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Long term (1983-1994) calibration of the Meteosatsolar (VIS) channel using desert and ocean targetsC. MOULIN a , C. E. LAMBERT b , J. POITOU a & F. DULAC ba Laboratoire de Modélisation du Climat et de I'Environnement, Commissariat a I'EnergieAtomique, Centre d'Etudes de Saclay , 709, Gif-Sur-Yvette Cedex, F-91191, Franceb Centre des Faibles Radioactivites, Laboratoire Mixte CNRS-CEA , Gif-Sur-Yvette Cedex,F-91198, FrancePublished online: 27 Apr 2007.

To cite this article: C. MOULIN , C. E. LAMBERT , J. POITOU & F. DULAC (1996) Long term (1983-1994) calibration of theMeteosat solar (VIS) channel using desert and ocean targets, International Journal of Remote Sensing, 17:6, 1183-1200, DOI:10.1080/01431169608949076

To link to this article: http://dx.doi.org/10.1080/01431169608949076

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INT. J. REMOTE SENSING, 1996, VOL. 17, No.6, 1183-1200

Long term (1983-1994) calibration of the Meteosat solar (VIS)channel using desert and ocean targets

c. MOULlNt, c. E. LAMBERTI:, J. POITOUt and F. DULAq

t Laboratoire de Modelisation du Climat et de l'Environnement,Commissariat a l'Energic Atomique, Centre d'Etudes de Saclay, 709,F-91191 Gif-Sur-Yvctte Cedex, France

tCentre des Faibles Radioactivites, Laboratoire Mixte CNRS-CEA,F-91198 Gif-Sur-Yvette Cedex, France

(Received /6 February /995; in final form 2 October (995)

Abstract. The successive operational Meteosat solar (VIS) sensors have shownsignificant variations of their spectral band which affected their relative sensitivi­ties to the light scattering by aerosols, molecules and Earth suface. This paperprovides a calibration of the various Meteosat VIS sensors used between June1983, the beginning of data archiving for the International Satellite Cloud Clima­tology Project (ISCCP), and December 1994, taking into account the character­istics of their individual spectral band thanks to an atmospheric radiative transfermodel. The method is based on a monitoring of the reflectance of desert and oceantargets free of clouds and aerosols, and relies on the absolute vicarious calibrationof Meteosat-2 performed in late 1981. We find different sensitivities and temporalevolutions for Meteosat-2, -3, -4, and -5, and our results compare well with othermore limited calibration studies.

1. IntroductionSaharan dust plumes are the major source of terrigeneous material in the

atmosphere of the Mediterranean Sea and North Atlantic Ocean, and they alsoinfluence the chemistry and biology of the water column. Dulac et 01. (1992) estimateddust concentrations over the Mediterranean basin from Meteosat-2 full resolutionvisible (VIS) images. We are interested in retrieving atmospheric loads of Saharandust plumes for as long as a decade over the Mediterranean and Atlantic Ocean inorder to study their variability in number, abundance and spatial coverage. This canbe done using the International Satellite Cloud Climatology Project (ISCCP) data­base of Meteosat VIS images since June 1983. Jankowiak and Tanre (1992) used fiveyears of Meteosat-2 [SCCP VIS data to perform a climatology of Saharan dusttransportover the north tropical Atlantic. However, they used the vicarious calibration ofMeteosat-2, performed by Koepke (1983) by means of calculated radiances, whichgives an absolute calibration coefficient for the period June-October 1981 only. Inorder to extend the monitoring of Saharan dust transport to the four different sensorswhich have been used during the Meteosat operation since June 1983, and because ofthe lack of prelaunch or onboard calibration system for the VIS channel, we have toassess the intercalibration and the instrumental drift. Kriebel and Amann (1993)performed a vicarious calibration of Meteosat-2 and -4, using an airborne compari­son radiometer viewing the Earth surface simultaneously with Meteosat sensor. This

0143-1161196 $12.00 © 1996 Taylor & Francis Ltd

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1184 C. Moulin et al.

type of calibration is suitable for Earth radiative budget purposes, but it is notsatisfactory for desert dust quantitative studies, mainly because the resulting calib­ration is dependent on the spectral behaviour of the surface reflectance which affectsthe satellite and the comparison radiometer signals differently. In addition, the driftof all the sensors cannot be estimated from such short experiments. Brisson et al.(1990) derived a multi-year calibration by a survey of Meteosat visible numeric countsover stable and homogeneous desert targets. However, for aerosol studies, we stillhave to take into account the impact of the sensor spectral response on the atmos­pheric contribution to the measured signal. Cabot et al. (1994) calibrated Meteosat-4sensor in 1990 and 1991 using AVHRR data. With the help of a bi-directionalreflectance model, they compute the reflectances of a desert target for the visible andnear-infrared channels of AVH RR; by combining these two reflectances, they obtainthe calibration coefficient for the Meteosat-4 VIS channel. We find such a method toosophisticated to perform a 12-year analysis of Meteosat images. In the present paper,we describe a long term calibration of the various Meteosat VIS sensors between June1983 and December 1994. Assuming a linear calibration, we derive the sensitivity ofthe sensors from a survey of the reflectance of several desert targets and their offsetfrom the observation of dark marine targets.

