normalization of satellite imagery

18
This article was downloaded by: [The University of Manchester Library] On: 18 December 2014, At: 22:53 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tres20 Normalization of satellite imagery HONGSUK H. KIM a & GREGORY C. ELMAN b a Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center , Greenbelt, Maryland, 20771, U.S.A b Ressler Associates Inc , 8419 Oak Stream Drive, Laurel, Maryland, 20708, U.S.A Published online: 27 Apr 2007. To cite this article: HONGSUK H. KIM & GREGORY C. ELMAN (1990) Normalization of satellite imagery, International Journal of Remote Sensing, 11:8, 1331-1347, DOI: 10.1080/01431169008955098 To link to this article: http://dx.doi.org/10.1080/01431169008955098 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

Upload: gregory-c

Post on 12-Apr-2017

213 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Normalization of satellite imagery

This article was downloaded by: [The University of Manchester Library]On: 18 December 2014, At: 22:53Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

International Journal of Remote SensingPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tres20

Normalization of satellite imageryHONGSUK H. KIM a & GREGORY C. ELMAN ba Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center , Greenbelt,Maryland, 20771, U.S.Ab Ressler Associates Inc , 8419 Oak Stream Drive, Laurel, Maryland, 20708, U.S.APublished online: 27 Apr 2007.

To cite this article: HONGSUK H. KIM & GREGORY C. ELMAN (1990) Normalization of satellite imagery, International Journal ofRemote Sensing, 11:8, 1331-1347, DOI: 10.1080/01431169008955098

To link to this article: http://dx.doi.org/10.1080/01431169008955098

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in thepublications on our platform. However, Taylor & Francis, our agents, and our licensors make no representationsor warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Anyopinions and views expressed in this publication are the opinions and views of the authors, and are not theviews of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should beindependently verified with primary sources of information. Taylor and Francis shall not be liable for any losses,actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoevercaused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Normalization of satellite imagery

INT. J. REMOTE SENSING, 1990, VOL II, No.8, 1331-1347

Normalization of satellite imagery

HONGSUK H. KIM

Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center,Greenbelt, Maryland 20771, U.S.A.

and GREGORY C. ELMAN

Ressler Associates Inc., 8419 Oak Stream Drive,Laurel, Maryland 20708, U.S.A.

(Received 1 February 1989; infinalform 27 June /989)

Abstract. Sets of Thematic Mapper (TM) imagery taken over the WashingtonDC metropolitan area during the months of November, March and May wereconverted into a form of ground reflectance imagery. This conversion wasaccomplished by adjusting the incident sunlight and viewangles and by applying apixel-by-pixel correction for atmospheric effects. Seasonal colour changes of thearea can be better observed when such normalization is applied to space imagerytaken in time series. In normalized imagery, the grey scale depicts variations insurface reflectance and tonal signature of multi-band colour imagery can bedirectly interpreted for quantitative information of the target.

1. IntroductionRemote sensing of the Earth's surface in the visible spectral region is a reflectance

problem and a space-borne imager is designed to measure the properties of theground material to absorb and reflect the incident sunlight. Such properties areconsidered inherent to the composition of the material and structure and are thereforea measurable physical parameter for information gathering. Thus the groundreflectance, if correctly derived, will become an important physical parameterequivalent to the surface temperature in the thermal infrared imagery. However, themeasurable reflectances from a satellite are a combined sum of the atmospheric andsurface reflectances. For example, a set of statellite coverage of an urban area taken atdifferent times of the year is shown in figure I. The three Thematic Mapper (TM)band-4 (TMjB-4) images of the Washington DC area, acquired on 2 November 1982,24 March J984 and 26 May 1985, display different shades of brightness as each scenewas taken under different sunlight illumination angles and atmospheric conditions.

Even though nearly two decades have elapsed since the initiation of NASA'sLandsat programme, the problem of subtracting the atmospheric albedos has notbeen solved. Thus satellite data are still distributed in raw radiance form of theupwelling light which reaches the sensor aperture.

The problem of the atmospheric effects on space imagery has been addressed sincethe early 1970s. Many studies have reported that the atmospheric constituents, fromthe ground level to 80 km height, influence the radiometric quantization of visiblespectral channels of satellite imagery (Horvath 1970, Herman et al. 1971, Hermanand Browning 1975, Fraser et al. 1977). The relative contribution of aerosol scatteringand absorption on the effect of satellite measurements of surface reflectance was

0143-1161/90 $3.00 © 1990 Taylor & Francis Ltd

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 3: Normalization of satellite imagery

1332 H. H. Kim and G. C. Elman

Figure I. TM/B-4 imagesof the Washington DC area in uncorrectedform. Different shadesof brightnesscan be seen as each image was taken under different sunlight illuminationanglesand atmospheric conditions. A, 2 November 1982; B, 24 March 1984; C, 26 May1985.

analysed by Fraser and Kaufman (1985). Also, it was recently shown that jhepresence of aerosol not only modifies the spectral characteristics of surface featuresbut also diffuses the spatial resolution of imagery (Otterman and Fraser ·1979,Kaufman 1979, 1984, Mekler and Kaufman 1980, Diner and Martonchik 1983,1985 a, b). Solutions correcting these effects have been suggested by Kaufman (1985,1988) and Diner and Martonchik (1985).

