METHODS FOR THE SURFACE REFLECTANCE RETRIEVAL FROM CHRIS/PROBADATA OVER LAND AND INLAND WATERS

Luis Guanter, Luis Alonso, and Jose Moreno

University of Valencia - Faculty of Physics, Dr. Moliner 50, 46100, Burjassot (Valencia), Spain.EMAIL: luis.guanter@uv.es

ABSTRACT

The Compact High Resolution Imaging Spectrometer(CHRIS) on board the Project for OnBoard Autonomy(PROBA) platform system provides the first high spa-tial resolution hyperspectral/multiangular remote sensingdata from a satellite system, what represents a new sourceof information for Earth Observation purposes. Whendealing with the retrieval of surface reflectance from suchkind of hyperspectral data, a radiative transfer approachis commonly preferred. However, since CHRIS 2003and 2004 data present reported calibration problems inseveral bands, especially in the nearinfrared region, astandard atmospheric correction based on radiative trans-fer models should not be performed. A dedicated at-mospheric correction algorithm for CHRIS/PROBA dataover land is presented in this work. It consists in thecombination of radiative transfer and empirical line ap-proaches to atmospheric correction, in order to retrievesurface reflectance images free from both the atmo-spheric distortion and artifacts due to miscalibration.The atmospheric optical parameters and the updated setof calibration coefficients are obtained jointly in an au-tonomous process, without the need for any ancillarydata. Results from the application of the algorithm toCHRIS/PROBA data from the two ESA SPectra bAR-rax Campaign (SPARC) held at the Barrax study site (LaMancha, Spain) in 2003 and 2004 are presented in thiswork, focusing on the validation of the final surface re-flectance using insitu measurements acquired simulta-neously to PROBA overpasses. Besides, the first ver-sion of an atmospheric correction module for inland wa-ters, which is currently under development, is also pre-sented, as well as the first results obtained from its appli-cation to data from the Rosarito reservoir. The potentialof CHRIS/PROBA data for Earth observation purposes isshown.

Key words: CHRIS/PROBA, Atmospheric correction,Surface reflectance, Radiative transfer, Empirical line,Calibration coefficients, SPARC campaign, Inland wa-ters.

1. INTRODUCTION

The CHRIS/PROBA system [1], provides high spatialresolution hyperspectral/multiangular data, what consti-tutes a new generation of remote sensing information tobe processed and exploited. On one hand, the PROBAplatform provides pointing in both acrosstrack and

alongtrack directions. In this way, the CHRIS/PROBAsystem has multiangular capabilities, acquiring up to 5consecutive images from 5 different view zenith angles(VZA). Each imaged target has an associated flybyposition, that is the position on the ground track when theplatform zenith angle, as seen from the target, is a min-imum. The platform acquires the images at times whenthe zenith angle of the platform with respect to the flybyposition is equal to a set of Flyby Zenith Angles (FZA):0, 36 or55. Negative FZAs to acquisition geome-tries when the satellite has already flown over the targetposition.

On the other hand, CHRIS measures over thevisible/nearinfrared (NIR) bands from 400 nm to1050 nm, with a minimum spectral sampling inter-val ranging between 1.25 (@400 nm) and 11 nm(@1000 nm). It can operate in different modes, com-promising the number of spectral bands and the spatialresolution because of storage reasons. The data we arepresenting in this work were acquired using operationMode1, with 62 spectral bands at a spatial resolutionof 34 m. CHRIS/PROBA images have an approximateswath of 15 km.

In those visible and NIR wavelengths, the atmosphericinfluence is strong enough to modify the reflected elec-tromagnetic signal. The main atmospheric species af-fecting the electromagnetic radiation in the visible andNIR spectral regions are ozone, aerosols, and water va-por. Those cause the loss or the corruption of part of thecarried information about the observed target. Thus, anyset of CHRIS/PROBA data needs for a previous removalof the atmospheric effects in the initial processing steps,to assure a maximal accuracy and reliability in the resultsinferred by the latter exploitation of the data. This is thefundamental basis of the atmospheric correction in opti-cal remote sensing: the elimination of the atmosphericeffects from the useful signal reflected by the observationtarget in the observers line of sight. A traditional state-ment of the problem can be found, for instance, in [2] or[3].

When dealing with accurately calibrated hyperspectralinstruments, such as the Airborne Visible/InfraRed Imag-ing Spectrometer (AVIRIS) [4] or the HyMap sensor[5], a radiative transfer approach is usually preferredto achieve the best results in atmospheric correction[6, 7, 8]. In those methods, radiative transfer codes areused to calculate the atmospheres optical parameters, inorder to remove the atmospheric contribution to the mea-sured atsensor radiances.

One of the most challenging issues in the radiative trans-

_____________________________________________________ Proc. of the 3rd ESA CHRIS/Proba Workshop, 2123 March, ESRIN, Frascati, Italy, (ESA SP-593, June 2005)

fer approach is the estimation of the concentrations of themain atmospheric species, which must be supplied to theradiative transfer code. The ozone concentration is quitewellknown, due to its low spatial and temporal vari-ability. An approximated value of ozone concentrationfor a given location can be obtained from several publicdatabases [9, 10]. However, the situation is very differentfor aerosols and water vapor, whose concentrations arehighly variable.

Therefore, in order to perform an accurate atmosphericcorrection, an estimate of aerosol and water vapor con-tents simultaneous to image acquisition is required. Thecommon procedure for hyperspectral data uses associatedalgorithms for the retrieval of water vapor column con-tent in a pixel by pixel basis [11, 12, 13]. In the case ofaerosols, default climatology values are usually assumedfor the aerosol optical thickness, although some methodsfor the retrieval from high spatial resolution hyperspectraldata have been presented [14, 15]. All of these methodslie on sophisticated physical models, for which the relia-bility of the sensors spectral calibration is fully required[16].