2. Materials and methods2.1. Meteosat images

Meteosat is a meteorological satellite programme operated by the EuropeanSpace Agency (ESA) since 1977. It consists in a temporal series of geostationaryspacecrafts stationed on the Greenwich meridian at an altitude of 35 800 km. Table Ishows the succession of Meteosat sensors and their gain settings during the con­sidered period. The so-called VIS channel measures the reflected sunlight in a wideband from about O' 3 to 1·1 /lm. The various Meteosat sensors have different sensitivi­ties due to their differences in the spectral band. However, the sensitivity of a givensensor is also affected by changes in the gain setting of the on-board radiometeramplifiers, which can be controlled from the central ground station (ESOC 1982).Digital VIS data from the operational meteorological satellites Meteosat-2, -3, -4 and-5 were obtained from the European Space Operations Centre, Darmstadt, Germany.We used the low resolution (ISCCP B2 format) images with a nominal nadirresolution of 30 km by 30 km (ESOC 1992). Such data are available from June 1983and we considered a sequence of 12 years of daily images (June 1983-December1994), corresponding to Meteosat slot 24 (11:30-12:00 UT observation). The B2images of the full Earth disk have 416 lines of 416 columns in the VIS channel.Meteosat-2 and -3 data were initially coded on 6 bits, but were extended to 8 bits, withvalues ranging from 0 to 252 by steps of 4. For Meteosat-4 and -5, data consist in aneffective 8-bit signal.

2.2. Earth-atmosphere radiative transfer modelWe used a radiative transfer model of the Earth atmosphere derived from the

computer code 'Simulation of the Satellite Signal in the Solar Spectrum' (5S),available from the Laboratoire d'Optique Atrnospherique, Universite des Sciences etTechniques de Lille, France (Tame et al.·1990). This model computes the satellitesignal due to the backscattering of solar light by the ground-atmosphere system,assuming a cloudless atmosphere. It includes analytical descriptions of the absorptionby aerosol particles and atmospheric gases H 2 0 , 03' O 2 and CO 2 , as well as the

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Calibration of Meteosat visible channel 1185

Table 1. Succession of the various Meteosat sensors from 1983 to 1994 (from MeteosatOperations Centre calibration reports).

Starting date Ending date Sensor Gain

I June 1983 12 May 1987 (slot 16) 2 012 May 1987 (slot 17) 11 August 1988 (slot 16) 2 II I August 1988 (slot 17) 19 June 1989 (slot 17) 3 I19 June 1989 (slot 18) 24 January 1990 (slot 17) 4 424 January 1990 (slot 18) 25 January 1990 (slot 17) 3 I25 January 1990 (slot 18) 19 April 1990 (slot 17) 3 019 April 1990 (slot 18) 30 October 1990 (slot 25) 4 430 October 1990 (slot 26) 5 November 1990 (slot 17) 3 05 November 1990 (slot 18) II December 1990 (slot 17) 4 4II December 1990 (slot 18) 13 December 1990 (slot 18) 3 013 December 1990 (slot 19) 22 January 1991 (slot 17) 4 422 January 1991 (slot 18) 25 January 1991 (slot 18) 3 025 January 1991 (slot 19) 2 May 1991 (slot 16) 4 42 May 1991 (slot 17) 3 May 1991 (slot 30) 5 unknown3 May 1991 (slot 31) 26 November 1991 (slot 16) 4 426 November 1991 (slot 17) 26 November 1991 (slot 25) 5 426 November 1991 (slot 26) 29 November 1991 (slot 17) 5 529 November 1991 (slot 18) 11 February 1992 (slot 17) 4 411 February 1992 (slot 18) 25 February 1992 (slot 17) 5 525 February 1992 (slot 18) 4 May 1993 (slot 16) 4 44 May 1993 (slot 17) 7 May 1993 (slot 17) 5 57 May 1993 (slot 18) 3 November 1993 (slot 20) 4 43 November 1993 (slot 21) 18 November 1993 (slot 17) 5 518 November 1993 (slot 18) 4 February 1994 (slot 18) 4 44 February 1994 (slot 19) 3 I December 1994 5 5

scattering by gas molecules (Rayleigh scattering) and aerosols (M ie scattering).Geometrical and spectral conditions, atmospheric gaseous composition, aerosol typeand concentration, as well as ground reflectances in and around the target are theinput parameters. The outputs of the model are the direct and diffuse transmittanceson the Sun-ground and ground-satellite paths, the spherical albedo of the atmosphereand of the environment, as well as the transmission multiplicative factor related to theabsorption by atmospheric gases and particles. To simulate the satellite signal, thecode takes also into account the spectral band of the considered sensor.

2.3. Meteosat spectral handsThe spectral response of each VIS channel of Meteosat-2, -3, -4 and -5 is taken

from Rossow et al. (1992). These normalized spectral responses are shown in figure I.The spectral responses have varying shapes, with a maximum transmission between0·6 and 0·9 /.lm. Figure I also illustrates the spectral dependence of the main compo­nents which influence the satellite signal: the transmissions represent the attenuationof the light due to absorption (T.) and scattering (TRoy) by air molecules; thereflectances characterize the scattering efficiency for the molecular atmosphere (PRoy)'for the sea surface (P,.o) and for the desert surface (PO.,), respectively. T", TRoy andPRoy are calculated using the radiative transfer model with the standard tropicalatmosphere model of McClatchey et at. (1971), P"o with the clear seawater reflectancemodel of Viollier (1980) and POe, with the desert soil reflectance model of Staetter and

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1.1

Wavelength (urn)

0.5 0.6 0.7 0.8 0.90.3 0.41.0r~~w....L.~~......~~;:-'-L1...L.~w....L.'-'i:-

I'

