC)

website (http://kosc.kordi.re.kr/index.kosc). Available data

cover the period from April 1st 2011 up to present as it is

continuously filled by new incoming observations. Although

GOCI acquisitions are made at each hour between 9:00 am

and 4:00 pm local time (Cho et al. 2010), only data at 2:16,

3:16 and 4:16 UTC (11:16 am to 1:16 pm local time) are

currently distributed to the scientific community. Fig. 1

shows an example of a GOCI acquisition (composite image

processed by KOSC) on October 4th 2011 at 2:16 UTC.

Because MERIS and MODIS acquisitions spatially collocated

in the GOCI FOV may not temporally coincide with the same

acquisition, all three L1B acquisitions (per selected date of

observation) are downloaded from the GOCI database.

The GOCI FOV covers a large area centered around the

Korean Peninsula (Fig. 1), fixed because of the geostationary

geometry of observation and delimited by latitudes 20° to

50° North and longitudes 115° and 145° East.

We use the GDPS to produce Level 2 (L2) data from the

downloaded L1B data, and the atmospheric correction

scheme used is the standard KOSC algorithm (Ahn et al.

2012). Among other quantities the GDPS notably retrieves

gridded products of fully normalized remote sensing

reflectances at GOCI wavelengths: 412, 443, 490, 555, 660,

680, 745, and 865 nm.

The KOSC atmospheric correction code has been developed

based on the SeaWiFS algorithm (Gordon and Wang 1994).

Prior to aerosol correction, the water reflectance at near

infrared bands is iteratively estimated with concentrations of

chlorophyll a, Coloured Dissolved Organic Matter (CDOM)

and suspended sediment using a bio-optical model. Also

minor differences are found in the Rayleigh reflectance

computation, the candidate aerosol models, the atmospheric

transmittance computation and so on. It is also noted that

current GOCI cloud masking is stricter than MODIS and

MERIS as discussed later. It is worth noting that the

Fig. 1. Composite image of the GOCI acquisition on 4th October2011, 2:16 UTC. Source: KOSC


standard current GOCI chain does not include a vicarious

calibration.

In addition, KOSC provided TOA Rayleigh-corrected

reflectances at the selected GOCI dates and times of

acquisition. The Rayleigh corrected reflectance is defined by

ρRC≡(ρt−ρr*tg)/tg, where ρt is the satellite-measured reflectance,

ρr is the pressure-corrected Rayleigh reflectance and tg is the

gaseous transmittance due to ozone and oxygen. However, the

Rayleigh reflectance ρr is only path reflectance which

excludes the surface (mainly sky glint) reflectance. Therefore,

the Rayleigh corrected reflectance for GOCI data includes

reflectance due to aerosols, water body and sky glint. The

inclusion of sky glint in ρRC is a major difference from

MODIS or MERIS.

MERIS data

In this study we have considered the MERIS 3rd reprocessing

data in Reduced Resolution provided by the European

Space Agency. Focusing on the water-leaving reflectance

product, this reprocessing includes notable improvements

with respect to the previous one, including better cloud/

haze detection, more accurate atmospheric transmittances,

more robust atmospheric pre-correction over turbid water

(Lerebourg and Bruniquel 2011). Level 1 (L1) data have

been downloaded through the ODESA online facility (http:/

/earth.eo.esa.int/odesa) and processed to L2 with the

ODESA software (ibid.) including the 3rd reprocessing

processor (MEGS.8) to get both the normalized remote-

sensing reflectance (nRrs) and Rayleigh corrected reflectance.

Practically, nRrs is computed in ODESA from the MERIS

marine reflectance (available at Level2), corrected for

bidirectional effects and divided by the π factor. We recall

here that MERIS atmospheric correction is based on two

sequential algorithms: first the Bright Pixel Atmospheric

Correction (BPAC, Moore and Lavender 2011), aiming at

removing any residual marine signal in the near-infrared

(NIR) bands, then the Clear water Atmospheric Correction

(Antoine and Morel 1999) which determines the aerosol

contents and type and yields to the water-leaving reflectance in

the full spectrum. Also, this reprocessing includes a vicarious

calibration, mainly justified and satisfying over clear waters

(Lerebourg et al. 2011); potential failure over coastal turbid

water has also lead us to deactivate this calibration in some

of the results presented hereafter.

The Rayleigh corrected reflectance, ρRC, comes from the

TOA reflectance corrected for the so-called smile effect

(spectral correction at CCD detector level, Bourg et al. 2008),

gaseous absorption, Sun glint and Rayleigh scattering, the

latter being computed at pixel actual pressure (see MERIS

Detailed Processing Model 2011).

Eventually, in the data analysis, we have discarded pixels

where high glint, ice haze, or dust aerosol flags are raised.

