NASA Technical Memorandum 104566, Vol. 4

SeaWiFS Technical Report Series

Stanford B. Hooker and

Elaine R. Firestone, Editors

Volume 4, An Analysis of GAC Sampling Algorithms:

A Case Study

Charles R. McClain, Eueng-nan Yeh,

and Gary Fu

November 1992
NASA Technical Memorandum 104566, Vol. 4

SeaWiFS Technical Report Series

Stanford B. Hooker, Editor

NASA Goddard Space Flight Center

Greenbelt, Maryland

Elaine R. Firestone, Technical Editor

General Sciences Corporation

Laurel, Maryland

Volume 4, An Analysis of GAC Sampling Algorithms:

A Case Study

Charles R. McClain

NASA Goddard Space Flight Center

Greenbelt, Maryland

Eueng-nan Yeh and GaryFu

General Sciences Corporation

Laurel, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

1992
C.R. McClain, E. Yeh, and G. Fu

Abstract

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument will sample at approximatelya 1 km resolution at nadir which will be broadcast for reception byrealtime ground stations. However, the global data set

will be comprised of coarser four kilometer data which will be recorded and broadcast to the SeaWiFS Project

for processing. Several algorithms for degrading the one kilometer data to four kilometer data are examined

using imageryfrom the Coastal Zone Color Scanner (CZCS) in an effort to determine which algorithm would

best preserve the statistical characteristics of the derived products generated from the one kilometer data. Of

the algorithms tested, subsampling based on a fixed pixel within a 4×4 pixel arrayis judged to yield the most

consistent results when compared to the one kilometer data products.

1. INTRODUCTION

Earlyin the SeaWiFS design phase, questions arose

regarding what the best scheme would be for producing

reduced resolution global area coverage (GAC) data from

the high resolution local area coverage (LAC) data generated bythe scanner. The primarymotivation for considering schemes other than a fixed pixel subsampling (a

predefined element in a pixel array) was to maximize the

number of cloud-free pixels. Other considerations included

reduction of sensor noise (average-value techniques) and

error introduced byhigh aerosol concentrations or clouds

with low albedo (least-value techniques). A comprehensive

studyof GAC sampling techniques using Landsat and Advanced VeryHigh Resolution Radiometer (AVHRR) data

has been published byJustice et al. (1989) who found fixed

pixel subsampling to be the best representation of the original full resolution data.

In this study, an analysis was performed on a single

scene from the southeastern U.S. coast. The scene was

selected because it encompasses both Case 1 and Case 2

waters (Morel and Prieur 1977). Of the five methods used

in this study, the fixed pixel subsampling produced the

best statistical fidelityto the LAC product. The other

approaches can show significant deviations from the LAC

statistical properties.

2. DATA PROCESSING

Various methods for reducing the resolution of an image

were tested in order to compare their adequacyin retaining

the statistical characteristics of the high resolution derived

products. The SEAPAK (McClain et al. 1991) program

L2GAC provides these methods as an option for the user

while processing CZCS level-2 products. The GAC data

analyses used in this study operate on 4×4 (pixel×line)

blocks of data. L2GAC supports the AVHRR GAC generation scheme which operates on a 5×3 block of data, but it

was not considered for this study. The level-2 products include the water-leaving radiances at 443, 520, and 550 nm;

the aerosol radiance at 670 nm; the pigment concentration;

and the Rayleigh radiance at 443 nm. L2GAC also allows

the user to specifythe atmospheric correction algorithm,

the  ̊Angstr ̈om exponents, the ozone optical depths (default

is computed from the Total Ozone Mapping Spectrometer

(TOMS) Dobson Unit value at the center of the scene),

the sensor calibration, the land/cloud threshold (750 nm),

the haze threshold (670 nm), the water radiance scaling,

and the water radiance product (upwelled subsurface water radiance or normalized water-leaving radiance). Ringing correction was not applied to the data (Mueller 1988).

L2GAC generates standard SEAPAK 512×512 pixel

images byfilling in the 4×4 pixel area with constant values. It is important to note that anypixel that fails either

the land and cloud or haze threshold test is excluded from

the analysis. In the case of fixed pixel subsampling where

the pixel fails a threshold test, the entire 4×4 pixel area

is assigned a greylevel of either 0 (land) or 255 (cloud) to

indicate an invalid pixel which is then excluded from the

statistical analyses. The methods tested are described in

Table 1, with the method numbering convention being the

same as in the L2GAC program.