- 0.8;<l

'"0.6 ::!l- - - T ~

KdY P Dr--T, ""Y -- Pd,"'''':......l..-f----!. 0.4 ~

-. <>:_":. .....----

ii~i -~-i-i·i-i\!"'!"or l,f"I"

0.6

0.2-

0.6

0.2

0.8co

'Vi'"'5~ 0.4'"...I-

c: 0.8o

,'til.:!lEV>c: 0.4'"...I-

03 0.4 0.5 O~ OJ OB 09

Wavelength (urn)

Figure I. Upper part of the graph shows the spectral responses of Meteosat sensors (afterRossow et al. 1992). Lower part illustrates the spectral dependences of the maincomponents affecting the satellite signal (computations for Sun zenithal angle=9'8°,viewing zenithal angle=38'9°, azimuthal angle difference=22·4°). The decreasesof T"are due to ozone (below 0·35/lm) and water vapour (between 0·90 and !·OO/lm andabove I·05/lm).

Schroeder (1978). Figure I shows that Meteosat-2 has a much lower sensitivity thanthe other sensors below 0·6 )lm, i.e., in the domain of scattering by atmospheric gasmolecules. On the opposite, Meteosat-4 and -5 are more sensitive to the scattering bygas molecules. At wavelengths above 0·8 )lm, Meteosat-5 is less sensitive thanMeteosat-2, -3 or -4; thus its sensitivity to gas absorption, mainly water vapour, issmaller. An accurate description of these radiative transfer components is given in§ 2.7 and their influence is further discussed in § 3.

(I)

2.4. Meteosat calibrationThe radiance emerging from the atmosphere and measured by the satellite, L,a' (in

W m - 2 sr - I ), is defined as:

L,a,(Os, o., r/»= l~o L,(A,Os, 0v, r/» SeA) dA

where A is the wavelength, Os and 0v are respectively the solar and viewing zenithalangles (depending on the day, time, and target position), and r/> is the differencebetween the solar and viewing azimuthal angles. L, is the spectral radiance at the topof the atmosphere and S the spectral response of the sensor. This radiance is digitized

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Calibration of Meteosat visible channel 1187

by the electronics of the sensor. Assuming a linear response of the sensor, the numericcount (NC) is recorded as:

(2)

where C( is the calibration coefficient and NCo the offset numeric count of thecalibration. For Meteosat-2 (gain 0) at the end of 1981, Koepke (1983) gives acoefficient C( of0·575 W m -2 sr- t NC- t ± 10 per cent and an offset numeric count of2.

The offset NCo is also often called the space count. However sensor responsesgenerally have a non-linear behaviour at low energy. Therefore NCo is likely to bedifferent from the space count measured by the sensor when it views outside the Earthdisk. For Meteosat-2, we have estimated the drift of the 1981 calibration coefficientgiven by Koepke (1983), assuming that the offset numeric count remained constantover the whole period. For Meteosat-3 to -5, we had to calculate the calibrationcoefficient, its drift, and the offset numeric count.

Our calibration procedure consists in a preliminary estimate of C( from the highnumeric counts of desert targets using the value of the space count as NCo in eq uation(2), considering that the influence of NCo is small. We further estimate NCo from thelow numeric counts of marine targets using the first estimate of c(. Finally, werecalculate the calibration coefficient C( using the adjusted value of NCo.

2.5. Choice of targetsThe optical depth of Saharan dust plumes is generally retrieved from VIS images

only over the sea because surface seawater has a very low reflectance, so that theatmospheric back scattering controls the satellite signal (Dulac et at. 1992, Jankowiakand Tame 1992). Moreover, the retrieval of the radiance from a small numeric countis more sensitive to the offset numeric count value. The calibration coefficient c(, then,should be derived from targets with large numeric counts, such as land or clouds, tominimize the influence of the offset as well as the contribution ofatmospheric aerosolsto the observed signal. On the opposite, the offset of the linear calibration, NCo,should be derived from marine pixels.

Desert soil targets were chosen for the determination of C(; they offer the advan­tage of inducing much less spatial and temporal variability than targets includingclouds or vegetation. Four African sand desert targets were chosen; their locations arereported in table 2. Targets 1 and 2 (Grand Erg Oriental, Algeria and Tunisia,respectively) were chosen for comparison with Brisson et at. (1990) calibrationexperiment. Target 3 (Grand Erg OCcidental, Algeria) was chosen for comparison withthe future multi-spectral instrument POLDER (Deschamps et al. 1994, Leroy and

Table 2. Number of 82 pixels used, and location of the desert targets chosen for thecalibration.

NumberTarget Location of pixels Latitude (ON) LongitudeeE)

I Algeria 3 32-8 6·82 Tunisia 3 32·8 9·53 Algeria 3 31·1 2·04 Lybia 2 24·5 13-1

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1188 C. Moulin et al.

Breon personal communication) and target 4 (Fezzan dunes, Lybia) has been used byCabot et al. (1994). For each target, two or three adjacent B2 pixels were analysedindependently.

In order to estimate the olTsetof the linear calibration, we have used all the marinepixels of the Mediterranean basin. We have only examined the yearly minimalnumeric count of each pixel because the satellite signal over the sea surface is sensitiveto the presence of aerosols and to variations in surface water reflectance. We assumethat this minimum characterizes the observation of clear water with an atmospherefree of aerosols.