MODIS data

While two MODIS sensors are currently operational

onboard the Terra and Aqua satellite platforms, only MODIS-

Aqua data were considered in this study. MODIS-Aqua

L1A data from the R2010.0 reprocessing and corresponding

geolocation files were downloaded from the NASA Ocean

Color website (http://oceancolor.gsfc.nasa.gov) and processed

using the SeaDAS 6.2 software (http://seadas.gsfc.nasa.

gov/) to generate L1B geolocated then L2 products. The

processing includes a vicarious calibration (Franz et al. 2007).

L2 products included (i) TOA reflectance (ρt, dimensionless),

(ii) Rayleigh-corrected reflectance (ρRC, dimensionless),

and (iii) nRrs (sr-1). It is worth noting that Meister et al.

(2012) showed that the quality of the MODIS ocean colour

products have been degraded significantly in the recent

years, especially at 412 nm and, to a lower extent, at 443 nm,

which is due to unpredictable changes of the radiometric

calibration and limitations to adequately track and correct

those changes. Therefore, MODIS 412 and 443 nm data

should be considered with great care.

The ρRC product was implemented into SeaDAS by Ruddick

et al. 2000 (note that this quantity is noted rhom in the SeaDAS

code). It corrects for various gaseous transmittances, glint,

whitecaps and Rayleigh reflectance, so it is a quasi-surface

reflectance. The computation can be written as:

ρRC=π/F0/µ0*(Lt/tg_sol/tg_sen/polarization_factor - tLf

−Lr)/t_o2/t_h2o–tLg (1)

with Lt and Lr the measured and Rayleigh radiance signals,

respectively, and where the t's are the various transmittances, µ0

is cosine solar zenith, F0 is the extraterrestrial irradiance; the

computation therefore accounts for the polarization, whitecaps

(Lf) and glint (Lg) effects.

Note that the whitecaps correction, not included in

MERIS, should have a very small impact because most of

the scenes studied here have wind speed lower than 6 m/s

(max. 8 m/s).

Two algorithms are applied to retrieve the nRrs product

at 1 km resolution. The first algorithm is the standard one


developed for open ocean waters and available as default on

SeaDAS which computes the above-mentioned radiometric

products only at the following visible spectral bands: 412,

443, 488, 551, 667 and 678 nm. A second algorithm specifically

designed for turbid coastal waters uses the Rayleigh-corrected

reflectance recorded at wavebands in the shortwave-infrared

(SWIR) to determine the aerosol contribution (Wang and Shi

2007). This algorithm computes the nRrs product at the same

visible bands and also at 555, 645, 748 and 859 nm.

In both cases, the cloud detection and flag threshold for

the 412 nm surface reflectance was set after few trials to

0.27. We have checked indeed that the default value (0.027)

filters out turbid waters, while it is possible to increase it as

only a selection of almost cloud-free MODIS data was

considered here. In order to develop an operational algorithm

for routine application on MODIS data independently of the

cloud cover, the solution to discriminate clouds from turbid

waters would certainly be to take into consideration the

spectral variations of the reflectance signal corrected for

atmospheric effects (Nordkvist et al. 2009).

Wavelength matching and regridding procedure

Although GOCI, MERIS and MODIS have different

spectral band sets, it is possible to select comparable

wavelengths (Table 1). Concerning visible bands, the main

shift is at 667 nm for MODIS (+7 nm compared to GOCI)

and at 560 nm for MERIS (+5 nm compared to GOCI). No

spectral correction has been applied here to correct for this

band-shift as it would be minor compared to the differences

observed (e.g. very few percents between 555 and 560 nm

according to Zibordi et al. 2009). Hereafter, for the sake of

simplicity, we use in the text the wavelengths of GOCI for

all sensors.

Since the MODIS and MERIS tracks never strictly overpass

the same location and because of their spatial resolutions

(which differ from the GOCI resolution) their footprints do

not coincide precisely with the GOCI grid. For this reason,

each GOCI, MERIS and MODIS observations are projected

on a regular grid between latitudes 20° to 50° North and

longitudes 110° and 150° East with a step of 0.01° in both

directions. This step is comparable to MERIS and MODIS

reduced resolution (1 km) and is lower than GOCI resolution

(500 m). This projection simply consists in averaging the

data within each pixel of the regular grid. We have checked

that a projection at 0.005° (closer to the GOCI native resolution)

does not change our conclusions, either qualitatively or

quantitatively.

Case studies

The GOCI acquisition on October 4th 2011 2:16 UTC

(Fig. 1) shows only few clouds covering the Yellow Sea, a

region of specific interest for its high turbidity near the coasts.