Table 1. GAC generation methods and the mechanisms used for each.

Method Generation Mechanism

1 Average level-2 product (level-2 products

are generated and then averaged).1

2 Fixed pixel subsampling (pixel [2,2] from

the 4×4 array).

3 Mean radiance product (level-2 products

are generated from mean radiances).

4 Lowest 670 nm radiance pixel.

5 NOAA/AVHRR subsampling which uses

a 5×3 array of data.2

6 Lowest 750 nm radiance pixel.

7 Pigment concentration derived from mean

LW values as computed in Method 1.

8 Pigment concentration derived from mean

Method 1 log(concentration) values.

1. Implemented only on the data processing system.

2. Not used in this study.

1
An Analysis of GAC Sampling Algorithms: A Case Study

In the cases of average-value and least-value algorithms,

the pixels that fail the flag criteria are ignored. The number of valid pixels within the 16 pixel arraymaybe as few

as one and the arraywill still be assigned a valid value.

Thus, implementation of average-radiance-value (Method

3) and least-radiance-value (Methods 4 and 6) algorithms

would require on-board processing for GAC data storage

on the spacecraft. Methods 1, 7, and 8 can onlybe implemented on the ground.

To provide some background on the standard analysis

methods (Gordon et al. 1983), the total radiance received

bythe CZCS is governed bythe equation

Lt(λ) = t(λ)LW (λ) + La(λ) + Lr(λ), (1)

where, λ is the wavelength, Lt is the total radiance, LW is

the water-leaving radiance, t is the diffuse transmittance

of the atmosphere, La is the aerosol radiance, and Lr is

the Rayleigh radiance. Lr depends upon the orientation

between the sun, Earth, and satellite, and, for this analysis,

LW (670) is assumed to be zero.

La(λ) is related to La(670) through an expression of

the form

La(λ) ∝

λ

670

n(λ)

La(670), (2)

where, n(λ) is the  ̊Angstr ̈om exponent. For the present

analyses, the  ̊Angstr ̈om exponents were assumed to be 0.

In this case, aerosol radiance equals La(670) multiplied by

a ratio of the solar constants times an exponential function

of the solar and spacecraft zenith angles and the ozone

optical thicknesses.

From the water-leaving radiances, pigment concentration is calculated using an equation of the form



chlorophyll a + phaeophytin= α

 LW (λ)

LW (550)β

, (3)

where, pigment concentration is considered to be the sum

of the concentrations of chlorophyll a and phaeophytin, α

is a positive number, and β is negative.

A CZCS scene of the United States East Coast, covering the area approximatelyfrom 70–90◦W and from 26–

34◦N, on October 28, 1979, was processed using L2GAC for

this test. The full-resolution level-2 products were generated byanother SEAPAK program, L2MULT. The inputs

used in the level-2 processing are provided in Table 2. All

wavelength dependent parameters in the table are given

in order of increasing wavelength, and three digit dayreferences are references to the sequential dayof the year

(February1 being the 32nd dayof the year). The method

of Evans was used for total radiance correction which is

unpublished but brieflydiscussed in McClain et al. (1992).

Table 2. Level-2 processing parameters.

Parameter Value

Level-1 Image Name: SNG:5106Bx.IMG

Level-2 Image Name: Y4NG:5106B-0-L2x.IMG

Processing Day/Time: 15-Oct-1991/14:21:33

Orbit Number: 5106

Tilt Angle: 10.000

Sensor Gain: 1

Wavelengths: 443,520,550,550,670 nm

Scene Year/Day/Time: 1979/301/16.766

Thresholds

Land/Cloud (750 nm): 21 counts

Haze (670 nm): 255 counts

Pigment Algorithm: Two Channel1

Water Radiances: Normalized

Water Rad. Range: 0.0,3.0

Rayleigh Calculations: Exact2

Mean Solar Flux: 186.96,187.02,186.81,

153.09

Optical Thicknesses

Ozone: 0.0011,0.0144,0.0279,

0.0125

Rayleigh: 0.237,0.123,0.098,0.044

 ̊Angstr ̈om Coefficients: 0.0,0.0,0.0

Epsilon Coefficients: 1.0,1.0,1.0

ILT Record Option: Yes

Solar Zenith Angle: 44.491◦ (at center)

Satellite Zenith Angle: 11.370◦ (at center)

Solar Azimuth Angle: 95.673◦ (at center)

Satellite Azim. Angle: 290.502◦ (at center)

Total Rad. Correction: Method of R. Evans3

Water Rad. Iteration: None4

1. Gordon et al. 1983.

2. Gordon et al. 1988.

3. Unpublished.

4. Smith and Wilson 1981.

3. RESULTS

Color Plates 1–8 (see envelope on back of cover for

all Plates) show the pigment images for the full-resolution

processing, Methods 1–4, and Methods 6–8, respectively.