Interestingly, the problem of this aerosol effect has been also studied in oceancolourimetry, where the primary interest is to apply space imagery for remote sensingof oceanic parameter analysis and interpretation of the colour of the ocean (Gordon1978, Kim 1979, Sturm .1.983). Atmospherically corrected satellite images are rout­inely available for studying phytoplankton concentration and the diffused attenu­ation coefficients of water. However, ocean .colourimetry represents a special case inwhich the reflectances from vast expanses of the ocean are quite small, as they seldomexceed' I°per cent. Under these conditions, the effect of aerosols only adds to the totalradiance perceived by the satellite sensor. The atmospheric correction algorithms inuse today are based on a single-scattering model in which the Rayleigh componentand aerosol scattering contribution are computed separately and the portion ofvisible radiation equivalent to the amount of aerosols detected by a near-infraredchannel (where the reflectance is zero) is subtracted from the total radiance.

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 4: Normalization of satellite imagery

Normalization ofsatellite imagery 1333

2. Removal of atmospheric effects2.1. Earth-atmospheric radiance

In this paper, the problems of many discordant elements of Earth/atmosphere willbe simplified into a simple land scenario in order to address the essentials of thetransfer processes in the Earth-atmosphere.

Figure 2 depicts an imager viewing a span of bridge from the top of theatmosphere. The radiance which reaches the aperture consists of the following.

(a) Radiance scattered from the Sun into the sensor's field of view without beingreflected by the surface. This portion of light is considered totally atmosphericin origin and denoted as L(atm) in equation (I).

(b) The second portion oflight deals with that reflected by the bridge and directlytransmitted through the atmosphere. This component, given in the secondright-hand term of equation (I), actually yields remote information about thesurface.

(c) Next, consideration is given to the portion of light which emanates fromadjacent pixels and strays into the scanner's field of view. Although thissegment of light is not expected to have significant effect on the outcome ofnormalization of space imagery, aliasing caused by neighbouring pixels caninfluence the quality of imagery. For instance, as the radiances reflected fromthe adjoining bridge surface and water scatter into the field of view due to-theatmosphere scattering, the contrast between the bright bridge surface anddark water will be decreased to some extent.

An analytical expression for the total radiance which reaches the satellite sensor,L(tot), can be given as

L(tot, 0, rp)= L(atm, 0, rp)+FopT(r)/(I-sp) (I)

where 0 and rp are the zenith and azimuthal angles of the directions of sunlight.propagation; p is the ground reflectance; F ° is the downwelling solar flux at the

Figure 2. A schematic of the atmosphericeffects showingthe reflected sunlightwhich reachesthe sensor aperture. Although the sensor is designed to view only the hatched areawithin' the field of view, radiation emanating from adjacent pixels as well as theatmosphere also reaches the sensor.

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 5: Normalization of satellite imagery

1334 H. H. Kim and G. C. Elman

ground; T is the transmissivity of the light from the surface to the sensor and isdefined as

T=exp(-r/sec()) (2)

where r is the optical thickness of the atmosphere; and s is the reflectance of theatmosphere for the upward flux from the ground. For brevity, all L terms arenormalized with respect to F, .the extra-terrestrial solar flux.

L = nJ.',«(), rp)/cos «()o)F

where J.',«(), rp) is the absolute spectral radiance. The reflectance, p, is defined as

p =nL(O, rp, 0, rp.)/F 0

(3)

(4)

The reflective properties of many terrestrial targets are expected to display non­Lambertian behaviour and so the phase angles of the incident sunlight and viewer, 0,and </Iv> need to be defined. Also note that if the p of equation (1) approaches zero,L(tot) becomes close to totally atmospheric in origin. Further elaboration of L(atm)gives

L(atm) = L(Ray) +L(aerosol) (5)

Usually the portion of radiance due to molecular scattering, L(Ray), and gaseousabsorptions are satisfactorily accounted for, but the effect of the aerosols, L(aerosol),which influences the optical characteristics of spectral signature due to scattering andabsorption, can be in a wide range and result in loss of classification accuracy.

A more quantitative description of the extent of aerosol effects on the totalradiance is presented in figure 3. In this simulation, L(tot) ofTM/B-l is plotted as a

RADIANCE AT THE TOP OF THE ATM1485nm SZA=42' AZI~ 100' VIEW ANG=30'1

90 <f­8070 UJ

U60 Zo «---1-----o U \o ~ CRITICALo ~ REFLECTANCE

a:

~-r-

@-, I

51---- --- --- - -- --- 4"

3

I 2100

~

lJl,;tE<.J<,

~ 40EUJ 30UZ« 2015«a: 10

o0.89 0.60 0.43 0.26 0.13 0.02

ATM T(Mie)

Figure 3. Upwelling radiance at the top of the atmosphere (485 nm, SZA =42°, AZI = 100°,view angle =30°) is plotted as a function of ground reflectance for a range ofatmospheric haziness. According to such a simulation, there exists a ground reflectancepoint at which the total upwelling to the top of the atmosphere will not be influenced bythe aerosol loading of the atmosphere.