However the Korean Peninsula is frequently covered with

clouds (which obstruct clear sky conditions for most of the

summer) and only few acquisitions provide clear pixels

over large portions of the GOCI FOV. This is an unfortunate

and serious limitation to obtain a wide range of concomitant

acquisitions from all seasons and various states of the

atmosphere. Only few days in 2011 provide very clear sky

situations; Table 2 summarizes the list of the GOCI days of

acquisition that have been selected both for the relative lack

of contamination by clouds and the possibility of having

concomitant MERIS and MODIS measurements overlapping

well the GOCI FOV. It also shows MERIS and MODIS

acquisition times in UTC, which shows that MERIS data

(resp. MODIS data) must preferably be compared to GOCI

2:16 data (resp. 4:16 data).

In order to conduct a quantitative analysis, we also consider

Table 1. GOCI spectral bands and corresponding wavebands for MERIS and MODIS

GOCI MERIS MODIS

Band # λ (nm) ∆λ (nm) Band # λ (nm) ∆λ (nm) Band # λ (nm) ∆λ (nm)

B1 412 20 B1 412.5 10 B8 412 15

B2 443 20 B2 442.5 10 B9 443 10

B3 490 20 B3 490 10 B10 488 10

B4 555 20 B5 560 10 B11 555 20

B5 660 20 B7 665 10 B12 667 10

B6 680 10 B8 681.25 7.5 B13 678 10

B7 745 20 - - - B14 748 10

B8 865 40 B13 865 20 B15 859 35


two transects for data extraction. The first West-East transect

starts from the highly turbid waters of Yangtze River delta

and extends East towards the East Sea/Sea of Japan clear

waters), crossing the South border of Korea where different

water types mix. A second North-South transect is selected

perpendicular to the first one and goes through mostly (but

not exclusively) clear waters of the Yellow Sea. These two

transects will be illustrated later in the discussion.

In situ data

In situ Rrs measurements were carried out during two

consecutive oceanographic campaigns organized by the

KORDI research institute between September 18th and October

2nd 2011. A total of 26 stations were sampled between the

coastal waters of South Korea and the middle of the East

China Sea (Ieodo station), corresponding to water depths

varying from 25 to 150 m.

During daytime (18 stations), the water apparent optical

properties were measured from the deck using a Compact-

Optical Profiling System (C-OPS) built by Biospherical

Instruments Inc. (San Diego, California). The C-OPS system

was equipped with an above-water global solar irradiance

sensor (Ed) and two in-water downward irradiance and

upwelling radiance (Ed and Lu, respectively) sensors. The

three radiometers were equipped with the following common

19 optical wavebands: 340, 412, 443, 465, 490, 510, 532,

555, 560, 589, 625, 665, 670, 683, 694, 710, 765, 780 and

875 nm. At each station, the C-OPS data were recorded

during at least three consecutive downcasts between the sea

surface and a variable water depth corresponding to the 1%

level of the Photosynthetically Available Radiation (Biospherical

Instruments 2009).

The Rrs signal is defined as the ratio between the water-

leaving radiance, Lw in W m-2 sr-1 nm-1, and the global solar

irradiance signal, Ed in W m-2 nm-1, both just above the sea

surface at z=0+ (Mobley 1994). At each station, the Lw and

Ed signals were respectively derived and measured using

the C-OPS system. It is based on a cluster of 19 state-of-the-

art microradiometers spanning 340–875 nm and a new latter

design (Morrow et al. 2010). The latter includes tuneable

ballast and buoyancy, plus pitch and roll adjustments.

During three consecutive C-OPS downcasts, the upwelling

radiance was measured as function of water depth and

extrapolated up to the water surface (at null depth z=0-) over

a near-surface interval with homogenous water properties

(verified with temperature and attenuation parameters). The

water-leaving radiance signal, Lw, was obtained from

Lu(0-) as:

Lw(λ, 0+) = 0.54 Lu(λ, 0-), (2)

where the constant 0.54 accurately accounts for the partial

reflection and transmission of the upwelled radiance

through the sea surface, as confirmed by Mobley (1999).

The recorded Lu(z) data were filtered within +/-5° around

the zenith angle prior to extrapolation to null water depth

and Lw computation. A verification of the extrapolation

process used to determine Lu(λ, 0-) was provided by

comparing the in-water determination of Ed(λ, 0-) to the

above-water solar irradiance signal using:

Ed(λ, 0-) = 0.97 Ed(λ, 0+), (3)

where the constant 0.97 represents the applicable air-sea

transmittance, Fresnel reflectances, and the irradiance reflectance,

and is determined with accuracy better than 1% for solar

elevations above 30° and low-to-moderate wind speeds.

The appropriateness of the extrapolation interval was

evaluated by determining if (3) was satisfied to within

approximately the uncertainty of the calibrations (a few

percent); if not, the extrapolation interval was redetermined

(while keeping the selected depths within a homogeneous

layer) until the disagreement was appropriately minimized

(usually to within 5%).