2.6. Selection of clear pixelsWhereas we simply extracted the yearly minimal numeric count for all the marine

Mediterranean pixels, we needed a more detailed analysis of the desert targets. Inorder to minimize short term variations of the numeric counts likely due to cloudsand/or high atmospheric turbidity, clear sky conditions must be selected. The ideawas, for each pixel, to divide the whole period of 12 years in small periods over whichthe Sun elevation variation from day to day at the same hour can be neglected, and touse for the calibration only bins of continuously clear sky. We found a period of fivedays suitable for discriminating cloudy situations by the variation of the numericcount. The whole 12 years of data were thus divided into bins of five days. For eachbin and for each desert pixel, we determined the maximum (NCmax ) and minimumnumeric counts (NCm;n)' Because clouds are highly variable in time and space, andinduce larger numeric counts than desert soils, NCmax and NCm;n are very close onlyfor clear sky periods. We considered that the atmosphere was either cloudy or turbidand eliminated the data if the dilTerence between NC ma , and NCm;n was greater than10 per cent of their average, i.e., for a given desert pixel and a given bin we retainedthe mean value or NC ma , and NCm;n as a 'clear sky' numeric count when:

(3)

(4)

We considered only the mean value of every bin retained because the impact of desertdusts on the measured radiance is not straightforward; for the Meteosat spectralband, LSaI may decrease or increase depending on aerosol type and soil nature whenthe desert dust concentrations increase (King et al. 1992). On the average, the testeliminated about half of the bins from the whole data set for any pixel. We have usedthis cloudy pixel elimination method to create a 12-year data set of 'clear sky', 5-dayaveraged, numeric counts for each target.

2.7. General calibration procedureThe apparent reflectance PSD' at the satellite level is derived from the satellite

radiance LSD" by normalizing the scattered radiance to the incoming irradiance:

PSD,(f)S' 0v, </» nLsD,(Os, °v, 1/»

u,1:0Es(A)S(A)dA

where Jls is the cosine of the solar zenithal angle and E, the solar irradiance at the topof the atmosphere. This apparent reflectance can also be written as (Tame et al. 1990):

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Calibration of Meteosat visible channel 1189

where Palm and P are the spectral reflectances of the atmosphere (both gas moleculesand aerosols) and of the surface, respectively. Tg is the total atmospheric gaseoustransmission; it takes into account the attenuation of the scattered light by gaseousabsorption (ozone, water vapour and oxygen) during the two paths (downwards andupwards) through the atmosphere. T.lm is the total atmospheric scattering trans­mission which represents the attenuation by gas molecules and aerosols of the light toand from the surface. The spherical albedo of the atmosphere, s, allows us to take intoaccount the multiple scattering between the surface and the atmosphere.

We assumed that, for 'clear sky' conditions, the aerosol contributions to Palm andT.lm can be neglected. In such conditions, the term p(J.) x s(),) can also be neglected.The apparent reflectance can then be written as:

Psal (Os,0v, 4J) = 1.:0 T,,(J., Os,OV)[pRaP' OS, 0v, 4J) + p(J.) TRay(J., OS, Ov )]S(J.) d(J.)

(5 b)

where PRay and TRay are respectively the spectral reflectance and the scatteringtransmission of the molecular atmosphere.

We also assumed that the desert reflectance, Pde" was independent of the wave­length. Then, using equation (4) in equation (5 b) we related the satellite radiance tothe reflectance of the desert target:

(6 a)

fls f'"+ Pdes. - Es(J.)T,,(J., e; Ov) TRay(J., o;Ov) S.(J.) d(J.)7[ <=0

where the subscript n refers to the Meteosat number. TR•y, T. and PRay were computedfrom the radiative transfer model considering the standard tropical atmospheremodel from McClatchey et al. (1971).

For the marine targets, we have used the spectral variation of the clear seawatermodel from Viollier (1980), used in 5S (see figure I). In order to adjust the integratedmarine reflectance to the value derived from the minimum Mediterranean counts, wecorrected the spectral reflectance Psea by a constant coefficient p, independent of theMeteosat spectral band. Then, using equation (4) in equation (5 b) we related thesatellite radiance to the reflectance of the sea surface:

(6b)

fls f'"+P-; <~ 0 Es(J.) T,,(J., Os,o.,Psea(J.) TRay(J., e; Ov) S.(J.) d(J.)

Our calibration procedure followed different successive steps: (i) we calculatedL,al' from all 'clear sky' desert and marine numeric counts from June 1983 to May1984, using equation (2) and the calibration coefficient and offset numeric count of

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1190 c. Moulin et al.

0.25,------'-------------'--------,

0.1

0.2

01~ 0.15.su01

<::01

c>:

0.05·

------------

36827692a-j-~~---,-----,---~~---,~-,.-----1

a 184Julian day

Figure 2. Effect of the Meteosat spectral response on the Rayleigh, aerosol and desertreflectances. Contributions to Meteosat-2 signal are drawn in plain and to Meteosat-4in bold; the two curves of aerosol reflectance are superimposed. Computations are fortarget I (see table 2).

late 1981 (Koepke 1983), (ii) from this first year of Meteosat-2 data, we derived Pd."

using equation (6 a) (see §3.1) and fJ using equation (6 b) (see § 3.2). Pde" wassupposed to be constant for a given target and a given time of the year throughout thewhole Meteosat-2 period, (iii) with various published data on desert reflectances, weestimated the impact on integrated desert reflectances of changes in Meteosat spectralbands (see § 3.1). This allowed us to compute Pdes" Pde" and Pdes, from Pdes" (iv) weused equations (6a) and (6b) to calculate L sa' . for each desert and marine targetbetween June 1984 and December 1994, (v) finally, using Lsa' . ' we inverted equation(2) to obtain the best fit of the calibration coefficient and offset numeric count overthe whole 12-year period (see §3.3).