When such accuracy in the instruments spectral calibra-tion is not assure, alternative methods should be usedto retrieve surface reflectance with a minimum error. Awidely used approach in those conditions is the empiricalline method [17, 18]. In its most general implementation,the empirical line method is based on the calculation ofcalibration coefficients from linear correlations betweenthe radiances registered by the sensor for some referencepixels with surface reflectance spectra measured insituin the same points. The spectral uniformity of the target,its low temporal evolution and the availability of one ormore bright pixels are assumed. In this way, not onlythe atmospheric distortion is removed, but also the ar-tifacts due to spectral calibration problems. Therefore,this approach is likely to produce better results in the at-mospheric correction of those instruments where calibra-tion deficiencies are expected. Two main shortcomingscan be highlighted: one is the dependency on simulta-neous ground measurements, which are seldom availablein usual processing chains. The other, the fact that theangular dependencies in the target are hardly taken intoaccount, since most of the insitu measurements are ac-quired from a nadir view. Those causes make standardempirical line methods can not be applied to series ofmultiangular CHRIS/PROBA imagery. Instead, the ra-diative transfer approach would be preferred.

However, since the CHRIS/PROBA system was designedas a technology demonstrator, limited resources were de-voted to the construction of a highprofile sensor. Forthis reason, CHRIS presents some miscalibration trendsall over the covered spectral region [19], being the un-derestimation of the signal in the NIR wavelengths themost important one. Even though updated sets of cali-bration coefficients are planned for 2005 CHRIS/PROBAdata releases [20], the spectral calibration of 2003 and2004 data does not allow using complex radiative transferalgorithms for the retrieval of atmospheric constituents(especially water vapor) and surface reflectance. In or-der to illustrate this fact, sample surface reflectance spec-tra which would be obtained by a common radiative

transferbased algorithm are plotted in Fig. 1 (thin line).The thick one corresponds to the results obtained by themethod presented in this paper, where the calibration is-sue is addressed. One can notice the large miscalibrationof CHRIS data in the NIR region, which leads to theloss of the typical plateau shape expected for vegeta-tion targets in those wavelengths, and the improvementsachieved by means of the update of the calibration coef-ficients.

Figure 1. Comparison between sample surface re-flectance spectra obtained from a common atmosphericcorrection method (thin line), based on a radiative trans-fer approach, and the methodology presented in this pa-per (thick line).

In this framework, a dedicated atmospheric correction al-gorithm for CHRIS/PROBA data over land has been de-veloped. The idea is combining both the radiative transferand the empirical line approaches, in order to derive theappropriate atmospheric parameters and a set of correc-tion factors for CHRISs gain coefficients altogether. Oneof the strongest points of the method is that it works in afully automatic manner, without the need for any groundbased atmospheric or surface reflectance ancillary infor-mation. The fundamental basis lies on a multiparameterinversion of the TopOfAtmosphere (TOA) radiancesfrom 5 reference pixels with a high spectral contrast, giv-ing as a result the estimations of aerosol optical thickness(AOT) and water vapor content, as well as the calibrationcoefficients. All of these variables are used to derive thesurface reflectance image afterwards.

In this paper we give a full description of the algo-rithm, as well as the results obtained from its applicationto data from the two ESA SPectra bARrax Campaigns(SPARC)[21]. Insitu reflectance measurements taken si-multaneously to CHRIS/PROBA acquisition have beenused in the validation of the atmospherically correcteddata, finding a good agreement. Moreover, some analysisof the directional features in the reflectance of several tar-gets will be performed, in order to show the potential ofCHRIS/PROBA data for monitoring Earths surface pro-cesses.

Moreover, a prototype of an atmospheric correctionmethod for inland waters is being designed too. The de-scription of its current state is also given in the paper. Thefirst results of water leaving reflectance are encouraging,after comparison with insitu measurements.

2. METHODOLOGY FOR LAND TARGETS

2.1. Fundamental basis

The algorithm is based on the combination of princi-ples of the empirical line and the radiative transfer ap-proaches: an empirical linelike procedure is used to cal-culate an updated set of CHRISs gain coefficients for theradiance measurements, while a common radiative trans-fer approach is followed after in order to retrieve surfacereflectance from TOA radiances.

It must be remarked that the calibration coefficients donot provide surface reflectance, as it would be the caseof an empirical line method, but are only used to correctthe miscalibration trends found in the radiance measure-ments. Thus, the similarity of the algorithm presentedin this work with standard empirical line methods is thederivation of linear correlations from several pixels withsome spectral contrast to find calibration coefficients, butthese are not used to retrieve surface reflectance here. Forthe retrieval of this surface reflectance, AOT and watervapor content estimated jointly with the calibration coef-ficients are used as an input in a radiative transfer codeto calculate the atmospheric optical parameters for the 5angular configurations of CHRIS/PROBA acquisitions.

For those estimations, the atmospheric state is consideredinvariant within the area covered by the image, which isquite realistic for CHRIS/PROBA images, approximatelysquare with a swath around 15 km. Surface reflectance isexpressed as a linear combination of two vegetation andsoil spectra, which act as artificial endmembers. Then,aerosol and water vapor contents, as well as the abun-dances of vegetation and soil, are retrieved simultane-ously from 5 pixels inside the image, by means of a mul-tiparameter inversion of the TOA spectral radiances

One might think that a better strategy would be perform-ing an iterative procedure in which the calibration coef-ficients are calculated firstly, and the retrieval of atmo-spheric parameters is done using the calibrated radiancesas a second step of the method. However, since coeffi-cients and atmospheric parameters are calculated jointlyat first, they are slightly coupled. This means that there

would not be much difference between the atmosphericparameters retrieved in both steps, increasing the compu-tation time in addition.