Table 2. Day and time (UTC) of GOCI, MERIS and MODIS concomitant acquisitions

Day GOCI MERIS MODIS Aqua Day GOCI MERIS MODIS Aqua

2011-04-05 02:16, 03:16, 04:16 - 05:35 2011-09-23 02:16,03:16,04:16 01:51 05:15

2011-04-11 02:16, 03:16, 04:16 02:36 05:00 2011-10-04 02:16, 03:16, 04:16 01:49 05:00

2011-04-12 02:16, 03:16, 04:16 - 05:40 2011-10-08 02:16, 03:16, 04:16 02:43 04:35

2011-09-04 02:16, 03:16, 04:16 01:45 04:45 2011-10-17 02:16, 03:16, 04:16 02:14 04:30

2011-09-06 02:16, 03:16, 04:16 02:12 04:35 2011-11-14 02:16, 03:16, 04:16 01:50 04:50

2011-09-07 02:16, 03:16, 04:16 01:35 05:15 2011-11-26 02:16, 03:16, 04:16 01:48 05:15


Based on the low to negligible Rrs values measured in the

near-infrared part of the spectrum (λ > 750 nm, see Fig. 2),

the field Rrs spectra proved to be only representative of the

clear to moderately turbid water masses identified on

satellite data, i.e. not of the most turbid waters found in the

East China Sea.

Using the geometry of observation, Rrs values are normalized

into nRrs, to be compared to remote-sensing data.

3. Results

We present comparisons of normalized remote sensing

reflectances (first) and TOA Rayleigh corrected reflectances

(second) between GOCI and the concomitant MERIS and

MODIS data summarized in Table 2. The analysis shows

global maps of reflectances on October 4th 2011 at selected

wavelengths over the GOCI area. Then, more quantitative

views of the discrepancies between sensors are given over

the two W-E and N-S transects. These transect comparisons

are made on several other dates of acquisition to show

behaviors eventually departing from or confirming what is

observed on October 4th.

For the normalized remote-sensing reflectances the influence

of the temporal variability of the GOCI reflectances as well

as the variability induced by the vicarious adjustments of

MERIS and the use of the SWIR bands in the MODIS

retrievals is discussed. Last, gathering all concomitant data

from all selected dates of acquisition into scatter plots

provides a complete quantitative comparison.

Inter-comparisons of the normalized remote-sensing

reflectances

Comparisons between nRrs products from MERIS and

GOCI (Fig. 3) then MODIS and GOCI (Fig. 4) on October

4th 2011 are presented as maps at selected wavelengths: 412,

490, 555 and 660 nm or equivalent. Order of appearance in

Fig. 3 and Fig. 4 reflects the chronological acquisition time

(from left to right).

As time passes by, we notice for example that the horizontal

cloudy sheet (partly composed of relatively thin clouds as

seen in Fig. 1) at about 39° latitude between Korea and

Japan moved eastwards, which is clear from the two GOCI

snapshots and the MODIS snapshot. On contrary MERIS

marine reflectance could be partially retrieved in that area,

although its acquisition is close in time with GOCI first

image (1:49 and 2:16 UTC respectively). This is most

probably because the MERIS Level 2 processing does not

properly detect these optically thin clouds (yet it has been

improved from 2nd to 3rd reprocessing, see Lerebourg and

Bruniquel 2011), as can be seen on the overestimated nRrs

at 665 nm, but as well because the cloud coverage is

extending quickly with time along the day (checked with a

MODIS/Terra image acquired at 1:40 UTC, not shown here).

Also, a flagging of data because of high glint induces blank

areas in the MERIS retrievals on the Eastern part of the

swath, that does not appear in the GOCI and MODIS

retrievals whose geometries of observation do not trigger

high glint conditions in this series of acquisitions.

A first striking observation is the absence of GOCI data,

and in a lesser extent of MODIS data, in the highly turbid

area of the Yangtze delta, while MERIS provides successful

retrievals very near to the shore. The difference between the

MERIS and GOCI acquisition times is too small to explain

this by a change in the cloud amount (what is confirmed by

a visual inspection of the corresponding composite L1

images). It seems that current GOCI atmospheric correction

masks out highly turbid pixels. Comparatively, the less

turbid zone in the West of the South Korean coast, free of

clouds, shows that both GOCI and MERIS retrievals are

successful except in a tiny area witnessing a high load of

sediments where GOCI shows no data again. It is

recommended that the cloud detection threshold be higher

than current threshold to process pixels over turbid waters.

Note also the systematic retrieval of maximum water

reflectance values north of the Yangtze River mouth in the

Yellow Sea. These high values result from the permanent

Fig. 2. Rrs in situ (C-OPS, 18 stations) from the oceanographicfield campaign


presence of a well-known maximum turbidity zone

(Beardsley et al. 1985). The origin of suspended particles in

this shallow coastal zone (5-10 m depth on average) is

twofold: (i) direct export of suspended particles from the

Yangtze River and (ii) accumulation and resuspension of

sediments from the Yellow and East China Seas due to the

regional circulation of water masses (main reason explaining

the persistence of the maximum turbidity zone).