Of these images, the most striking is Plate 2 for Method

1 which shows GAC blocks with high pigment values in the

Gulf Steam and Sargasso Sea regions. Apparently, these

high values are artifacts of the imperfect cloud detection

threshold. In all cases, the high values are isolated, occur in the vicinityof a cloud, and always produce pigment values of 39 mg m−3 (a greylevel of 254 based on

SEAPAK’s scaling convention). Most cases are associated

with the small scattered clouds over the Gulf Stream and

not the larger cloud bank along the eastern portion of the

image. Therefore, the problem does not appear to be related to sensor ringing, but is possiblyrelated to subpixel

size clouds. Generally, the effect results in low normalized water-leaving radiances in the 443, 520, and 550 nm

2
C.R. McClain, E. Yeh, and G. Fu

Table 3. Statistical summaryof various methods. Analyses of the full resolution are labeled “Full”. Because

the GAC methods fill the entire 16 pixel block with constant values, the “Valid Count” is not the true number

of independent pixel pairs.

Parameter Cross-correlation Cross-correlation Mean Standard Valid

Between Methods Coefficient Value Deviation Count

LWN (443) Full 1.527 0.759 131,413

2 1.518 0.757 131,344

2 and 1 0.777 1.584 0.780 164,160

2 and 3 0.772 1.566 0.798 164,160

2 and 4 0.755 1.752 0.848 164,160

2 and 6 0.760 1.686 0.831 164,160

LWN (520) Full 0.807 0.386 131,413

2 0.801 0.378 131,344

2 and 1 0.568 0.874 0.447 164,160

2 and 3 0.567 0.867 0.453 164,160

2 and 4 0.581 1.033 0.555 164,160

2 and 6 0.531 0.936 0.478 164,160

LWN (550) Full 0.505 0.235 131,413

2 0.503 0.231 131,344

2 and 1 0.757 0.511 0.223 164,160

2 and 3 0.755 0.507 0.225 164,160

2 and 4 0.677 0.558 0.217 164,160

2 and 6 0.687 0.547 0.234 164,160

La(670) Full 0.467 0.367 131,413

2 0.468 0.363 131,344

2 and 1 0.512 0.527 0.290 164,160

2 and 3 0.511 0.525 0.291 164,160

2 and 4 0.261 0.310 0.226 164,160

2 and 6 0.283 0.351 0.223 164,160

Pigment Full 1.320 5.343 131,413

Concentration 2 1.364 5.471 131,344

2 and 1 0.411 2.046 6.149 164,160

2 and 3 0.358 1.405 5.537 164,160

2 and 4 0.268 1.108 4.664 164,160

2 and 6 0.300 1.176 4.790 164,160

2 and 7 0.356 1.370 5.462 164,160

2 and 8 0.341 1.294 5.026 164,160

derived products. Because of the lower number of valid

values in a GAC block containing clouds, the weight of

the high value is amplified which biases the average pigment value of the scene to higher values and dramatically

modifies the frequencydistribution as discussed below.

Histograms of the level-2 images were generated using the SEAPAK program HIST. Pixels flagged as being

land, clouds or saturated in the 670 nm band were excluded

from the analyses. The histograms in Figs. 1–4 compare

the frequencydistributions of the normalized water-leaving

radiances [LWN (443), LWN (520), LWN (550)] and aerosol

radiance [La(670)] for the full-resolution and Method 2

products. The histograms of Figs. 5–11 compare the frequencydistributions of pigment concentration for the fullresolution data and the seven GAC methods. The comparison of the full-resolution and Method 2 histograms

show high fidelityas would be expected, but with some

scatter due to the fact that Method 2 has a greatlyreduced number of valid samples. Methods 1, 3, 4, 6, 7,

and 8 all show elevated peaks at low pigment concentrations. Also, note the histogram minimum at 1.5 mg m−3

in all pigment histograms except Methods 1 and 8. This

is due to the algorithm switching mechanism in the twochannel bio-optical algorithm (Denman and Abbott 1988;

Muller-Karger et al. 1990). Methods 3, 4, 6, and 7 clearly

tend to over estimate pigment concentrations for the range

above 1.5 mg m−3. Method 1 produces greatlyexaggerated values at high concentrations while Method 8 results in reasonablygood estimates in this concentration

range.