3. Results and discussionThe relative importance of the various reflectances PRay, Paer (the aerosol reflect­

ance), Pdes and p,.a is first assessed using the radiative transfer model 55. Figure 2illustrates the variations of three of the integrated reflectances (PRay, Po., and Pdes) as afunction of Julian day over one year, as computed for Meteosat-2 and -4. Theatmosphere was described by the standard tropical atmosphere model of McClatcheyel al. (1971). For the aerosols, we chose the background desert aerosol model ofShettle (1984) with a trimodal size distribution. This model is characteristic of soil­derived particles in remote continental areas (Jaenicke 1987) but has been also shownto be likely representative of desert dust particles transported ofTSahara (Dulac et al.1992, Jankowiak and Tame 1992). In figure 2, the aerosol optical depth at 0·55 Jlmwas taken as 0·2. Desert and marine reflectances were determined respectively fromthe model of dry sand reflectance of Staetter and Schroeder (1978) (see figure 1). Wemade the same type of computation for a marine target, using the 'clear water'reflectance model of Viollier (1980). The values of the marine integrated reflectancePsea are about constant and of 0·024 and 0·027 for Meteosat-2 and -4, respectively.

Results in figure 2 justify three of our contentions: (i) the increase by almost afactor of two of PRay from Meteosat-2 (low sensitivity at short wavelengths) to

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Calibration of Meteosat visible channel 1191

Meteosat-4 implies that the influence of the spectral bands cannot be neglected; (ii)Pde, is the main contribution to the satellite signal and its relative variation (about 10per cent between Meteosat-2 and -4) has to be taken into account when the spectralband changes. (iii) Pae, is small and not affected by the change of the spectral responseof the sensors (the two curves are superimposed on the figure). Indeed most of thedesert aerosol signal comes from scattering by large particles, which is ratherunsensitive to the wavelength in the visible spectrum (Jaenicke 1987).

3.1. Reflectance of desert soilsUsing equation (6a) for the period from June 1983 to May 1984 with the

calibration of Koepke (\983) (see equation (2», we obtained the annual variability ofthe integrated desert reflectance Pde" for each of our targets, as shown on figure 3..The number of 'clear sky' data per target varied between 85 and lIS. For each target,a cosine-like dependence with the Julian day (JD) is observed with a summerminimum and an overall variation in amplitude of 10-20 per cent. Such a variationmay be attributed to the directionality of the soil reflectance, as previously observedby Dedieu (1992). With a geostationary sensor like Meteosat, the viewing zenithalangle is constant for a given pixel; directional effects, then, are mainly connected tothe Sun elevation. Directional effects are thus expected to show up as a cosine curvecentred around the solstices and we fitted the data by the following curve:

Pde"=mo +m, cos (2nJD3:5m2 ) (7)

Resulting fitted reflectances are shown by the solid lines on figure 3. The coefficientsmo, m, and m2' together with the number of 'clear sky' data and regressioncoefficients are given in table 3. The formula chosen for the fit is certainly over­simplified to represent the actual directionality for soil reflectances (Roujean et a1.1992, Rahman et a1. 1993), but the correlation being rather good (correlationcoefficients between 0·86 and 0,92), we considered that its accuracy was sufficient for

0,5 -,-------------------,

0,4 -

QJ 0,45uc

'"UQJc::~

368276920,35 +-~~-_,__~~-.-~~_____,-~~---j

o 184Julian day

Figure 3. Integrated reflectance of the desert soils as derived from Meteosat-2 data. Solidlines represent the fitted integrated desert reflectances for each target (numbers refer totarget numbers in table 2).

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1192 C. Moulin et al.

Table 3. Parameters (mo, m, and m,; see equation (2)) and correlation coefficient (r) of the fitsof integrated desert reflectance Pde" , as computed from the first year of Meteosat-2 B2data (June 1983-May 1984).

NumberTarget of data mo m, m, r

I 106 00440 0·031 -22 0·922 84 00409 0·037 -18 0·913 114 00420 0·030 -14 0·924 92 0·461 0·026 -18 0·86

our calibration purpose. Figure 3 shows that each target has specific characteristics,likely due to dilTerent soil types and sun and viewing geometries.

In order to compute the etTect of the spectral response of Meteosat-3, -4 and -5 onfide.", we used the general property of desert soil reflectance, which is an increase withwavelength (Courault et at. 1988), as illustrated in figure I for the model of Staetterand Schroeder (1978). With this reflectance model, we computed the desert reflect­ance integrated over the various Meteosat spectral bands: we found that fid"'3' Pde",

and Pde", have respectively to be decreased by 4, 12, and 17 per cent relatively to fid"'2'

We verified that such variations were representative of desert soils by using variousother spectral reflectances obtained from laboratory measurements performed onarid soil samples from Tunisia (Escadafal 1989), and on dry sand and dry silt samplesfrom U.S.A. (Bowker et al. 1985). Whereas spectral evolution and mean value ofthese reflectances are dilTerent, they all lead to a decrease in Pde,. of about the sameamplitude. Because we had no accurate information about the actual reflectances ofour targets, we used this data set to estimate the uncertainties of our corrections (see§3.4).