Another feature detected on both MERIS and MODIS

nRrs(660) products is a thick ribbon of high values along

the coasts of Korea and Russia, much thicker than in the

GOCI retrievals. This is very likely due to adjacency effects

(reflection by the water body of light coming from the coast

and into the direction of the sensor, as well as altered diffuse

scattering from surroundings), triggered by the forward

scattering geometry on the half-East part of the MERIS and

MODIS swath. On the contrary, geostationary geometry

is closer to the backscattering domain and makes such

contamination negligible. It is even more interesting to

notice that the small adjacency effect observed for GOCI at

2:16 UTC (Fig. 3) decreases at 4:16 UTC (Fig. 4), when the

scattering angle is closer to 180°.

In term of reflectance level, MERIS nRrs values are

systematically lower than GOCI and MODIS ones. MODIS

nRrs values are higher than GOCI values at 412 nm then

progressively become lower as the wavelength goes towards

red. Again, the MODIS 412 nm should not be taken with too

much consideration because of changes in the radiometric

calibration.

A more quantitative analysis is made by overplotting the

Fig. 3. Maps of nRrs (412, 490, 555 and 660 nm from top to bottom) retrieved from MERIS at 01:49 (left) and GOCI at 2:16 (right) onOctober 4th 2011. The sttaight black lines represent two specific transects (one almost N-S and one almost W-E) along which thedifferent satellite products were compared


nRrs values retrieved from GOCI, MERIS and MODIS

data along the two transects (Fig. 5 to Fig. 8) with large

variations depending on water type. Transects are ideal to also

show nRrs from MERIS retrievals not using the vicarious

adjustment gains as well as from MODIS retrievals using

the SWIR bands. In addition, the three GOCI daily products

(2:16, 3:16, 4:16) are shown along with the two MERIS and

the two MODIS products, which finally provide full

quantitative views of all possible discrepancies at separate

wavelengths and for few selected dates of acquisition

providing the most various and complete examples. Some

sensors may not provide data because of failure, cloudiness,

or flagging where others do and some data may seem to be

missing because of a lack of overlapping of the FOVs. On

all figures the vertical scale is similar for each transect but

the geographical extent along the transects is adapted to fill

each figure horizontally.

At 412 nm, MERIS nRrs values are lower than all the

others, but we notice that deactivating the vicarious calibration

brings them much closer to GOCI retrieval (e.g. October 4th

and September 23rd cases). Contrary to what is seen on Fig.

4 from the October 4th case, MODIS nRrs values are not

necessarily higher than GOCI nRrs values at 412 nm as

seen now on top of the transect figures. However, this is

where the maximum differences are observed (up to a factor

of four) between MODIS and GOCI. Overall, the 412 nm

MODIS reflectances show a very divergent behavior along

the W-E transect, i.e. across the sensor swath. Results at 443

nm are qualitatively comparable to those at 412 nm (not

shown). This confirms the impact of the changes in the

radiometric calibration mentioned in Meister et al. 2012 at

412 nm and, to a lesser extent, at 443 nm.

At 490, 555 and 660 nm, an interesting feature is the good

agreement observed between MODIS and GOCI nRrs

Fig. 3. Continued


products, with very nice consistency in the spatial dynamics

and the amplitude of the signal (with however sometimes

slightly higher values for GOCI), except for the most turbid

section of the transects. On the other hand, over those turbid

waters, GOCI nRrs, when successful, get much closer to

MERIS retrieval (see e.g. September 23rd on the W-E transect

and October 4th 2011 on the N-S transect), what shows the

sensibility of the retrieval with the different bright water

atmospheric corrections. At 660 nm the discrepancy (Fig.

4) is logarithmic and accentuates the rather small absolute

difference. The use of SWIR bands for the atmospheric

correction is noisier and does not necessarily lead to

improvements. This retrieval option, along with the removing

of the vicarious gains for MERIS, is not considered further

in the present study.

Overall, where the GOCI retrieval does not fail and at

wavelengths for which MODIS nRrs can be considered

valid, the GOCI nRrs reside within the MERIS and MODIS

uncertainties and their spatial evolution is qualitatively well

correlated to those of the two other sensors. This gives credence

to the quality of the GOCI nRrs products compared to other

sensors.