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 6: Normalization of satellite imagery

Normalization ofsatellite imagery 1335

function of ground reflectances and for a range of r(Mie) values from 0·02 to 0·89.The figure shows that the effect of the aerosol is such that the presence of the aerosolsadds to the total radiance as the sensor views relatively dark objects with p < 20 percent. However, when bright objects are viewed, the net effect is reversed as the netamount of total radiance actually decreases. For instance, for a lower boundaryreflectance of80 per cent, the net upwelling radiance of the Earth-atmosphere systemis at about 26mWcm- 2 jlsr- 1 for an optical thickness of 0·01 atmosphere, whereasfor a r(Mie) of 0·8 the net amount is smaller, at about 22 mW em - 2 jl sr " 1. Owing tothis dichotomy, there exists a ground reflectance point at which the upwellingradiance is almost invariant to the aerosol loading of the atmosphere. This pheno­menon has also been noted by Fraser and Kaufman (1985) and reported as the criticalreflectance point.

In addition to the above contrast loss due to intensity modifications, theatmospheric scattering has the effect of diffusing the boundaries between a non­uniform surface of high and low reflectances. Such aliasing was reported by Ottermanand Fraser (1979) and Mekler and Kaufman (1980). Diner and Martonchik(1985 a, b) were able to give a more quantitative account of the effect, as theydemonstrated that the computation of such effects caused by adjacent pixels and theatmosphere, in terms of its response to surface variations of varying spatial scales, canbe expressed in the spatial frequency domain. For example, their computation showsthat the influence of a high-albedo pixel at about 60 per cent reflectance to a low­albedo neighbouring pixel of about I per cent level can be as large as +/- 3 per centwhen observed from a height of 5 km under an optical depth of O'3 atmosphere.

In order to accommodate this adjacency effect, the second term of the transferequation (I) is separated into direc. and diffuse components (Kaufman 1,985 a). Thetarget radiance can also be separated as

L(dir)=FOp(dir)T(r,dir)/(I-sp)(6)

Likewise,

L(dif)=FOp(dif)T(r,dif)/(I-sp)

By combining the two, the total process can be given as

L(tot) =L(atm) +Fo[p(dir)T(dir) +p(dif)T(dif)]/(1 -sp)

(7)

(8)

2.2. Correction algorithmThe correction algorithm being developed here assumes that the present knowl­

edge of the atmosphere is sufficiently good that modelling with appropriate para­metrization will yield reasonably accurate radiance values. The approach is, inessence, an inversion method where a modelled atmosphere which best fits theimage scene is being sought after via a trial-and-error method. Measured data, avail­able in the form of pixel digital counts (ON), are matched with the numericalresults. When an atmosphere which fits the empirical data is-found, a simplified radi­ance model of the atmosphere is applied to pixel-by-pixel construction of a groundreflectance image.

Our image-processing algorithm adapts the outputs of an atmospheric radiativetransfer code, known as the Dave code (Dave 1972). Even though a number of otherradiative transfer codes are available, the Dave code is used because the code is not

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 7: Normalization of satellite imagery

1336 H. H. Kim and G. C. Elman

only conveniently structured to accept \l range of atmospheric input parameters, butalso its computational results can be tapped at many different levels of computation(Fraser et al. 1977, Fraser and Kaufman 1985, Kaufman 1979, Kim et al. 1980, 1982).

The Dave code, which was originally developed to account for the visible andultraviolet radiation in planetary atmospheres, computes the intensities of thescattered radiation emerging at any level of a plane parallel at the lower end to aLambertian surface of known reflectivity. The basic transfer equation given byChandrasekhar (1960) is computed for all values of phase before proceeding to thenext successive layer using Gauss-Seidel iteration procedure. The computations ofsuccessive layers are performed until the values merge and subsequent iterationbecomes almost meaningless. The net effect is a model which incorporates multiplescattering and absorption functions.

The original format of the code lists the up- and downwelling radiances atdifferent levels of the atmosphere for a given solar zenith angle and groundreflectance. This form of output is not well suited for Landsat data analysis, as theground surface is made up of a wide range of individual pixels which exhibit localvariations in reflectance. Thus a new output format, more convenient for satelliteimage analysis, was devised so that the radiance of a given atmosphere and incidentsolar angle is listed according to sensor look angles and ground reflectances as shownin table I (Kim 1988).