2.2. Inversion procedure

The inversion is performed by minimizing a Merit Func-tion 2 specifically designed for this problem,

2 =

5

pix=1

i

1

2i

[

LSEN |pix,i LSIM |pix,i

]2

(1)

whereLSIM is the TOA radiance simulated with the ra-diative transfer code,i corresponds to the center of theiband, given inm units, in the particular band configura-tion of the sensor, andLSEN stands for the TOA radiancemeasured by the sensor. The equivalent TOA apparentreflectanceSEN is given in terms of the TOA radiance,the solar constantEsc and the cosine of the solar zeniths angle by

SEN =LSEN

sEsc. (2)

The Merit Function in Eq. 1 is weighted by2i to drivethe inversion towards the smaller wavelengths, where theeffect of the aerosols is bigger, while the reflectance ofmost of the natural surfaces is lower.

For aerosol characterization, the Standard Radiation At-mosphere (SRA) types defined by the Radiation Com-mission of the International Association of Meteorologyand Atmospheric Physics (IAMAP) [22], dustlike, wa-ter soluble, oceanic and soot, are implemented in the 6Sdatabase. So, aerosols are specified by means of the AOTat 550 nm and the percentages of those basic types, re-sulting in 4 parameters. Thus, the free parameters to becalculated in the optimization are the water vapor columncontent, the aerosol optical thickness at 550 nm and thepercentages of basic types, and the proportions of vege-tation and soil for each of the 5 reference pixels.

The 6S (Second Simulation of the Satellite Signal in theSolar Spectrum) radiative transfer code [23] was cho-sen for the atmospheric simulations. The MODTRAN4code [24], instead of 6S, is generally used in such hy-perspectral processing, because of the rigorous treatmentof scattering and absorption processes in the radiativetransfer simulations, the accurate coupling between theatmosphere and the directional properties of surface re-flectance, and its high spectral resolution of up to 1cm1.However, the much faster 6S code was chosen, since theaccuracy differences between the codes can be neglectedwhen compared with the commented calibration prob-lems.

For the CHRIS Mode1 band configuration, the bandsinside the oxygenA absorption band are not considered,due to the difficulties in the computation of such a narrowand strong absorption. Moreover, CHRIS presents somecalibration problems in the extremes of the detector focalplane array. The first band is also discarded, and the sameis done for wavelengths larger than 0.9m.

The 5 reference pixels must have as spectral contrast aspossible (ranging from pixels with high green vegetationcontent to high bare soil content) in order to find a max-imum sampling for the linear correlation in the calcula-tion of calibration coefficients, with low and high radi-ance points. Moreover, the spectral contrast is helpful toachieve a maximal decoupling between atmosphere andsurface contributions to TOA signal. A perfect choicefor the set of reference pixels would be a pure vegetationpixel, a pure bare soil pixel, and three intermediate ones,mixture of vegetation and soil. The particular selection of5 as the number of pixels to serve as a reference for theretrievals is a balance between the computation burdenand the number of points for the calculation of the coeffi-cients: a higher number would increase the computationtime, without adding too much information to the sam-pling. In the operative procedure, the selection is basedon the definition of three categories of land pixels, us-ing theNormalized Difference Vegetation Index(NDVI)[25] calculated from TOA reflectances. Pure bare soilpixels are those with a NDVI value between 0.01 and 0.1,mixed pixels are between 0.1 and 0.45, and pure vegeta-tion pixels have a NDVI in the range 0.450.7. To assurea maximum spatial sampling within the window, the ref-erence pixels are selected randomly from each one of thecategories.

For the construction of the simulated TOA radianceLSIM , CHRIS bands are reproduced using Gaussian fil-ter functions, even though the binning (superpositionof several narrow bands to constitute a band with theincreased width) in the shorter wavelengths makes theshape more rectangular. The surface is assumed to beLambertian, what leads to the expression

LSIM (s, v, ) = tg

[

L0 +

sEsc

T T s1 Ss

]

, (3)

where tg is the transmittance due to gases;L0 is theintrinsic atmospheric radiance, also called atmosphericpath radiance;s is the surface reflectance;S is the spher-ical albedo, reflectance of the atmosphere for isotropiclight entering it from the surface;v is the cosine of theview zenith angle; is the relative azimuth between theSun and viewing directions;T , T are, respectively,the upward and downward total atmospheric transmit-tances (for diffuse+ direct radiation), in the illuminationand observation directions. The angular dependencies oftg, L0, T andT have been omitted for the sake ofsimplicity.

The surface spectral reflectance is given by the lin-ear combination of two artificial endmembers of typicalgreen vegetation and soil spectra,

s = Cvveg + Cssoil Cv,s > 0, s [0, 1] (4)

The proportions of vegetation and soil are allowed to belarger than1.0 in case spectra brighter than the endmem-bers were present. The 10 coefficientsCv,s, 2 for eachof the 5 pixels, are also free parameters in the TOA ra-diance simulation. The endmembers role is to provide areflectancebasis for the construction of TOA radiances,but not reproducing real targets present in the scene. Thatis why the term artificial is used. As a result, theCv,s

coefficients are not real abundances, but effective abun-dances of the endmembers. The ideal case would bethe availability of an algorithm for the estimation of end-members free from the atmospheric influence, but no onehas been found. Anyway, the hypothesis that any com-mon land pixel can be represented by this kind of linearcombination seems to work well in the reduced spectralrange covered by CHRIS, where most of the targets spe-cific absorption/reflectance characteristic features can notbe identified. Further effort will be needed for the exten-sion of the method to sensors covering the 0.42.5mrange.