Inter-comparisons using all concomitant data

First results have been presented only for four dates of

acquisition providing the best overlapping of the FOVs for

analyses over maps and transects. However, the other dates

listed in Table 2 also provide concomitant observations

worth of interest. A grouping of all data together allows the

make-up of general scatter plots of nRrs values obtained

from the three sensors and the computation of linear

regressions. On these scatter plots the nRrs values from

Fig. 4. Maps of nRrs (412, 490, 555 and 660 nm from top to bottom) for GOCI at 4:16 (left) and MODIS at 5:00 (right) on October 4th

2011


GOCI (2:16) and MERIS, then GOCI (4:16) and MODIS

are compared. Another specific interest is to compare the

GOCI (2:16) and GOCI (4:16) nRrs values which are

representative of the natural variability of the seawater

reflectance within two hours.

Fig. 9 shows the resulting scatter plots of nRrs at 412,

490, 555 and 660 nm (or equivalent). The color scale of the

density (i.e. the normalized probability of a (x,y) couple to

“drop” in a cell, normalization is made with respect to the

total amount of couples) is logarithmic but the linear regression

is computed from all data without transformation.

These results confirm the preliminary analysis: (i) on

average MERIS nRrs are lower than GOCI, but MERIS 412

nm band shows slightly higher nRrs at higher turbidity; (ii)

MODIS nRrs values show drastic differences at 412 nm

which should not be taken too much into consideration. The

best correlations (r2 higher than 0.9) are obtained at the

longest wavelengths (red part of the spectrum), which are

closer to the NIR domain where aerosols are detected. The

dispersion within the GOCI data (with two hours difference) is

comparable to the dispersion obtained when comparing

GOCI to MERIS and MODIS data. However, it does not

explain the biases observed between sensors since there is

no bias in the GOCI dispersion.

Inter-comparisons of Rayleigh-corrected TOA reflectances

We remind here that in absence of Sun specular reflection

(Sun glint), the Rayleigh corrected reflectance is composed

of different contributors: aerosols as well as multiple scattering

effects between aerosols and Rayleigh, foam and water-

leaving reflectance propagated at TOA level. Because these

contributors all depend on the viewing geometry, it is

difficult to rigorously compare the TOA signal from low

Earth orbit and geostationary sensors. Our analysis is mainly

Fig. 4. Continued


Fig. 5. Overplot of nRrs products from GOCI (at 2:16, 3:16, 4:16 UTC, black to grey), MERIS (red; pink w/o vicarious calibration) andMODIS (dark blue; clear blue with SWIR correction), along the West-East transect, at 412 nm (left) and 490 nm (right) on fourcase studies


Fig. 6. Overplot of nRrs products from GOCI (at 2:16, 3:16, 4:16 UTC, black to grey), MERIS (red; pink w/o vicarious calibration) andMODIS (dark blue; clear blue with SWIR correction), along the West-East transect, at 555 nm (left) and 660 nm (right) on fourcase studies


Fig. 7. Overplot of nRrs products from GOCI (at 2:16, 3:16, 4:16 UTC, black to grey), MERIS (red; pink w/o vicarious calibration) andMODIS (dark blue; clear blue with SWIR correction), along the North-South transect, at 412 nm (left) and 490 nm (right) onfour case studies


Fig. 8. Overplot of nRrs products from GOCI (at 2:16, 3:16, 4:16 UTC, black to grey), MERIS (red; pink w/o vicarious calibration) andMODIS (dark blue; clear blue with SWIR correction), along the North-South transect, at 555 nm (left) and 660 nm (right) on fourcase studies


Fig. 9. Density scatter plot (logarithmic color scale) and corresponding linear regressions between the GOCI (2:16) and MERIS (left),GOCI (4:16) and MODIS (middle), and GOCI (2:16) and GOCI (4:16) (right) nRrs values at 412, 490, 555 and 660 nm (fromtop to bottom)


aimed at checking GOCI TOA signal consistency for

exploitation over turbid waters.

Fig. 10 shows a panel of the ρRC fields on October 4th 2011

for MERIS 1:49, GOCI 3:16 and MODIS 5:00 at 412 and

660 nm. Other wavelengths produce similar patterns (not

shown). The sole use of the GOCI 3:16 data is justified a

Fig. 10. Maps of Rayleigh-corrected reflectances at 412 (left) and 660 nm (right) for MERIS (top), GOCI 3:16 (middle) and MODIS(bottom) on October 4th 2011


posteriori by the much smaller temporal variability of the

ρRC fields (see transects analysis below).

Note that a threshold (> 0.2) has been applied to GOCI

ρRC values in order to discard data over land and most of the

clouds (while MERIS and MODIS processors provides ρRC

only over water); this threshold was set as a compromise

Fig. 11. Overplot of Rayleigh-corrected reflectances from GOCI (at 2:16, 3:16, 4:16 UTC, black to grey), MERIS (red) and MODIS(blue) along the West-East transect at 412 nm (left), 490 nm (middle) and 660 nm (right) on four dates (top to bottom)


between high values over water pixels and small values over

cloudy pixels. At each wavelength, ρRC values compare

qualitatively well with however slightly smaller values for

MERIS except at some high value spots apparently

contaminated by clouds.