3
An Analysis of GAC Sampling Algorithms: A Case Study

Figs. 1 and 2. Histograms comparing the distributions of LWN (443) [top] and LWN (520) [bottom],

respectively, as derived from the full resolution and Method 2 analyses.

4
C.R. McClain, E. Yeh, and G. Fu

Figs. 3 and 4. Histograms comparing the distributions of LWN (550) [top] and La [bottom], respectively,

as derived from the full resolution and Method 2 analyses.

5
An Analysis of GAC Sampling Algorithms: A Case Study

Figs. 5 and 6. Histograms comparing the pigment concentrations of the full resolution analysis with

those obtained from Methods 1 [top] and 2 [bottom], respectively.

6
C.R. McClain, E. Yeh, and G. Fu

Figs. 7 and 8. Histograms comparing the pigment concentrations of the full resolution analysis with

those obtained from Methods 3 [top] and 4 [bottom], respectively.

7
An Analysis of GAC Sampling Algorithms: A Case Study

Figs. 9 and 10. Histograms comparing the pigment concentrations of the full resolution analysis with

those obtained from Methods 6 [top] and 7 [bottom], respectively.

8
C.R. McClain, E. Yeh, and G. Fu

Fig. 11. Histogram comparing the pigment concentrations of the full resolution analysis with those

obtained from Method 8.

Fig. 12. Scatterplot comparing the LWN (443) obtained from Methods 1 and 2.

9
An Analysis of GAC Sampling Algorithms: A Case Study

Fig. 13. Scatterplot comparing the LWN (520) obtained from Methods 1 and 2.

Fig. 14. Scatterplot comparing the LWN (550) obtained from Methods 1 and 2.

10
C.R. McClain, E. Yeh, and G. Fu

Fig. 15. Scatterplot comparing the La obtained from Methods 1 and 2.

Fig. 16. Scatterplot comparing the pigment values obtained from Methods 1 and 2.

11
An Analysis of GAC Sampling Algorithms: A Case Study

Fig. 17. Scatterplot comparing the LWN (443) obtained from Methods 3 and 2.

Fig. 18. Scatterplot comparing the LWN (520) obtained from Methods 3 and 2.

12
C.R. McClain, E. Yeh, and G. Fu

Fig. 19. Scatterplot comparing the LWN (550) obtained from Methods 3 and 2.

Figs. 20. Scatterplot comparing the La obtained from Methods 3 and 2.

13
An Analysis of GAC Sampling Algorithms: A Case Study

Fig. 21. Scatterplot comparing the pigment values obtained from Methods 3 and 2.

Fig. 22. Scatterplot comparing the LWN (443) obtained from Methods 4 and 2.

14
C.R. McClain, E. Yeh, and G. Fu

Fig. 23. Scatterplot comparing the LWN (520) obtained from Methods 4 and 2.

Fig. 24. Scatterplot comparing the LWN (550) obtained from Methods 4 and 2.

15
An Analysis of GAC Sampling Algorithms: A Case Study

Fig. 25. Scatterplot comparing the La obtained from Methods 4 and 2.

Fig. 26. Scatterplot comparing the pigment values obtained from Methods 4 and 2.

16
C.R. McClain, E. Yeh, and G. Fu

Fig. 27. Scatterplot comparing the LWN (443) obtained from Methods 6 and 2.

Fig. 28. Scatterplot comparing the LWN (520) obtained from Methods 6 and 2.

17
An Analysis of GAC Sampling Algorithms: A Case Study

Fig. 29. Scatterplot comparing the LWN (550) obtained from Methods 6 and 2.

Fig. 30. Scatterplot comparing the La obtained from Methods 6 and 2.

18
C.R. McClain, E. Yeh, and G. Fu

Fig. 31. Scatterplot comparing the pigment values obtained from Methods 6 and 2.

Fig. 32. Scatterplot of pigment values from Methods 7 and 2.

19
An Analysis of GAC Sampling Algorithms: A Case Study

Fig. 33. Scatterplot of pigment values from Methods 8 and 2.