Neglecting the directional effects in the target reflectanceprovides a simple formulation of the radiative transfer,what leads to an important decrease in the computationtime and modelling effort. It has been demonstrated thatthe Lambertian approach can work well in the generalcase where the acquisition geometry is not in the retrodispersion hot spot direction [26], and so if the obser-vation is close to nadir. Thome et al.[27] state that thepercentage difference between the Lambertian case andtypical nonLambertian cases is less than 1% in the nearnadir viewing range. So, in order to reduce the errorsassociated to this approach, the atmospheric retrievalsare performed from the image acquired from the viewzenith angle closer to nadir (in PROBA nomenclature,FZA equals to0), and the corresponding atmospherictransmittance and reflectance functions in Eq. 3 neededto derive the surface reflectance are calculated then foreach one of the 5 acquisition geometries. In any case, theCv,s coefficients partially account for directional prop-erties, as the effective abundances of vegetation and soilthey represent vary with the observation angle.

The minimization of the Merit Function in Eq. 1 is per-formed by the Powells Minimization Method [28], basedon a 1D minimization separately in each direction of theparameters space, without the need for the analytical ex-pression of the function derivatives. An appropriate ini-tialization of the Powells algorithm is needed in order toreduce the convergence time and to reach the best mini-mum. Default climatology values are used for the atmo-spheric species, while a strong correlation between theNDVI and the coefficientsCv,s was found from severalsimulations.

2.3. Calculation of calibration coefficients

Once the minimization of the Merit Function has beenperformed, the resulting modelled and real TOA spectraare used in order to derive the calibration coefficients ineach of the CHRIS bands. Since fails in CHRIS calibra-tion are expected to happen in the gain coefficients, ratherthan in the dark current, a linear regression with the inter-cept set to 0 is considered,

LSIMi = AiLSENi (5)

beingAi the updated calibration coefficient for the TOAradiance in the channelith. The 5 points obtained fromthe 5 reference pixels are fitted by means of a leastsquares algorithm. With the aim of discriminating thosepixels where the assumption of the linear correlation

worked worst, the linear regression is weighted with aCHI square value accounting for the goodness of the fit.

In this way, a set ofAi coefficients is calculated fromthe CHRIS/PROBA image acquired with the minimumVZA. Since calibration coefficients are intrinsic to thesensor, and not angledependent, the same set of coef-ficients is applied to the 5 images acquired from 5 anglesin the same overpass. The validity of this assumption willbe shown later.

2.4. Surface reflectance retrieval

Once the calibration coefficients are obtained, the 5 TOAradiance images provided by the sensor are recalibrated.After, the calculated atmospheric functions are used to re-trieve the surface reflectance images. As a starting point,a Lambertian reflectance for the surface is assumed, whatleads to the analytical inversion of Eq. 3 to retrieves.An initial surface reflectance image is obtained with a lit-tle algebra.

This is an important justification for the Lambertian as-sumption. However, some authors have pointed out thatthis approach may lead to noticeable errors in some par-ticular combinations of geometry, target reflectance andatmospheric conditions [29, 30]. The problem is thatmultiangular information is needed in order to character-ize properly the directional effects in the target. In thecase of platforms with multiangular viewing capabilities,such as PROBA, accounting for the directional effects inthe target reflectance is feasible.

Then, although the general procedure is based on theLambertian assumption also for CHRIS/PROBA, a fur-ther step involving directional effects was been done forsome pixels in the image. It performs an iterative scheme,in which directional effects are corrected in each of thesteps. A detailed description of the procedure is givenin [31]. It has not been implemented in the operationalalgorithm because of several reasons, such as the pro-hibitive computation time, the error associated to thegeometric correction or the low overlapping proportionfound for the 5 CHRIS/PROBA images in some dates.However, tests have been made for some pixels in theCHRIS/PROBA images, extracted visually from the cen-ter of some targets (alfalfa, corn and bare soil) in the over-lapped region, to avoid the problems with the geometriccorrection. Results for these sample show that the errorsarisen with the Lambertian approach are smaller than a8% in the worst cases, FZA= 55 and corn crop. Inthe other angles, FZA= 0,36, the average errors arearound a 3%. Although those results justify the Lamber-tian assumption considered here, a more intensive anal-ysis of the directional effects in the atmospheric correc-tion of CHRIS/PROBA data should be done using a largerdata set, covering a wide range of solar zenith angles andrelative azimuth positions, in order to assess to what ex-tent directional effects must be considered in the atmo-spheric correction of multiangular data.

The final step in our atmospheric correction algorithm isthe removal of the image blurring caused by those pho-tons reflected by the target environment and scattered by

the atmosphere particles into the sensors line-of-sight.This effect is calledadjacency effect, because the appar-ent signal at the TOA for a given pixel comes also fromthe adjacent ones.