3.2. Reflectance oj the clear seawaterFrom our first year of Meteosat-2 data (June 1983-May 1984), we obtained a

mean value of 0·55 for the correcting factor of the Viollier's reflectance model (fJ seeequation (6 b». The histogram for Meteosat-2 shown on figure 4 shows the narrowdispersion of fJ around 0·55. In order to test if such a decrease in sea surfacereflectance compared to the value given by the model of Viollier (1980) is realistic, weused the seawater reflectance model of Morel (1988). This model is developed forocean colour study and has a strong dependence with phytoplancton content ofseawater. We compared the spectral reflectances given by the two models for severalwavelengths and dilTerent phytoplancton contents, and we found that the model ofMorel leads to reflectances smaller by about a factor of 2 to 10 than the model ofVioilier. Our coefficient of 0·55 then leads to a sea surface reflectance compatible withthe more sophisticated reflectance model of Morel. Using this value, we retrievedintegrated marine reflectances of 0,013, 0,015, 0·015 and 0·018 for Meteosat-2, -3,-4and -5, respectively.

3.3. Calibration offsets and coefficientsFigure 5 shows the evolution of the coefficient IX for the whole period from 1983 to

1994. For each configuration characterized by a sensor with a specific gain, we

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Calibration of Meteosat visible channel 1193

25-r-------------------,

20

~ 15>.u

iii"arlO....

IJ.

5

o 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

J3

Figure 4. Histograms of the {i coefficient for yearly minimal image of a Mediterraneanwindow of 1908 82 pixels for Meteosat-2 (1983-84), -3 (1988-89), -4 (1993) and -5(1994). The values of the ofTset numeric count (NCo) come from table 5.-- Met -2,--- Met -3,-- Met -4, --- Met -5.

performed a linear regression fit of the data. Coefficients, uncertainties and confi­dence levels are given in table 4. Figure 5 clearly shows that the various sensors arestrikingly different. The difference in the mean values of the calibration coefficientsbetween successive Meteosat configurations is always higher than the dispersion ofpoints within a given configuration. The slope represents the drift of the given sensor;very low for Meteosat-2 and -5, about 0·5 per cent per year, and higher for Meteosat-4(2 per cent per year). The drift of Meteosat-3 seems to be high (around 10 per cent peryear), although the uncertainties remain important (see table 4).

. , ~

(f(

(b)

10,6 -jj

0,8 --,--------------------,

0,75 ~.ri

0,7 .::

0,45 ~

0,4 --,---------r---,-------r---,-------r---,-------r---,-------r-------r----1

i0,65 -j

"~

E055 -"~ , J

0,5 ~

"Lz.,.....'"

1~1_IEl_l_l_l_lmlmlmlmlm

YearFigure 5. Calibration coefficients (X of the various Meteosat sensors over the 12years as

derived from the present method (letters refer to table 5).

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'e.

Table 4. Results of the calibration coefficient Cl determination over desert targets; the intercept of the linear regression analysis is given for the first day ofthe period.

Fit of the calibration coefficient (see figure 4)0

Label Overall period Number Intercept Slope Confidence~Sensor Gain in figure 4 (see table I for details) of points (Wm- 2 sr- 1 NC- 1 ) (Wm- 2sr- 1 NC- 1 d-') level (%) <:l

'"2 0 I June 1983-12 May 1987 970 0·577 ±O·OOI 1·4x 10- 5 +0·1 x 10- 5 " 99·9

~a'"2 I b 12 May 1987-11 August 1988 250 0·496 ± 0·002 0·4 x 10- 5 ±0'7 X 10- 5 69·9 ~

3 I c II August 1988-19 June 1989 186 0·542 ±0·002 10·3 x 10- 5 ± i-o« 10- 5 96·0 e,3 0 d 25 January 1990-19 April 1990 53 0·674 ±0·003 23·3 x 10- 5 +5·3 X 10- 5 99·54 4 e 19 June 1989-4 February 1994 1145 0·655±0·001 H x 10- 5 ±O'I X 10- 5

" 99·95 5 f 4 February 1994-31 December 1994 328 0·585±0·002 0·7 x 10- 5 ±0·9 X 10- 5

" 99·9

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Calibration of Meteosat visible channel 1195

Table 5. Results of the calibration offset numeric count NCo determination over marinetargets and relevant space counts.

Sensor

223345

Gain

oIIo45

Overall period

I June 1983-12 May 198712 May 1987-11 August 1988II August 1988-19 June 1989

25 January 1990-19 April 199019 June 1989-4 February 1994

4 February 1994-31 December 1994

Space count

222255

Offsetcount

22oo0·5

-1

Offset numeric counts of each Meteosat sensor and gain configuration have beenestimated using equation (6 b) and the coefficient P of 0·55. Table 5 shows thecalibration offset as well as the space count for each sensor. The comparison betweenthe offset and space count clearly shows that Meteosat VIS sensors do not have alinear response at low energy. Thus, the space count should not be taken as the offsetof the linear calibration, particularly for the more sensitive Meteosat-4 and -5 sensors.