Since space images are usually collected as scans are made in the directionperpendicular to the motion of the spacecraft, the output's off-nadir observations arematched to represent the cross-track scans and the Y axis to point along thespacecraft ground track. In order to store this look up table in more convenient formto be invoked for pixel-by-pixel corrections, the N by 10 matrix of table I (N is thenumber of pixel elements in a scan line) is first reduced to N by 3 dimensions via thematrix operation of the following.

The matrix given in table 2 is linearized as

A, +A2(PI)+A 3(PI)2 PI (PI)2

Al +Aip2)+A 3(P2)2 P2 (P2)2

Al +Aip3)+Aip3)2 P3 (P3f

Al +A2(P4)+A 3(P4)2 P4 (P4)2 Al

Al +A2(Ps)+A 3(PS)2 Ps (pS)2

[Y] = Al +A 2(P6)+A 3(P6)2 P6 (P6)2 A2 (9)

Al +A2(P7)+A 3(P7)2 P7 (P7)2

A, +A2(Ps)+Aj(pS)2 Ps (pS)2 A3

A I + A 2(P9) +A 3(Pg)2 I pg (pg)2

Al +A 2(PIO)+A 3(P,O)2 PIO(P,O)2

where PI to P,O take the values of 0 to 60 per cent. The pertinent matrix operations tosolve for [A] are given as

[Y]=[X][A]

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 8: Normalization of satellite imagery

Table 1. Radiance at the top of the atmosphere, (Listed as a function ofground reflectance and view angle). TM band-4 (wavelength = 0-844 Jlm)- Solarzenith angle = 59°_ Viewer's azimuthal angle = 132°_ r(Mie) = 0-1044; r(Rayleigh)=0-018; r(Ozone)=0-008_ Aerosol density: n=9-011 x 10· crrr'of columnar volume, Reflectance is given in fractions. ~

-e

Reflectance :;"~

View 0 0-05 0-1 0-15 0-2 0-25 0-3 0-4 0-5 0-6 "5'"0 0_01371 0-05463 0-09579 0-13719 0-17883 0-22071 0-26284 0-34783 0-43384 0-52088~2 0-01389 0-05481 0-09597 0-13737 0-179 0-22088 0-26301 0-348 0-43401 0-52104 IS4 0-01408 0-05499 0-09615 0-13754 0-17917 0-22105 0-26317 0-34816 0-43415 0-52118

'"6 0-01427 0-05518 0-09633 0-13771 0-17934 0-22121 0-26332 0-34829 0-43428 0-52129 S;8 0-01447 0-05537 0-09651 0-13788 0-1795 0-22136 0-26346 0-34842 0-43438 0-52137 '"10 0-01467 0-05556 0-09668 0-13805 0-17965 0-2215 0-26359 0-34852 0-43446 0-52142 §-

12 0-01488 0-05575 0-09686 0-13821 0-1798 0-22163 0-2637 0-3486 0-43451 0-52144 "'"14 0-01508 0-05593 0-09703 0-13836 0-17993 0-22174 0-2638 0-34867 0-43454 0-52143 '"~

wW--.J

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 9: Normalization of satellite imagery

1338 H. H. Kim and G. C. Elman

Table 2. Second-order best fit coefficients of the upwelling radiancegivenin'table 1.

Reflectance dependence

AI A2 A3

View-angle a l 0·0137365727 bl 0·8155001765 c, 0·0495301479dependence a2 0·0001514759 b2 - 0·000 1011062 C2 0·0000005987

a3 -0,0000055486 b3 0·0000030653 C3 - 0·0000003497

Let

[M] = [XY[X]

[A] = [Y][M]-I[Xy

(10)

Upon the completion of reducing from ten-dimensionality, the N-dimensionalityof the view angle dependence is also reduced to second-order best-fit coefficients,eventually ending up with the nine quadratic coefficients given in table 2. In thismanner an Earth-atmosphere system is effectively stored in a 3-by-3 matrix andimage processing is carried out as the 3-by-3 matrix is invoked for every pixelcorrection in solving the quadratic equation (II).

(II)

(12)

where

A, =a 1 +a2(O.)+a3(OY

A 2 =b , +b2(O.)+b 3(OY

A3=cl + c2(O.)+C3(O.)'

For every image scene, several r (Mie) atmospheres are created and stored in equation(12) form.

2.3. Selection of r (Mie) and aerosol size distribution parametersOne of the pertinent parameters necessary in carrying out the atmospheric

correction process is a measure of haziness or aerosol optical thickness, r(Mie), andthe size distribution patterns of the aerosol in the atmosphere. Since a prioriknowledge of r(Mie) is not available, a technique to deduce such haze levels from theimage itself has to be devised.