We follow the simple formulation proposed by Vermoteet al. [29], which is based on the idea of weighting thestrength of the adjacency effect by the ratio of diffuse todirect groundtosensor transmittance:

s = us +

td(v)

e/v[us

u], (6)

where s the final surface reflectance,us is the sur-face reflectance before the adjacency treatment, output ofthe complete atmospheric correction algorithm,td(v),e/v the transmittances for diffuse and direct radiationin the ground to sensor path, andu is the average of theenvironment reflectance. This average is calculated for a1 1 km2, which is in the same order of the aerosol cou-pling scale. In the present case of CHRIS Mode1, thismeans a window of around30 30 pixels.

A flow chart describing the whole atmospheric correctionprocess is displayed in Fig. 2. Summarizing, it starts withthe extraction of the 5 reference pixels from the FZA= 0

image, which is made according to an initial classifica-tion of candidate pixels based on NDVI thresholds. TheAOT, water vapor content and calibration coefficients areretrieved simultaneously by means of the inversion of theTOA radiances in the 5 reference pixels. The coefficientsare used to update the radiance values in the set of 5images acquired in the same CHRIS/PROBA overpass,while AOT and water vapor are inputs in the calculationof the atmospheric optical parameters for the 5 angularconfigurations. The analytical inversion of Eq. 3 and theremoval of adjacency effects are the last steps in the re-trieval of the final surface reflectance images.

TOA Radiance

FZA=0

AOT, Water

vapor column

Calibration

coefficients

tg, 0, T , T , S

in 5 angular

configurations

Calibrated TOA

radiance images

(5 angles)

1) Selection of reference pixels2) Inversion of TOA radiances

4) Application of calib. coefficients

5) Inversion of Eq. 3 6) Correction of adjacency

Surface reflectance images (5 angles)

3) 6S forward runs

Figure 2. Flow chart describing the main steps for thesurface reflectance retrieval.

3. METHODOLOGY FOR INLAND WATERSTARGETS

In the case of inland waters pixels, the particular perfor-mance of CHRIS in the Mode2 configuration (optimizedfor the observation of water bodies) makes a differentapproach must be followed in the retrieval of the atmo-spheric parameters. Concretely, the use of land pixelsshould be avoided for Mode2 data [20], due to the satu-ration found for surface albedos higher than around 25%.Thus, the methodology discussed previously for Mode1is not applicable to Mode2 data.

According to the particular spectral response of water tar-gets, characterized by very low reflectances in the NIRwavelengths, the water vapor absorption feature centeredaround 940 nm cannot be used to estimate the atmo-spheric water vapor content. However, since the watervapor absorption is only residual in the rest of the spectralrange covered by CHRIS, we shall assume a default valuefor the integrated column. Neglecting water vapor vari-ations is not a critical issue, since Mode2 channels arelocated in wavelengths out of water vapor absorptions.

For aerosols, the situation is completely different, as theirinfluence on the radiation is maximum in the visible re-gion, where most of the absorption features due to waterpigments happen. The main idea is to perform an iter-ative procedure that seeks for the AOT that minimizesthe subsequent water reflectance in CHRIS bands 2 and3 (centered around 440 and 490 nm), where the aerosolscattering is maximal, with the physical constraint thatthe reflectance has to be positive. The first band, cen-tered in 410 nm, is avoided because of the high noiselevels detected. No recalibration procedure is neededfor this Mode2: because the ratio signaltonoise is lowin the NIR bands, no miscalibration can be detected inthem. The pixel used for the inversion is a representativepixel selected from the middle of the reservoir. The useof clusters of pixels is foreseen for future versions of themethod, to account for instabilities caused by noise in theinstrument or fluctuations in water composition.

Once the AOT has been estimated, the surface reflectanceimages are obtained from TOA radiance ones using theprocedure described in section 2.4. First results of thismethodology are shown in section 5.

4. RESULTS FOR LAND TARGETS

4.1. CHRIS/PROBA Data Available from SPARCcampaigns

The SPARC campaigns [21] were held in the Barrax(39.05N, 2.10W, La Mancha, Spain) CHRIS/PROBAcore site in July 2003 and 2004, as part of the PhaseA Preparations for the Surface Processes and Ecosys-tem Changes Through Response Analysis (SPECTRA)mission [32]. The Barrax site is a flat continental areawith an average elevation over the sea level of around700 m. There is a big contrast in natural surfaces, rangingfrom large homogeneous vegetation fields (e.g. alfalfa

and potatoes crops) to large dry bare soils. The crops inthe area were classified previously, so the comparison be-tween insitu surface reflectance measurements and theatmospherically corrected data is feasible for differentland uses.

Four CHRIS/PROBA data sets over the Barrax area areavailable (acquired in Mode1, 62 bands and 34 m spatialresolution), two for each year. For the SPARC 2003 cam-paign, acquisitions were made on the 12th and the 14thJuly. The situation over Barrax on those days was par-ticularly favorable, because PROBA almost passed over(-4 across-track zenith angle) on 13th July, and then onthe 12th July (+20 across-track zenith angle) and on 14thJuly (-27 across-track zenith angle). Unfortunately, theimage from 13th July was not correctly taken because ofsatellite pointing problems, so we have had only two im-ages from the campaign. Concerning SPARC 2004, twodata sets were also acquired, on 15th and 16th July. Nev-ertheless, the system failed on the first date, and only 3of the 5 images for 15th July were recorded. The obser-vation angles for each of the two days and the two yearsare plotted in Fig. 3. It must be remarked that all theimages have been initially corrected from dropouts andstriping in the first pre-processing step, and geometricallycorrected afterwards.

Despite the failures in the acquisitions, the resulting database, with 4 different dates, turned out to be enough forthe validation exercise, as well as to show the capabili-ties of the CHRIS/PROBA data to represent the particu-lar spectral and angular features of different targets. Theresults obtained from the application of the algorithm tothose images and the validation with insitu measure-ments will be discussed next.