3.4. Accuracy of the methodA first source of error in our calibration procedure is due to the low radiometric

resolution of the sensors, especially for Meteosat-2 and -3. Considering a mean 8-bitnumeric count of 100 over the desert areas, an uncertainty of ± 2 NC for Meteosat-2and -3, and of ±0·5 NC for Meteosat-4 and -5 leads respectively to an error of 2 percent and 0·5 per cent on the calibration coefficient. Over marine targets, thisuncertainty increases to 17 and 4 per cent respectively for a mean minimal numericcount of 12. However, this random error is strongly reduced by our large number ofdata. The use of a standard gaseous atmospheric model to estimate Rayleighscattering and gaseous absorption also generates some error because the features ofthe temperature, pressure and absorbing gas concentration profiles vary with thelocation and the season. However, the contributions of Rayleigh scattering andgaseous absorption to the measured radiance are small: changes in atmosphericcharacteristics involve a relative error of 1-2 per cent.

Our determination of the calibration coefficient for 1983 relied on the use of aformer calibration, supposed stable with time since 1981. The low drift of Meteosat-2(about 0·5 per cent per year) validates our use of the 1981 calibration for 1983.Indeed, if we extrapolate our results from 1une 1983 to October 1981, we find acalibration coefficient C( of 0·569 which differs by only about I per cent fromthe coefficient estimated by Koepke (1983), a difference much smaller than theuncertainty on Koepke's coefficient (± 10 per cent).

For the 'clear sky' pixels, we took into account the mean value of NC..in andNC..ax to compute the reflectances and the calibration coefficients. Whereas weimposed a maximum difference of 10 per cent between NC ..in and NC ..ux , the meanvalue of the measured difference over 12 years is 4·5 per cent; such a range on desertnumeric counts leads to an uncertainty of about 2·5 per cent on the calibrationcoefficient. Another uncertainty of our method comes from the unknown spectraldependence of the desert soil reflectance of our targets. First, we assumed that forMeteosat-2, the desert reflectance was independent on the wavelength in equation(6 a). Such an approximation may lead to an error of about ± 10 per cent on the

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1196 C. Moulin et a!'

absolute integrated desert reflectance for Meteosat-2. However, our calibrationprocedure considers the relative variation of the apparent reflectance as measured bythe various sensors. Using the sand reflectance model of Staetter and Schroeder(1978), we have verified that this error yields an uncertainty of about 4-5 per cent onthe intercalibration of the desert reflectance. Second, comparison between variouspublished spectral reflectances of desert soils shows a 3-4 per cent variability for theintegrated desert reflectance. This value can be considered as the uncertainty comingfrom our ignorance of the true spectral reflectance of each target. The errors describedin this section being independent, the total uncertainty is the quadratic sum of allerrors. The relative uncertainty on our calibration coefficients is ± 8 per cent rela­tively to the Meteosat-2 calibration given by Koepke (1983). Combined with the ownuncertainty of Meteosat-2 calibration, the total relative uncertainty is ± 13 per cent.

The main uncertainty on the offset comes from the lack of knowledge on theactual marine reflectance in the visible spectrum, which is highly variable dependingon dissolved and particulate matter contents and surface wind speed. Assuming thatthe integrated marine reflectance has to range between the reflectances given by themodel of Viollier (1980) and the model of Morel (1988), we found an uncertainty of± I NC on the offset numeric count.

4. Comparison with other results4.1. Brisson et a!' (/990)

Brisson et at. (1990) and Leborgne and Marsouin (1993) have performed anintercalibration between Meteosat-2 (gain 0) and other configurations. They use thecalibration of Koepke (1983) and observe the variations of the Meteosat numericcount over two desert targets (numbers 1 and 2 in table 2) during 12 years (1981­1990). They compare year-to-year monthly average values of the numeric count toobtain the evolution of the calibration. Because they aim at cloud determination, theirprocedure does not take into account the atmospheric effects due to changes inspectral bands between successive Meteosat sensors. The comparison between theirresults and ours is shown for each sensor in table 6; the calibration coefficients as wellas the drifts in sensitivity are relatively close. The main difference between the twocalibrations comes from the value of the offset numeric count. Indeed, Brisson et at.consider that NCo and the space numeric count are equal whereas we find significantdifferences for Meteosat-3 and -4 (see table 5). This induces only a small error for high

Table 6. Calibration coefficients: a comparison with Brisson et al. (1990). The relativecalibration represents the variation of the calibration coefficient in comparison withMeteosat-2. The total drift is the temporal evolution of the calibration coefficient overthe considered period.

Relative calibration (%) Total drift of the calibration (%)

Brisson BrissonSensor Gain et al. This work Period et a/. This work

2 0 Reference Reference 1984-87 +4 +3·52 1 -20 -14 1987-88 +0·63 1 -10 -6 1988-89 +13·84 4 +10 + 12 1990-91 +4·6 +4·2

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Calibration of Meteosat visible channel 1197

numeric count pixels and then small differences on the calibration coefficient. How­ever, this may strongly underestimate the calibrated radiances over seawater, andthen significantly bias the retrieved aerosol optical depth.