The first constant, A, in equation (11), can be rewritten to include the r(Mie)parameters;

(13)

where Lo is the intensity of upwelling radiance for a Rayleigh sky with no surface.feedback. In figure 4, equation (13) is used to plot the upwelling radiance and itscorresponding reflectance for several r(Mie) atmospheres. A pixel with knownground reflectance can serve as a convenient data point for inferring an effective r(Mie) of the scene from figure 4. In the absence of ground-truth measurement data, areasonable approach is to search for a pixel or pixels which presumably have zeroreflectance. As recognizable dark pixels, water surface in the red or near-infrared

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 10: Normalization of satellite imagery

Normalization ofsatellite imagery

+2.0%

1339

if~ +1.0%

UZ

~Uw-'u.wII:

ClZ:::>aII:o

-1.0%

_ 85TM DATA_ 841M DATA

- 821M DATA

, , , , ,005 0.10 ·0.15 0.20 0.25

r(Mie)

Figure 4. Pixels with minimum digital numbers (DN) from a dark surface are used forinferring a r(Mie) of the atmosphere. The DNs read from 1982, 1984 and 1985

TM/B-4 data determine a range of r(Mie) and p that can be assumed.

region has been commonly accepted (Gordon 1978, Kim et af. 1980). Plant canopy inthe blue-green channel has also been suggested as a likely candidate for such apurpose (Kaufman 1985). However, our scrutiny of TM data indicates that pixelswith true zero reflectance are difficult to identify.

In table 3, a list of averaged digital counts taken from various sources of potentialdark pixels such as inland water bodies, including the Chesapeake Bay and a golfcourse, are presented. Also included are sets of the smallest ON readings found fromhistogram analysis of each scene. Frequently the pixels with the lowest DN werefound in shaded areas. For instance, a fountain in front of the Capitol building wasfound to be the source of the lowest radiance pixels in the near-infrared channel in1985 data. And a totally shaded football field (RFK stadium) was the darkest spot inbands 2 and 3 in 1984. Indications are that these ON are real and so modelling shouldtake into account these numbers. .

Table 3 also shows that the average values of water bodies or greenery can varywidely and that no single class of surface features can be accepted as a reliable sourceof zero reflectivity. For example, the count of 10·5 obtained by averaging a 9-by-9

Table3. Digitalcounts of differentwater bodiesand other dark pixel sources read from 1982,1984 and 1985 TM/B-4 scenes.

November 1982 March 1984 May 1985

Smallest DN 5 4·3 6Chesapeake Bay 9·4 6 10·5

(averaged)Tidal Basin 10 10-4 20·1Potomac River 10 14·2 18·4WashingtonChannel 10 9 19

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 11: Normalization of satellite imagery

1340 H. H. Kim and G. C. Elman

matrix of adjacent pixels from Chesapeake Bay from 1985 data far exceeds theminimum digital count of 6 observed from several isolated pockets in the DC scenery.Usually a large oceanic water body, such as Chesapeake Bay, has been acceptedas a convenient zero reflectance source. An atmospheric correction based on thisapproach would create negative reflectances for some pixels due to overcompen­sation of the aerosol content. For this reason a histogram was taken for each scene.Additional measures were also performed to examine the origin of the darkest pixelfound in the data.

The three solid lines of figure 4 are r(Mie) against p plots for DN of 4, 5 and 6obtained from TM/B-4 images of 1982, 1984 and 1985. The DN of 6 from 1985 dataintercepts an atmospheric r(Mie) of 0·07 for p = O. In table 4, those values whichappear to be the best suited r(Mie) and v· for the 1982, 1984 and 1985 images arelisted. In selection of v·, the Jungean exponent of the aerosol size distribution, ourmodel assumes that the aerosols are spherical particles with a refractive index withrespect to air of ',5 and have an imaginary refractive index of 0·01.

In modelling, transmission through aerosols is computed for aerosol size distri­butions given by the Jungean power law (Bullrich 1962)

dn(r) =Cr-'· d(log r) (14)

where n is the number density of aerosol particulates and r denotes their radius. A v·value of 3·4 was used for 1982 and 1984 data. In 1985, the atmosphere above theDistrict of Columbia (DC) was measured with a ten-channel transmissometer on theday of Landsat 5 overpass, 26 May 1985 (R. S. Fraser, personal communication). Theplot ofr(Mie) for multiple bands (shown in figure 5) indicates that a better fit Jungeanparameter would be about 3·8. The optical thickness determined by this methodgenerally agrees with the visibility being reported by the National Weather Service atthe time of TM overpass.

Once these atmospheric parameters are set, each scene is processed via thealgorithm which invokes the 3-by-3 matrix of the atmosphere which best fits the scenefor pixel-by-pixel correction.

2.4. Correction of adjacency effectsThe final phase of the correction involves adjusting the derived ground reflectance

Table 4. Correction parameters applied to TM data.