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Figure 3. Acquisition geometries and illumination anglesfor the images of (a) SPARC 2003, and (b) SPARC 2004.Labels indicate the corresponding nominal FZA of theclosest symbols.

4.2. Inversion of TOA radiances and retrieval ofcalibration coefficients

Starting with the inversion of TOA radiances, Fig. 4shows the fits obtained by the minimization of the MeritFunction in Eq. 1, for 2 of the 5 selected reference pixelsin the image from the 12th July 2003. It may be notedthat, although there is a general overlapping between the

TOA CHRIS/PROBA radiance spectra and the modelledcurves, noticeable discrepancies exist in some bands.

Dashed circles show the wavelengths where the disagree-ment is a maximum. The bands in the edges of the spec-tral range are among them. The first band presents im-portant noise levels, as expected in those channels in theextremes of sensors compounded of focal planes arrays.Some spikes appear around 0.5m, what might be ex-plained by a possible spectral shift in the band centerwavelengths in such spectral region, or by the abrupt tran-sition caused by the different number of CCD rows in-tegrated in bands 6 (5 CCD rows) and 7 (4 rows) [20].Finally, the NIR region is affected by a systematic over-estimation of the TOA signal, as it can be checked bycomparing with the simulated spectra. Similar trends arefound in the rest of data sets, both in 2003 and 2004.

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Wavelength ( m)

Ra

dia

nc

e (

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-2m

-1sr-

1)

Alfalfa CHRIS

FIT

Figure 4. Fit of 1 of the 5 reference pixels for the deriva-tion of the calibration coefficients , as well as aerosolsand water vapor retrieval, corresponding to the FZA= 0

image of 12 July 2003. Dashed circles point the wave-lengths with major deviations.

The resultingAi coefficients calculated from the 4 datesof CHRIS/PROBA acquisitions are plotted in Fig. 5. Agood agreement is observed. The largest deviations arefound in the wavelengths centered around 0.94m, whatmight be explained by the important perturbation due towater vapor. However, some degradation in the instru-ment seems to have been found in addition, because theshapes of the curves are coincident by pairs, with similartrends in the dates corresponding to the same year, either2003 or 2004. Anyway, the temporal stability of CHRISgain factors has been assessed to a large extent, whichis a good proof on the instrument reliability, despite thefact that the CHRIS/PROBA system was launched onlyfor technology demonstration purposes.

Moreover, the calibration is more important in the NIRwavelengths, with coefficients reaching a factor 2. Thecorrection in the first band is important as well. This con-firms the a priori expectations, because the focal plane ar-ray is designed to have the best radiometric quality in thecentral wavelengths, getting worse as the wavelength ap-proaches both edges of the spectral domain. Besides, theunderestimation in the measurements in the NIR wave-lengths is detected and corrected here. Both the gen-eral shape and the levels of the recalibration curve are invery good agreement with the one provided by Sira Tech-nology Ltd, the company on charge of the design of theinstrument, calculated from engineering arguments [19],

Figure 5. Calibration coefficientsAi calculated for the 4dates of CHRIS/PROBA SPARC acquisitions.

Figure 6. Calibration coefficients calculated for the 5 an-gles of the same overpass on the 12th July 2003.

which confirms the good performance of the methodol-ogy presented here.

Even though the calibration coefficients are retrievedfrom the image acquired from the minimum VZA, FZA=0, and applied to the rest of angles afterwards, the ro-bustness of the calculated coefficients to angular varia-tions must be checked. The sets of coefficients calculatedindependently from the 5 angles in the overpass on the12th July 2003 are displayed in Fig. 6. It can be seenthat the calibration curves are nearly angleindependent,as the different curves are almost overlapped. This rein-forces the validity of the coefficients as universal coeffi-cients to be applied in a vicarious calibration of the rawTOA data.

Regarding the retrieval of atmospheric parameters, wa-ter vapor contents of 1.11, 1.23, 1.52 and 1.68gcm2

were obtained for the 12th and 14th 2003 and the 15thand 16th 2004, respectively. There is a high correla-tion with the 1.4, 1.6, 1.9 and 2.1gcm2 values measuredby radiosoundings launched simultaneously to PROBAoverpasses in both campaigns. However, it is difficultto achieve a better accuracy due to the remarked miscalibration. The most important thing here is that thisvalues are consistent with the calibration coefficients cal-culated simultaneously, what is needed to lead to reliablevalues of the subsequent surface reflectance. Commonvalues of 0.2, 0.25, 0.17 and 0.28 were calculated for theAOT at 550 nm in the same dates.

4.3. Results for surface reflectance

The surface reflectance after the complete atmosphericcorrection for several pixels is shown in Fig. 7, altogetherwith field measurements. The insitu reflectance mea-surements were taken with an Analytical Spectral De-vices (ASD) FieldSpect Pro FR Spectroradiometer (foot-print around 0.8 m, 2 nm of spectral resolution). Thesemeasurements were acquired almost simultaneously tosatellite overpasses (time differences were always smallerthan 30 minutes) from a nadir view while walking acrossthe target, integrating in one spectrum all the measure-ments taken for every path of around 10 m. The reasonfor this is assuring that most of the natural variability inthe target could be reproduced. The grey stripe gives themean value and the standard deviation calculated fromall the acquisitions made for the same target, to provideinformation on the spatial variability of the target.