Scatterplots of the data products from Methods 1, 3,

4, 6, 7, and 8 versus Method 2 are shown in Figs. 12–16,

17–21, 22–26, 27–31, 32, and 33, respectively. Only pigment comparisons are shown for Methods 7 and 8 because

both are derived from Method 1 products. Method 2 was

used as the baseline for evaluation because it most closely

represents the the full resolution image as shown in the

histograms. The SEAPAK program SCATT was used to

generate the scatterplots. SCATT excludes anypixel pair

that includes a value outside the range of valid values. All

methods tend to yield high values of LWN (443), especially

Methods 4 and 6, the least-value methods. For LWN (520),

the bias towards high values is especiallypronounced with

Method 4. As expected, Methods 4 and 6 stronglybias

La(670) towards low values. Finally, the pigment scatterplots do not indicate anypronounced biases, which are

better illustrated in the frequencydistribution plots and

quantified in Table 3.

The last analysis performed used the SEAPAK program CORCO to determine the image’s first and second

statistical moments and the correlation statistics between

Methods 1, 3, 4, and 6 versus Method 2. The statistics

are presented in Table 3. The statistics for full resolution

products (no subsampling; labeled “Full” in Table 3) and

Method 2 products were computed using the same image

for the two image inputs required byCORCO, so crosscorrelation coefficients for these cases are meaningless.

4. DISCUSSION

As indicated in the histograms, Table 3, and the scatterplots, the average-value and least-value techniques tend

to overestimate the normalized radiances with the leastvalue value methods performing the worst. Additionally,

the relative increases in normalized water radiances are

highest at 520 nm and lowest at 550 nm. However, as indicated in the pigment histograms, this does not necessarilyhold for all water masses in the scene. The tendency

in the pigment range above 1.5 mg m−3 is to overestimate the concentrations and requires the relative increase

in LW (550) be greater than in LW (520). As expected,

the least-value methods bias the aerosol radiances towards

lower values. On the other hand, the average-value methods bias the aerosol radiances toward high values. Also,

the least-value methods underestimate the mean pigment

concentrations.

In summary, the fixed pixel subsampling gives the best

representation of the full resolution data for GAC product

generation. The explanation for whythe water-radiances

are biased in one direction, or the other, depending on

water mass, is more involved and is beyond the scope of

this analysis.

20
C.R. McClain, E. Yeh, and G. Fu

Glossary

AVHRR Advanced Very High Resolution Radiometer

CZCS Coastal Zone Color Scanner

GAC Global Area Coverage

LAC Local Area Coverage

NOAA National Oceanic and Atmospheric Administration

SEAPAK Software package developed at NASA/Goddard

Space Flight Center which ingests,displays,and

processes data from the CZCS

SeaWiFS Sea-viewing Wide Field-of-view Sensor

TOMS Total Ozone Mapping Spectrometer

References

Denman,K.L. and M.R. Abbott,1988: Time evolution of surface chlorophyll patterns from cross spectrum analysis

of satellite color images, J. Geophys. Res., 93, 6,789–6,798.

Gordon,H.R.,D.K. Clark,J.W. Brown,O.B. Brown,R.H.

Evans,and W.W. Broenkow,1983: Phytoplankton pigment concentrations in the Middle Atlantic Bight: Comparison of ship determinations and CZCS estimates, Appl.

Opt., 22, 20–36.

,J.W. Brown,and R.H. Evans,1988: Exact Rayleigh scattering calculations for use with the Nimbus-7 Coastal Zone

Color Scanner, Appl. Opt., 27, 862–871.

Justice,J.O.,B.L. Markham,J.R.G. Townshend,and R.L.

Kennard,1989: Spatial degradation of satellite data, Int.

J. Remote Sensing, 10, 1,539–1,561.

McClain,C.R.,M. Darzi,J. Firestone,E. Yeh,G. Fu,and D.

Endres,1991a: SEAPAK Users Guide,Version 2.0,Vol. I—

System Description,NASA/Goddard Space Flight Center,

NASA Tech. Memo. 100728, 158 pp.

,M. Darzi,J. Firestone,E. Yeh,G. Fu,and D. Endres,

1991b: SEAPAK Users Guide,Version 2.0,Vol. II—Descriptions of Programs NASA/Goddard Space Flight Center, NASA Tech. Memo. 100728, 586 pp.

,W.E. Esaias,W. Barnes,B. Guenther,D. Endres,S.B.