CentreWavelength November March May

(nm) 1982 1984 1985

SZA (degrees) 59 46 30Viewer azimuthal angle

(degrees) 131 140 151Aerosol size parameter v' 3-4 3-4 3-8Aerosol density (cmt ') 4·22 2·32 9·01Columnar volume(108

)

r(Mie) for band I 485 0·16 0·09 0·27r(Mie) for band 2 589 0·12 0·07 0·19r(Mie) for band 3 660 0·11 0·06 0·16r(Mie) for band 4 844 0·08 0·04 0·10

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 12: Normalization of satellite imagery

Normalization ofsatellite imagery 1341

900800700600500

0.1

0041-I --- FIELD TRANSMISSOMETER

-CALCULATED FOR v' = 3.8~CALCULATED FOR v' = 304-0-0- CALCULATED FOR v' = 3.0

WAVELENGTH (nm)

Figure 5. r(Mie) measurement data from a field transmissometer used on the day of Landsat­5 overpass are shown along with r(Mie) points of different aerosol size parametersapplicable for 1985 TM/B-4 data.

for the adjacency effects caused by neighbouring pixels. The reflectance term, p(tot),from equation (8) can be rewritten as

p(dir) = K lP(tot) - K 2P(dif) (15)

where K, and K 2 respectively denote 1{tot)jT(dir) and T(dif)jT(dir). p(dif) is thereflectivity of adjacent pixels including pixels which are not within the field of view butinfluence the radiance. If the sphere of influence extends to N pixels, the valuebecomes

p(dif) = I (NiP(tot)JN,) (16)

Note that p(tot) is the derived reflectance from step (I) and K 1 and K2 are obtained bymodelling. In figure 6 (A, B, and C) a step-by-step processing of the atmosphericeffect correction of TMjB-4 image including the adjacency effect correction isdemonstrated for a close-up view of an area which encompasses the Potomac River,Tidal Basin and Pentagon complex. The image B pertains to the diffuse backgroundcomponent of image A and in essence it is the adjacency effect which degrades theresolution. Subtraction of this background image with appropriate parameters, K 1

and K 2, will generate a true surface reflectance image in C.

3. Applications of corrected imageryThe measurement of surface reflectance from the same area through time can

provide valuable information to the image interpreter. In figure 7 (A, B, and C),multi-band ground reflectance images of the DC area in Fall and early and late Springare presented. The false-colour composite was created by assigning customary blue,green and red (BGR) guns to bands 1,3, and 4 respectively. Correction parametersapplied to the images are listed in table 4. The images presented here differ from otherfalse-colour TM images in that the values from each scene can be directly comparedto portray quantitative information of the targets. There is a similarity in the generalcolour tone between the 1982 and 1984 scenes. The areas of vegetative surface duringthe month of November and early March appear to have approximately the same leaf

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 13: Normalization of satellite imagery

1342 H. H. Kim and G. C. Elman

Figure 6. Adjacency effectcorrection of TM/B-4 is shown in steps: (A) is a derived ground­reflectance image from initial conversion of equation (3); (B) pertains to the diffusecomponent, p(dif), givenin equation (16);and (C) is the finalproduct as the correctionprocedure is completed.

area. However, the 1985 image is set apart from the others. In late May, vegetation ofthis region reaches its peak green.

Furthermore, the grey scale can be directly translated into reflectances depictingvariations in ground reflectance. In table 5, the reflectances of various urban targets,retrieved directly from the corrected imagery, are listed along with the measuredalbedos of similar targets reported by Brest and Goward (\ 987). It is of note that theground reflectances listed in table 5 are not of hemispherically integrated albedos butof sun- and view-angle-defined non-Lambertian surfaces. Also, it should be pointedout that the samples selected are of homogeneously small areas, except for the cityblocks given in the last row. A certain element of heterogeneity is involved as the largearea of downtown city blocks is expected to contain numerous side w'iilk trees.

An examination of table 5 reveals interesting facts about changing surfacereflectance in a metropolitan area. Firstly the overall landscape varies seasonally dueto the changes associated with the presence of vegetative areas. Also, transientchanges, such as the Potomac's high sediment load during spring, account for thehigh albedo of the Anacostia river in 1984. Anomalous changes caused by man'sactivities have also been detected. Most urban targets are relatively immune toseasonal changes, as the class of features such as asphalt and concrete are notexpected to change seasonally. Thus their reflectances are inherent to the materialcomposition and the data acquired in time series can be used for reconnoitering. Forinstance, there was a sudden change in the reflectances of the Pentagon South ParkingLot in 1985 data, a drop from 12·3 to 8·5 per cent in Band-4 (table 5). This was firstspotted as the 26 May 1985 image was superimposed on the 23 March 1984 imageafter geometrical and atmospheric corrections. Our ground truth revealed that theobserved change was caused by repaving of the southern half section of the northPentagon parking lot during the winter of 1984-1985. Repavings and large excav­ations are frequently sighted man-made changes when two time-lapsed reflectanceimages of the same area are superimposed for comparison.

Table 5 also demonstrates the bi-directional reflectivity of city blocks as men­tioned by Brest and Goward (1987). Even though concrete buildings are not expectedto change their reflectance seasonally, less than perfect Lambertian reflectance of thebuildings introduces bi-directionality displaying increasingly large reflectance as the

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 14: Normalization of satellite imagery

Normalization ofsatellite imagery 1343

Table 5. Retrieved reflectances of various urban targets from corrected imagery.