The agreement between the ASD spectroradiometer andCHRIS data is relatively good, both in the shape and inthe reflectance levels, what validates the methodologypresented here. It must be taken into account that thefield spectra were acquired from a nadir view, not coinci-dent with any of the PROBA view angles, so small devi-ations due to angular trends are expected a priori. This isconfirmed by the fact that the maximum agreement withthe insitu measurements is found for the minimum viewzenith angle angle. Thus, since we are comparing differ-ent observation angles, a detailed error study has not beendone, considering the visual comparison enough in orderto check the good performance of the atmospheric correc-tion method. The fit of the resultant multiangular surfacereflectance information to a parametric reflectance modelcan provide the necessary interpolation to the nadir viewfor an accurate statistical analysis.

Nevertheless, it can be stated that the comparison is fairlygood in the case of the uniform Lambertian alfalfa targets,and also for the sunflowers crop. In this case, though, alarge natural variation was found, due to the crops con-sisted of plant rows separated by bare soil. The agreementreinforces the integrating procedure used in the groundmeasurements. For bare soils the directional effects aremore important, what makes larger deviations are foundin some targets. Finally, the bad comparison with the drywheat crop might be explained by the practical difficul-ties in the measurement process, arisen because of thedensity and height of the plants.

Surface reflectances for the 5 views of the same point in 4different targets are plotted in Fig. 8. An accurate analy-sis of the directional effects appearing in those plots is outof the scope of this work, but some expected features canbe noticed easily. It can be stated that the magnitude ofthe directional effects varies with the nature and verticalstructure of the target, according to the behavior predictedby the radiative transfer theory. In order to quantify thoseangular differences, a functioni is defined as the stan-dard deviationi of the surface reflectance in the 5 anglesfor thei channel, normalized by the mean valuesi :

i =i

si(7)

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- Cab = 50.17 g/cm-2- LAI = 3.73- Cw = 0.0145 g/cm-2- Cm = 0.0036 g/cm-2- N = 1.5

Figure 9. Results of SAIL/PROSPECT simulations usinginsitu measurements of the biological parameters for thealfalfa crop and the geometrical configuration plotted inFig. 3.

According toi the alfalfa crop, consisting of uniformdense short canopies, is closer to the Lambertian behav-ior than the vertical tall (around 2 m height) corn canopy,where the angular effects are more important. Calculat-ing the mean values of for two spectral regions, in thered (10 bands, from 604 to 688 nm) and in the NIR (10bands, from 804 to 900 nm), we obtainred = 0.10and NIR = 0.06 for the alfalfa, whilered = 0.15,NIR = 0.10 for the corn. Something similar occurs be-tween the bare soil and the dry wheat targets: althoughthe spectral response is quite similar, directional effectsare more intense in bare soils, as the wheat cover con-sists of a very dense canopy, almost uniform, that tendsto make isotropic the angular response due to multiplescattering processes (red = 0.08 andNIR = 0.05 forthe wheat, whilered = 0.12, NIR = 0.09 for the soil).

Since no insitu multiangular measurements were avail-able, the consistency of the directional behavior regis-tered by the CHRIS/PROBA data has been checked usinga newer coupled version of the SAIL and PROSPECTmodels [33] for simulating the reflectance of a real al-falfa crop. We have selected the alfalfa crop because ofits 2D uniformity, what makes it the prototype of crop tobe modelled with the SAIL/PROSPECT model. Besides,all the inputs needed (chlorophyll content, LAI, watercontent and dry matter content) were explicitly measuredduring the SPARC 2004 campaign. Results for the 5 an-gles in Fig. 3 are plotted in Fig. 9. The input values for themodel are shown in the legend. It can be stated that boththe spectral shape (Fig. 9) and the angular dependencies(Fig. 10) are highly coincident with those showed for thealfalfa crop in Fig. 8, what confirms the validity of theprocedure described in this work, as well as the potentialof CHRIS/PROBA to reproduce the reflectance trends ofreal surface, both in the spectral and angular domains.

5. RESULTS FOR INLAND WATERS TARGETS

CHRIS/PROBA Mode2 images acquired over theRosarito reservoir, located in Northern Spain (40.12N,5.27W) have been used in this study. Results obtainedfrom the atmospheric correction of the image of 20 May2004 are shown in Fig. 12. The acquisition angles for

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Figure 7. Sample surface reflectance spectra for different targets, compared with insitu measurements taken with an ASDspectroradiometer. Plots in the left column come from SPARC2003 data, and those in the right one from 2004.

Figure 10. Projection of the alfalfa simulations in Fig. 9on the view zenith angle (VZA), for 2 wavelengths in thered (663 nm) and in the NIR (861 nm) regions.

this date are plotted in Fig. 11. The 5 views of the samepoint in the Rosarito reservoir and the corresponding insitu reflectance spectrum measured simultaneously to theCHRIS/PROBA acquisition are plotted. Insitu measure-ments come from the Spanish Center for HydrographicStudies (CEDEX), in the frame of its activities to vali-date algorithms for the monitoring of water quality usingremote sensing data [34].

It has to be remarked the relatively high agreement be-tween CHRIS data and the insitu spectrum, taking intoaccount the low signal arriving at the sensor from wa-ter targets and that the field measurement angle is notcoincident with any of the CHRIS/PROBA observationangles. Nevertheless, the closest spectrum to the nadirASD measurement is again corresponding to the mini-mum VZA, as expected. This validates both the qualityof CHRIS Mode2 data and the aerosol retrieval and at-mospheric correction procedures implemented for inlandwaters.