Hooker,G. Mitchell,and R. Barnes,1992: Calibration and

Validation Plan for SeaWiFS, NASA Tech. Memo. 104566,

Vol. 3, S.B. Hooker and E.R. Firestone,Eds.,41 pp.

Morel,A.,and L. Prieur,1977: Analysis of variations in ocean

color, Limnol. Oceanogr., 22, 709–722.

Mueller,J.L.,1988: Nimbus–7 CZCS: Electronic overshoot due

to cloud reflectance, Appl. Opt., 27, 438–440.

Muller-Karger,F.E.,C.R. McClain,R.N. Sambrotto,and G.C.

Ray,1990: A comparison of ship and CZCS-mapped distributions of phytoplankton in the Southeastern Bering Sea,

J. Geophys. Res., 95, 483–499.

Smith,R.C.,and W.H. Wilson,1981: Ship and satellite biooptical research in the California Bight, Oceanography from

Space, J.F.R. Gower,Ed.,Plenum Press,281-294.

21
An Analysis of GAC Sampling Algorithms: A Case Study

Color Plate Captions

Plate 1. Pigment concentration at full resolution using processing parameters provided in Table 2.

Plate 2. Pigment concentration at GAC resolution using Method 1 and processing parameters provided

in Table 2.

Plate 3. Pigment concentration at GAC resoution using Method 2 and processing parameters provided

in Table 2.

Plate 4. Pigment concentration at GAC resolution using Method 3 and processing parameters provided

in Table 2.

Plate 5. Pigment concentration at GAC resolution using Method 4 and processing parameters provided

in Table 2.

Plate 6. Pigment concentration at GAC resolution using Method 6 and processing parameters provided

in Table 2.

Plate 7. Pigment concentration at GAC resolution using Method 7 and processing parameters provided

in Table 2.

Plate 8. Pigment concentration at GAC resolution using Method 8 and processing parameters provided

in Table 2.
An Analysis of GAC Sampling Algorithms: A Case Study
An Analysis of GAC Sampling Algorithms: A Case Study
An Analysis of GAC Sampling Algorithms: A Case Study
An Analysis of GAC Sampling Algorithms: A Case Study
 Form Approved

 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering

 and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of

 information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite

 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

 November 1992 Technical Memorandum

 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

 SeaWiFS Technical Report Series

 Volume 4–An Analysis of GAC Sampling Algorithms: A Case Study Code 970.2

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING/MONITORING

 AGENCY REPORT NUMBER

11. SUPPLEMENTARY NOTES

12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

14. SUBJECT TERMS 15. NUMBER OF PAGES

 16. PRICE CODE

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. 239-18, 298-102

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION

 REPORT NUMBER

 REPORT DOCUMENTATION PAGE OMB No. 0704-0188

 OF REPORT OF THIS PAGE OF ABSTRACT

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT

 Unclassified Unclassified Unclassified Unlimited

 Oceanography, SeaWiFS, Algorithms, GAC, SEAPAK, Pigment Concentration 20

 E. Yeh, G. Fu, and E.R. Firestone: General Sciences Corporation, Laurel, Maryland.

Report is available from the Center for AeroSpace Information (CASI),

7121 Standard Drive, Hanover, MD 21076–1320; (301)621-0390

Unclassified–Unlimited

Subject Category 48

13. ABSTRACT (Maximum 200 words)

National Aeronautics and Space Administration

Washington, D.C. 20546–0001 TM–104566, Vol. 4

Laboratory for Hydrospheric Processes

Goddard Space Flight Center 93B00016

Greenbelt, Maryland 20771

6. AUTHOR(S)

 Charles R. McClain, Eueng-nan Yeh, and Gary Fu

 Series Editors: Stanford B. Hooker and Elaine R. Firestone

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument will sample at approximately a 1 km resolution at

nadir which will be broadcast for reception by realtime ground stations. However, the global data set will be comprised of

coarser 4 km data which will be recorded and broadcast to the SeaWiFS Project for processing. Several algorithms for

degrading the 1 km data to 4 km data are examined using imagery from the Coastal Zone Color Scanner (CZCS) in an

effort to determine which algorithm would best preserve the statistical characteristics of the derived products generated

from the 1 km data. Of the algorithms tested, subsampling based on a fixed pixel within a 4x4 pixel array is judged to yield

the most consistent results when compared to the 1 km data products.