Reflectance (per cent)

TM/Data Source Year Band-I Band-2 Band-3 Band-4

Chesapeake Bay (Averaged) 82 2·2 2·6 2·4 2·184 4·3 2·1 0·7 0·385 2·9 2·0 1·1 0·9

Potomac River 82 \·4 4·1 0·9 2-484 5·9 8·4 5·2 2·985 4·5 5·0 1·9 3·2

Tidal Basin 82 1·4 3·\ 0·2 2-484 4·8 6·3 1·5 1·985 4·3 4·8 3-8 3·1

Golf Course 82 2·9 5·5 2-8 33·7(E. Potomac River Park) 84 4·7 6·3 2·3 32·4

85 4·8 6·7 2·5 46·7

City Blocks (Averaged) 82 4·6 6·5 4·1 11·0(DC Down Town) 84 7-8 9·0 6·2 12-4

85 8·8 9·9 8·9 17-9

Concrete Roof 82 \6·8 19·4 15·1 26·7(Kennedy Center for Arts 84 23·4 24·9 20·3 32·4& Performances) 85 21·3 22·5 22·1 33·6

Asphalt Surface (I) 82 6·1 6·9 5·6 10·5(Pentagon N. Parking Lot) 84 8·7 9·0 5·2 10·1(Northern Half) 85 9 8·8 7·1 11·2

Asphalt Surface (2) 82 9·2 10·8 8·3 \·6(Southern Half) 84 8·9 9·7 6·2 12·3

85 7·2 6·7 5·0 8·5

Hartfort Calibration Targets(Brest and Goward 1987) MSS/Band-4 Band-7Tar 3·5 HGravel (I) 7 9·5Gravel (2) 26·6 33·3Asphalt (I) 6·2 7·7Asphalt (2) 8 9·2Concrete 23·5 23·6

incident sunlight angles become high. The p of the Kennedy Center for PerformingArts building and city blocks of the downtown DC show such behaviour. Forinstance, the average reflectances of downtown city blocks increased as much as 60per cent as the sun elevation angle changed from 30° to 60°. Such bi-directionalreflectance behaviour is thought to be related with the size of building shadows beingcast by the Sun.

4. DiscussionAlthough the retrieved reflectances in table 5 have not been verified with ground­

based measurements, our results appear to be in fairly good agreement with surfacealbedo measurements reported by Brest and Goward (1987). The authors find that avery limited amount of qualified surface reflectance data is available for comparison.

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 15: Normalization of satellite imagery

1344 H. H. Kim and G. C. Elman

(0)

(b)

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 16: Normalization of satellite imagery

Normalization ofsatellite imagery

(e)

1345

Figure 7. Normalized images of Washington DC; (A) in Fall; (B) in early Spring;and (C) inSummer.

This is due, in part, to the difficulty of comparing ground-based field measurementtechniques to the measurements of space-borne TM. This is because traditionalground-based field measurements rely on point measurements to represent samples oftotal purity, whereas TM averaged over a 30 m pixel.

On a positive note, our study indicates that the range of error one might induce inderiving reflectances will not be seriously affected by a slightly incorrect r(Mie) input.Computations show that an uncertainty of 0·1 in r(Mie) will result in an uncertaintyof O'7 per cent in reflectance when applied to the low albedo of water. This level ofuncertainty diminishes when the general albedo of targets reaches 10 to 40 per cent. Inpractical terms, one may expect a reflectance error of0·7 per cent for every error of0·1unit in r(Mie) in ocean colourimetry, but such a high level of uncertainty is less likelyfor terrestrial targets.

There is, however, much room for improvement in this methodology, especially inthe method of inferring an effeective r(Mie) of a scene. Present procedure calls for ahistogram analysis of the entire image followed by investigator's verification toascertain the validity of the dark pixel source. The authors believe that such processeswill eventually be automated in the future as the data base of surface reflectancesbecomes more extensive.

Finally, it should be noted that this method of implementing a best-fit matrix toadapt an atmospheric model will work only if the radiative transfer model being

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 17: Normalization of satellite imagery

1346 H. H. Kim and G. C. Elman

adapted is valid. Even though the Dave code is accepted by many, applications ofother radiative transfer codes are also being considered for comparison. Themathematical entity of the best fitting is accurate within I per cent---only poor fits areat extreme high and low ends of the reflectance range.

In summary, the technical goal of this study was to develop a practical method­ology to derive surface reflectance corrected for atmospheric scattering and absorp­tion. In so doing, the authors have been able to demonstrate, for the first time, a time­series set of normalized TM data. The ultimate aim of this research is to havenormalized imagery more readily accessible and improve the prospect of applyingsatellite data for much wider public acceptance. For instance, standardized cropimagery, provided yearly, would be much more acceptable to ordinary farmers inassessing the status of crops.