On the other hand, the large directional behavior expectedfor water bodies is found in CHRIS/PROBA data: eventhough acquisition angles are not in the principal solarplane, forward and backward scattering regions can be

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clearly predicted in Fig. 11. The maximum reflectanceshould be expected in the specular reflection region, typ-ical in polished surfaces such as calm water bodies. Theclosest angle to the ideal specular position in the geo-metrical configuration of the Rosarito scene of 20 May2004 is the one labelled by +36. The maximum of re-flectance for this angle can be observed in Fig. 12.

Moreover, the angular dependency of the registered re-flectance can be found not only in the mean reflectancevalue, but also in the composition variability inside thereservoir. While the Rosarito reservoir looks very uni-form when viewed from one direction, noticeable struc-tures with different tonalities can be seen when it is ob-served from another one. An example is displayed in

Figure 12. Comparison of CHRIS water reflectance spec-tra for the 5 different observation angles plotted in Fig. 11with an insitu measurement acquired simultaneously on20 May 2004.

Fig. 13, for the +36 and 36 angles. Several spectrahave been extracted randomly from all over the reservoir.Large differences can be detected in the +36 angle,while the 36 appears as much more uniform. There-fore, one observation angle may turn out to be more use-ful than the other with the aim of water quality analy-sis. Besides, multiangular information is very useful inthe substitution of those pixels mostly affected by thesunglint effect in any of the observation angles.

6. CONCLUSIONS

Two different methodologies for the atmospheric correc-tion of CHRIS/PROBA data have been presented. A ma-jor part of the paper has been devoted to the description ofan atmospheric correction of CHRIS/PROBA data overland. In addition, the current state of an atmospheric cor-

Figure 13. Sample of Rosarito reflectance spectra, ex-tracted randomly for different pixels across the image, fortwo different observation angles.

rection algorithm for the atmospheric correction of inlandwaters targets has also been described.

Concerning the atmospheric correction algorithm forCHRIS/PROBA data taken over land, a detailed descrip-tion of the algorithm steps has been introduced in thiswork, as well the results obtained from its application toCHRIS/PROBA data from the SPARC 2003 and 2004campaigns. The performance of the method has beenassessed using insitu measurements from those cam-paigns.

Since important calibration problems have been reportedin several CHRIS channels for 2003 and 2004 data, astandard radiative transfer approach to the atmosphericcorrection can not be applied. Indeed, any processingmethodology for CHRIS/PROBA should deal with theupdate of CHRIS gain coefficients as an important issue.The method presented in this work retrieves simultane-ously a set of calibration coefficients and the atmosphericoptical parameters for the 5 acquisition geometries. Itworks in a fully automatic way, without the need for anyancillary data, neither atmospheric parameters nor sur-face reflectance.

The algorithms fundamental basis lies on the inversionof the TOA radiances from 5 reference pixels with a highspectral contrast. The 5 fits of modelled radiances to realones are used to calculate the calibration coefficients, bymeans of the linear regression of the resulting 5 pointsin each spectral band. The AOT and water vapor columncontent are also retrieved in the inversion. Those con-

tents are the inputs to the radiative transfer code in thecalculation of the atmospheric optical parameters for the5 CHRIS/PROBA acquisition geometries. The spectralcontrast of the reference pixels allows the separation ofatmosphere and surface contributions to TOA radiances,as well as a proper sampling of radiances to calculate thecalibration coefficients. Once the 5 TOA radiance im-ages are calibrated, surface reflectance is obtained fromthem using the calculated atmospheric parameters in the5 angular configurations. Finally, adjacency effects areremoved from the images.

The calculated sets of calibration coefficients showed thehigh temporal stability of the instrument, without toomuch variation in the response between 2003 and 2004years. Moreover, the spectral shape of the derived curvescompares very well with the one calculated from en-gineering assumptions by CHRIS operators, which en-hances the reliability of the presented methodology. Fi-nally, the robustness to angular variations in the retrievedcoefficients is also a good sign of the method consistency.

Concerning surface reflectance retrievals, SPARC 2003and 2004 data have been used to validate the method. Re-flectance field spectra acquired with a spectroradiometerhave been compared with CHRIS/PROBA atmospheri-cally corrected data, showing a good agreement both inthe spectral shape and the reflectance levels, althoughsmall deviations because of different observation anglesare expected. The capability of CHRIS/PROBA data toreproduce the directional properties of a vegetation crophas also been assessed. Due to the absence of multian-gular field measurements, those have been simulated bymeans of a coupled version of the PROSPECT and SAILmodels. Real data of the needed biophysical variableshave been used as an input, leading to a good agreementwith CHRIS/PROBA results.

The potential of CHRIS/PROBA to reproduce real spec-tral and angular trends in land targets has been shown, aslong as the appropriate processing techniques are used.Nevertheless, further analysis of the limitations of theLambertian approach in the atmospheric correction ofCHRIS/PROBA data is foreseen. Also, the applicationof the method to the analysis of the quality of 2005 data,in which the instruments spectral calibration issue hasbeen revisited.

Finally, with respect to inland waters targets, a short de-scription of the algorithm which is under developmenthas been given. First results from the processing of onedate of acquisition are shown. The comparison withinsitu measurements of waterleaving reflectance arepromising, although further validation with other datasets is still needed.

ACKNOWLEDGMENT

This work has been done in the frame of the ESASPARCProject, contract ESTEC18307/04/NL/FF. The first au-thor (LG) acknowledges the support by a PhD grant fromthe Spanish Government, Ministry of Education and Sci-ence. The authors also want to thank M. Cutter from Sira

Technology Ltd. for his assistance with CHRIS technicalissues, and to R. Pena, J. A. Domnguez and A. Verdu forthe provision of inland waters data.

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