Again, the two transects provide more quantitative views

Fig. 12. Overplot of Rayleigh-corrected reflectances from GOCI (at 2:16, 3:16, 4:16 UTC, black to grey), MERIS (red) and MODIS(blue) along the North-South transect at 412 nm (left), 490 nm (middle) and 660 nm (right) on four dates (top to bottom)


of these discrepancies for all the acquisition dates. Fig. 11

and Fig. 12 show the three GOCI (2:16, 3:16 and 4:16) along

with the MERIS and MODIS ρRC for the same selection of

dates.

The dispersion between the daily GOCI data (2:16, 3:16

and 4:16 UTC) is much smaller than the difference with the

other sensors, except where strong spikes reveal pixels not

corrected for clouds having an effect below the threshold of

0.2. It means that the natural variability of the signal with

time does not explain the discrepancies observed between

GOCI and MERIS/MODIS. Note that spikes on MERIS

data along the transects are also related to thin clouds not

detected in the L2 processing and should not draw our

attention in the present study (this was checked on L1

data).

As previously mentioned, the across track calibration

problem change of MODIS at 412 nm has been identified

on the West-East transect (in particular on September 23rd

and October 4th 2011); hence these precise data should be

considered with caution. However, on the West part of the

transect, and on the whole North-South transect, there is a

good agreement between GOCI and MODIS at 412 nm,

except on April 11th 2011. This is however not true for

MERIS Rayleigh-corrected reflectances, which are lower

(by a factor of 1.5); this is an unexpected difference, which

cannot be explained by the MERIS vicarious adjustment,

not applied at this stage of the processing, and should be

analysed further. The difference is likely due to viewing

angle difference between sensors, which gives different

path lengths and scattering angles. In the swath center of

MERIS or MODIS, GOCI ρRC would give higher values

due to slant view by GOCI. To give order of magnitude, we

have checked with MERIS radiative transfer Look-up

tables that the aerosol reflectance may vary up to +0.01

between view zenith angle of 10° and 45° in forward

geometry (i.e. in the half-west part of MERIS swath) for

aerosol optical thickness up to 0.15 at 865 nm. It is also

worth noting that GOCI ρRC contains the sea-surface

reflection term, which is not the case for MODIS and

MERIS. This would explain qualitatively the differences in

ρRC between sensors.

At 490 nm, a better agreement is found between MERIS

and MODIS and some discrepancies appear with GOCI

(e.g. October 4th) for the clearest waters, while the signal on

turbid waters (most western part of the W-E transect) is

generally in good accordance for all sensors.

At 660 nm, the variations detected by the GOCI, MERIS

and MODIS sensors are quite consistent, with only significant

differences observed for the lowest values (i.e. clearest

waters).

Considering uncertainties in the sensor comparison, due

to GOCI longer path and GOCI sea surface reflection

effects (mainly sky glint), these preliminary results prove

there is no obvious bias in GOCI Rayleigh-corrected

reflectance (hence in TOA radiance) so that this signal can

be used with confidence over turbid waters where current

atmospheric correction fails to retrieve the seawater

reflectance.

Fig. 13. Match-ups between in situ (green), GOCI (black andgrey), MERIS (red) and MODIS (blue) nRrs spectra onSeptember 23rd 2011 (10:30 am, top, and 1:30 pm,bottom). For the satellite spectra: mean in plain lines andmean +/- standard deviation in dashed lines


Quality assessment against field reflectance measurements

Few in situ nRrs measurements carried out during the

2011 oceanographic campaign were concomitant with clear

sky conditions for match-ups with GOCI, MERIS and

MODIS data. If restricting the collocation with satellite data

within a circle of 0.05° latitude/longitude centered on the in

situ observations, only September 23rd provides data from

all sensors.

Fig. 13 shows all of the in situ, GOCI, MERIS and

MODIS spectra obtained for the two in situ acquisitions in

the East China Sea (32°06.459 N, 125°12.150 E, 10:30 am

local time and 32°06.148 N, 125°11.363 E, 1:30 pm local time).

Satellite-derived nRrs spectra are shown as mean as well

as mean +/- standard deviation (dashed lines) of the nRrs

values collected within the circle of 0.05° surrounding the

field measurement.

Due to the time difference (3 hours) imposed for match-

ups between satellite and field data, only measurements

carried out on September 23rd are considered here. The two

field acquisitions on this date were spatially close and logically

the corresponding satellite-derived spectra are mostly the

same. However, the two in situ acquisitions show a decrease

from the 10:30 am shot (top) to the 1:30 pm shot (bottom).

One of those is between the spectra of GOCI and MERIS

(10:30 am shot) and the other one is generally closer to the

MERIS spectrum.

Both GOCI and MERIS show a bulge around 500-600

nm, which can be related to a general bulge in the in situ

spectra. This bulge is not seen on MODIS since the 412 and

443 nm nRrs are overestimated.