4.2. Kriebel and Amann (1993)Kriebel and Amann (1993) developed vicarious calibration experiments for

Meteosat sensors. They use an airborne comparison radiometer to perform coinci­dent measurements with Meteosat (same target, date, hour and viewing geometry).The comparison radiometer has a quasi-rectangular spectral response, between 0·4and 1·1 JIm, and a known calibration. The authors compare the radiance measured bytheir radiometer to the Meteosat numeric counts and deduce the calibration factors.However, the coefficients obtained are target-dependent because of the differences inspectral response for the radiometer and Meteosat and because reftectances of clouds,ocean, desert and forest have not the same spectral behaviour. We applied theformula given by the authors to our calibration coefficients, considering a perfectrectangular comparison radiometer:

(9)

where IX is the absolute calibration coefficient (see equation (2», k is the targetdependent relative calibration coefficient as given by the authors, S is the spectralresponse of the Meteosat sensor and L, is the spectral radiance at the top of theatmosphere. The numerator is equal to L'a1 (see equation (I»; the denominatorrepresents the radiance measured by the comparison radiometer. We calculated thetwo integrals using the fit of desert reftectances and the radiative transfer model 5S.Knowing IX, we then computed the relative calibration coefficient k for all our data forMeteosat-2 and -4 and we used a linear fit to deduce the value of k for the comparisonperiods (table 7). Differences between their data and ours are within 10 per cent forMeteosat-2 and -4; such an agreement is rather satisfactory considering the differencebetween the methods and the respective spectral bands used. We also computed arelative calibration coefficient of 0·91 for Meteosat-5 in case of a possible airbornecomparison campaign (Kriebel and Amann 1993).

Table 7. Calibration coefficients: a comparison with Kriebel and Amann (1993). k is thecalibration factor relative to a rectangular radiometer over desert targets.

Sensor

2245

Gain

oo45

Period

November 1981May 1982

August-September 1989February-December 1994

Kriebel andAmann

0·97\·061·08

This work

1·061·070·960·91

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1198 C. Moulin et al.

0.8-,-----------------,

.",. ~--.-......-"G 0.75 - • "',A,.e':" Jt.IIl - ""\IiZ 0..--."'" .'V . ~.

: .. ' .:." .. :,..: :;'''~:'';' ~:., ~:. "'!>" .,$;';" ., •• I, ,'L, .• ~~<l: '

.."i'~~;;x.: .. " . . . . . • . ~

. '::"':l-~':'" . -v.> • :~~'.:.':..... :~.! .. . .;"

';"...'"';'e 0.7-

~ L~~~~mo 0.65-

0.6a

Figure 6.

I

365Julian day since 1 January 1990

Meteosat-4 calibration coefficient a: a comparison with__ this work, --- Cabot et al. (1994).

730

Cabot et al. (1994).

4.3. Cabot et al. (1994)Cabot et al. (1994) use a model of desert soil directional reflectance and NOAA/

AVH RR observations of target 4 to calibrate Meteosat-4 over two years (1990-1991).Using the calibration of Kaufman and Holben (1993), they first determine theparameters of the bidirectional reflectance model of Rahman et al. (1993) forAVH RR visible plus near-infrared channels, after accounting for atmospheric con­tamination; they then combine AVHRR visible and near-infrared desert reflectances,normalized at a reference viewing geometry, to deduce the reflectance over the visibleMeteosat channel. Using a radiative transfer model, they compute the satelliteradiance and deduce the calibration coefficient by comparison with the measuredMeteosat numeric count. This yields a calibration coefficient varying between 0·74(early 1990) and 0·76Wm- 2 sr-! NC- 1 (late 1991), as compared to our values of0·66 for January 1990 and 0·69 for December 1991 (see table 4). Figure 6 evidencesthe rather good agreement between the two methods. Differences between thecalibration coefficients are about 10-12 per cent and the drift is found around 1'5-2per cent per year by both methods. The Cabot et al. (1994) intercalibration methodexhibits lower data dispersion, mainly due to the daily AVHRR coincident measure­ment of the desert reflectance of a single target instead of a fitted value for numeroustargets in our case.

5. ConclusionWe extracted the numeric counts of different desert and marine targets from 12

years (June 1983-December 1994) of Meteosat ISCCP-B2 images. We used five dayaverages to select only clear sky observations of desert pixels and yearly minimalvalues for marine pixels. Using the Meteosat-2 absolute calibration of late 1981 as areference for the first year of data (June 1983-May 1984), we computed the corres­ponding satellite radiances. We then derived the desert and marine reflectances of thetargets by modelling the atmospheric contribution with the help of a radiativetransfer model. Assuming that these reflectances were constant over the years andcorrecting the influence of Meteosat spectral band variations, we followed the inversepath for the next years; we computed the target and atmospheric radiances and we

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Calibration of Meteosat visible channel 1199

compared the retrieved satellite radiance to the extracted numeric count. We used thehigh reflectance desert targets to derive the calibration coefficient and its evolutionwith time (table 4) and the low reflectance marine targets to estimate the offsetnumeric count of the linear calibration (table 5). Uncertainties were found to be ± 13per cent for the calibration coefficient and ± I NC for the calibration offset. Thecalibration of the VIS channel of Meteosat-S was obtained with less than a year ofdata, and further calculations will likely improve the drift estimate. Our results weresuccessfully compared with other limited calibration studies from the literature. Testsshowed the importance of both spectral and atmospheric corrections in the pro­cedure. The calibration coefficients and their time evolutions obtained for the differ­ent visible Meteosat sensors will be used for quantitative studies of Saharan aerosoltransport since June 1983.

AcknowledgmentsWe thank K. T. Kriebel and F.-M. Breon for helpful comments and F. Cabot,

G. Dedieu and P. Maisongrande for providing us with their data. This work wasfunded by the Commissariat a l'Energie Atomique and the Centre National de laRecherche Scientifique. Some funding was provided by the French research pro­gramme "Erosion Eolienne en Regions Arides et Semi-Arides". C. E. Lambert andF. Dulac of the CFR would like to express their gratitude to the CEA for thecomputing facilities and the generous hospitality provided at the LMCE. This iscontribution number 1632 of the CFR and contribution number 289 of the LMCE.

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