AcknowledgmentsThe authors would like to acknowledge the following individuals at Goddard

Space Flight Center who have contributed in making this study possible: DrsR. S. Fraser and H. Montgomery for helpful discussions on scientific aspects of thestudy, Mr L. Stuart for providing the TM data sets and Mr W. Kovalick for helpfulsuggestions in developing the image-processing algorithms.

ReferencesBREST, C. L., and GOWARD, S. N., 1987, Deriving surface albedo measurements from narrow

band satellite data. International Journal of Remote Sensing. 8, 351-367.BULLRICH, K., 1964, Advances in Geophysics, Vol. 10 (New York: Academic Press), pp. 99-260.CHANDRASEKHAR, S., 1960, Radiative Transfer (New York: Dover Publications).DAVE, J. V., 1972, Development of Programs for Computing Characteristics of Ultraviolet

Radiation Technical Report Scalar Cases A-D, IBM Palo Alto, NAS Contract 5-2168.DINER, D. J., and MARTONCHIK, J. V., 1983, Atmospheric transfer of radiation above an in­

homogeneous non-Lambertian reflective ground-I. (Computational Consideration andResults), Journal of Quanrum Spectroscopy and Radiative Transfer, 31, 97-109.

DINER, D. J., and MARTONCHIK, J. V., 1985a, The three dimensional radiative transfer using aFourier-transform matrix-operator method. Journal of Quantum Spectroscopy andRadiative Transfer, 34, 133-148.

DINER, D. J., and MARTONCHIK, J. V., 1985 b, Influence of aerosol scattering on atmosphericblurring of surface features. I.E.E.E. Transactions on Geoscience and Remote Sensing,23, 618-624.

FRASER, R. S., BAHETHI, O. P., and AL-ABBAS, A. H., 1977, The effects of the atmosphere onclassification of satellite observation to identify surface features. Remote Sensing ofEnvironment, 6, 229-249.

FRASER, R. S., and KAUFMAN, Y. J., 1985, The relative importance of aerosol scattering andabsorption in remote sensing. I.E.E.E. Transactions on Geoscience and Remote Sensing,23, 625-633.

GORDON, H., 1978, Removal of atmospheric effects from satellite imagery of the ocean. AppliedOptics, 17, 1631-1635.

HERMAN, B. M., and BROWNING, S. R., 1975, The effect QY aerosols on the Earth-atmospherealbedo. Journal of Atmospheric Science, 32; 1430-1445. .

HERMAN, B. M., BROWNING, S. R., and CURRAN, R. J., 1971, The effects of atmosphericaerosols on scattered sunlight. Journal ofAtmospheric Science, 28, 419-428.

HORVATH, R. B., POLCYN, J. G., and FABIAN, C., 1970, Effects of atmospheric path on airbornemultispectral sensors. Remote Sensing of the Environment, I, 203-211.

KAUFMAN, Y. J., 1979, Effect of the Earth's atmosphere on contrast for zenith observations.Journal ofGeophysical Research, 84, 3165-3172.

KAUFMAN, Y. J., 1984, Atmospheric effect on spatial resolution of surface imagery. AppliedOptics, 23, 3400-3408.

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14

Page 18: Normalization of satellite imagery

Normalization ofsatellite imagery 1347

KAUFMAN, Y. J., 1985, Atmospheric effect on the separability of field classes measured fromsatellites. Remote Sensing of the Environment, 18, 21-34.

KAUFMAN, Y. J., 1988, Atmospheric effect on spectral signature-measurements and corrections.I.E.E.E. Transactions on Geoscience and Remote Sensing, 26,441-450.

KIM, H. H., 1988, Atmospheric correction of satellite imagery. Proceedings of the FourthInternational Colloquium on Spectral Signatures of Objects in Remote Sensing held inAussois, France. on 18-22 January 1988, ESA SP-287 (Paris: European Space Agency),pp. 193-197.

KIM, H. H., HART, W. D., and VAN DER PIEPEN, H., 1982, Initial analysis of OSTA-I oceancolor experiment imagery. Science, 218, 1027-1032.

KIM, H. H., MCCLAIN, C. R., BLAINE, L. R., HART, W. D., ATKINSON, L. P., and YODER, J. A.,1980, Ocean chlorophyll studies from a U-2 aircraft platform. Journal of GeophysicalResearch, 85, 3982-3990.

MEKLER, Y., and KAUFMAN, Y. J., 1980, The effect of Earth's atmosphere on contrast reductionfor a non-uniform surface albedo and two halves field. Journal of GeophysicalResearch, 85, 4067-4083.

OTTERMAN, J., and FRASER, R. S., 1979, Adjacency effects on imaging by surface reflection andatmospheric scattering: cross radiance to zenith. Applied Optics, 18, 2852-2860.

STURM, 8., 1983, Atmospheric effects. In Remote Sensing in Meteorology. Oceanography andHydrology, edited by A. P. Cracknell (Chichester: Ellis Harwood), pp. 163-197.

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 2

2:53

18

Dec

embe

r 20

14