These spectra, only observed on one day of acquisition,

recall some of the features observed along the previous

analyses of nRrs: at short visible wavelengths (412-443

nm), MODIS nRrs values are overestimated while MERIS

nRrs seem sometimes too low.

In this particular case the in situ measurements give

highest confidence in the MERIS retrieval (spectrum 2 of

Fig. 13) and, to a lesser extent, in the GOCI retrievals

(spectrum 1). However, for a statistically significant result

more direct comparisons between field and satellite data

are required. Therefore, we conclude this section with the need

of further in situ observations for validation purposes.

4. Discussion and Conclusion

The region of the Korean Peninsula, East China and

Yellow Seas is complex for the retrieval and validation of

ocean colour products. A strong limitation is the high

cloudiness of this region which usually impedes ideal

conditions for the atmospheric corrections. Another local

difficulty, both for atmospheric correction and cloud masking, is

the high level of turbidity especially over the Yangtze delta

and close to the coasts of the South-West of Korea. Highly

turbid regions represent a challenge for ocean colour remote

sensing and are ideal cases to assess the current validity of

GOCI radiometric products.

The benefits of the geostationary geometry, as compared

to these sensors, have first been observed with less adjacency

effects and less glint. Although we have not fully exploited

the possibility of acquiring observations every hour over

the same area, the high periodicity of the geostationary

acquisitions allowed a closer temporal coincidence with the

other sensors.

Our analyses show first a relative agreement between

GOCI, MERIS and MODIS seawater reflectance products,

which is quite promising for the exploitation of ocean

colour satellite data in geostationary orbit. Considering

uncertainties in the marine signal for both Sun-synchronous

sensors, and their relative differences, no obvious bias was

found here in the GOCI product. However our results

highlight the need of lowering the cloud detection threshold

and improving the atmospheric correction of GOCI data

over turbid waters. This conclusion is drawn based on

numerous analyses of the reflectance signal prior and after

atmospheric corrections. The problem does not come from

a sensor mis-calibration in the visible wavebands, as

illustrated on Fig. 14 for September 4th and October 4th

2011: on those cases, MERIS and GOCI sensors reveal

strong consistency in Rayleigh corrected signal, in

particular over the Yangtze delta where GOCI nRrs are

unavailable.

Quantitatively, the seawater reflectances retrieved from

MERIS data are typically lower than those retrieved from

GOCI and MODIS. A good agreement is generally

obtained between GOCI and MODIS nRrs products at

wavelengths longer than 443 nm (where calibration issues

are known for MODIS). These results are however

independent from a knowledge of the “ground-truth” which

is the key information to validate the algorithms. The

availability of few in situ data from the KOSC campaign

allowed a comparison of all sensors together against two in

situ high-quality spectra of the nRrs. Although, locally,


these two spectra showed better correspondence with

MERIS we cannot but only conclude in the necessity to

gather more of these spectra to obtain statistical confidence

in such comparisons.

We recommend two necessities for the assessment of

GOCI data: 1) the provision of the atmospheric corrected

data over highly turbid waters probably by improving cloud

discrimination over turbid pixels; and 2) the need of numerous

field nRrs measurements over the Yellow and East China

Seas to multiply match-ups with satellite measurements (up

to ten satellite images a day over this region when combining

GOCI, MERIS and MODIS data).

In a short term, systematic delivery of GOCI Rayleigh

corrected reflectance could be a solution for data exploitation

over the most turbid areas.

Fig. 14. Maps of GOCI nRrs and ρRC (first and second row) and MERIS nRrs and ρRC (third and fourth row) at 660 nm. September 4th

2011 (left) and October 4th 2011 (right)


AcknowledgementsWe are strongly grateful to the Korea Ocean Satellite

Center (KOSC)/KORDI team for its help and support in

GOCI data handling. This work was cofunded by the EC

FP7 AQUAMAR project, Centre National d’Etudes Spatiales

(CNES) contract n°116405 and GOYA project (TOSCA,

Principal investigator D. Doxaran). GOCI data were provided

by KOSC/KORDI and processed with the GDPS software,

supported by the Research and Applications of Geostationary

Ocean Color Satellite (PM56890) funded by the Korea

Ministry of Land, Transport and Maritime Affairs (Ryu and

Park). Level 1 MERIS data of the 3rd reprocessing were

provided and processed to Level 2 with the ODESA facility

developed by ESA and ACRI-ST (http://earth.eo.esa.int/

odesa). We thank Julien Demaria from ACRI-ST for his

support on the data reprojection. MODIS data were provided by

NASA and processed at Level 2 with the SeaDAS software

(http://seadas.gsfc.nasa.gov/). We thank two anonymous

reviewers for their fruitful comments.

Fig. 14. Continued


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